Today's panel is called governing the global digital economy. And I'll give a little words of introduction as the panel is getting set up. The digital economy is it's just a new name and new description of the impact of technology on economic growth. Digitalization spans many economic sectors, making it very difficult to limit the focus of it. So the meaning of digital as a result of meaning of digital economy continues to evolve alongside these technology advancements. The current digital what we're calling digital economy today, focus on the way digital technologies and services. And products, techniques and skills are integrated across the economy. In what we're calling digitalization of the economy, our definition would typically just this definition will typically just encompass core digital sectors, which refer to the provisioning of digital technologies, products, services, infrastructure solutions, as well as all forms of economic activities that are completely dependent on digital tech technology. And as we discussed yesterday about data. So we're looking at other areas of ICT, but we're also looking at digital commerce, Internet finance, and some of these areas that are not traditionally seen as part of the ICT or what used to be called the telecom sector. So as digital transaction crosses borders, we're creating universally accepted back regulations with in those what's needed. As countries digitize their economies, there are a variety of trade, governance the vibe of have trade, governance and other issues that arise. And we we really need to have all countries have proper policies and regulations that focus closely on the digital economy. And we're not just talking about developed countries, we're talking about all the developing countries as they are jumping headfirst into a process versus called digital transformation. So we it's very imperative that each of these countries work on creating different policies that can impact the impact of digital economy, because in these economies, it will enable smaller, even some small island states to become digital powerhouses by focusing on creating digital policies. So today, we're going to hear from three engaging panelists that will help us shed light on the challenges associated with the range of issues in the digital economy, whether they be international trade issues, Internet governance, geopolitical tensions, issues of sovereignty. So he'll pounds discuss this delicate balance, how we're going to promote innovation and competition, while also addressing the ethical concerns tied to data privacy, artificial intelligence, human rights in other areas. So our three panelists are Bill Drake, who is, as you heard many times, the Director of International Studies at Columbia's Institute for talent information at the Columbia Business School, we have Deborah Cogburn, who heads up the Internet Governance Lab, as well as the AU Institute on Disability and public policy. And of course, our good friend, Wolfgang Klein watcher, who are with with the University of odd of our house. I'm not really clear on pronouncing it in Denmark. But I will let Bill start out with sort of talking about these wider issues of trade. And then, and then we'll go on to Derek and Wolfgang.
All right, thank you, Judith. And hello, everybody. So again, so there's quite a number of different issues on the agenda here, potentially, depending on how you want to parse things I thought I would speak to maybe four things real briefly. And then hand over that way. I think the idea is to just put on the table, a bunch of ideas, the three of us and then try and engage You everybody in conversation around that menu. So the first one is I think the notion of the digital economy that Judith was noting is been somewhat contested, sort of construct. There is a tendency among a lot of people, I think to when they talk about the digital economy to think that it's all about gut fam, you know, Google App, Apple, Facebook, Amazon, Microsoft, and what's going on in supply side of the industry in terms of big tech, right, that's in the popular imagination. Often, that's the way people think about this. But But that's really kind of just a small part of a larger picture, Judith use the term digital transformation, digital transformation is what's going on. And it's a transformation that cuts across all economic sectors, all sectors of the economy, agriculture, manufacturing services, the the supply side of technology, ICT and services, but also the demand side, the way technology impacts across the banking sector, the shipping sector, the transport sector, all sectors of the economy are increasingly becoming digitized, and digitalized. And as that's occurring, the importance of information, data and knowledge inputs, is increasing radically, we are moving into an economic environment in which more and more economic activity is connected, and involves processing of information and the utilization of information to embody things often that used to be in material form. So you know, today, for example, we watch a movie or something, in the old days, you would have gotten maybe a video cassette, right that you put in, there was a digital, fixed physical object you put into a cassette machine, and you watch the movie now comes streamed over the Internet. Same with music, same with everything that can be rendered in digital form, increasingly, is, so many physical products are becoming immaterial. And they're becoming networked. And they're being transported globally, across the boundaries, often subjected to various forms of digital protection. But there's a lot of the parts of the digital economy where globalization is, in fact, still operating quite effectively. And that's important. So it goes on the weight and significance of information, digital, information, data, and knowledge in each process and product is increasing. Everything about around us is basically embedded information. It represents, whether it's the the chairs that we have, they were designed according to the use of knowledge measurements, so on the data and information allows you to figure out how to combine different elements, labor, land capital, to produce a physical product. So the role of information data, knowledge is very pervasive throughout the economy. Everything is embedded to digital information, increasingly. So I think when we talk about the digital economy, let's keep in mind what that we're not just talking about the farm, we're not just talking about what's going on in big tech, we're talking about a macro level transformation of the economy that's been under underway for a while. So that's the first point I would make. I also wanted to talk a bit about data flows, maybe AI and digital trade. But I think Judith baby would make sense to take issues when one at a time I just put on the table the question of the digital economy? Sure. Why don't we see if there are by two colleagues who have something to say about this issue? And then maybe we can go to the sequence?
We want to make this as interactive as possible? Sure.
Well, I'll say a little bit about about that I have four things that I want to cover, but I'll cover the first one. And so there's an overall overarching theme for what I'd like to highlight, which are knowledge and skills for digital economy and how that gets manifested in a variety of ways. So one of the things that I think is important to highlight in this period that has has been in existence for a while, but is being accelerated is the opportunity to do globally distributed collaboration in economic development opportunities. So creating companies and organizations where you can have knowledge and expertise anywhere in the world, working together to respond to business opportunities, requests for proposals, bringing in a range of digital skills that can contribute to really strong responses to opportunities is I think something that is, again, has been in place but it's something that can be accelerated, you know, in our audience here, I've been able to talk to a number of our fellows As in participants, and a number of them are already engaging in those kinds of activities. So being able to create companies where they can work with other entrepreneurs in different parts of the world, and be able to bring to bear their knowledge and expertise and their teams to be able to take care of these kinds of opportunities. So that's the, that's the first issue that I wanted to highlight.
And I think you're I remember it was more than 30 years ago that Bill Clinton in his election campaign argued, it's the economy stupid. So you argue today, it's the digital economy, stupid. So everything is now digital in Clinton time, then we discuss the new economy. So it has old economy, these are all, you know, steel, and oil, but the new economy is on data. And then we realize the beginning of the 21st century, the data is the Oil of the 21st century, and there is no new economy. It's just the economy and the economy is a digital economy now. So I think this is really what we have learned in the last quarter of a century. And also, in the early years of the new millennium, we argued, okay, if data is the raw material, like oil, then data is translated into information, information into knowledge, and hopefully, knowledge into wisdom. So, where we are today, we are stiff with the raw material, because data is the starting point for everything. And also, you know, probably we can discuss also a little bit, the international dimension. And Bill mentioned already, they should tweet, you know, arrangements among countries. So, if data is the oil, so we have oil rich countries, which are rich countries, Saudi Arabia is a good example. So that means, do we have data rich countries and data data poor countries, so the individual is the, you know, produces the majority of data. So that means a country with 1 billion people, instead of rich, the country with just 1 million people is data. And what does it mean, for the future of the digital economy? Let's just, you know, some startups. And let's continue the
debate, if I could jump in there that so one of the things about this data as the new oil that I think is really interesting is the fact that we have a period of what I'm trying to sort of promote as democratizing data science. So data is all around us, particularly as more and more people are using the Internet, social media, blogs, websites, and so forth. All of that becomes data. So we have all this techspace data, we have open data movements, so governments are making more data available, international organizations are making more data available, state governments, local governments, so much can be acquired through the data that's out there. So it's a really rich data environment. And what what I talk about in terms of making this democratizing data science is that the tools to be able to capture, scrape, analyze that data are all free. So they're free in terms of open source software, like R and Python, they're free in terms of, you know, being able to go out and grab that data, and do the analysis. So I think it's a really interesting period, going back to, you know, the theme that I want to highlight around the skills so the to democratize data science as possible. So how many of you can program in Python or R? Okay, that's decent. Exactly. I think every hand should go up, because of the potential for data that's out there and to be able to take advantage of these tools. Now, I'm sure in a moment, we'll turn to talk about AI, which is also what I want to talk about. But this idea of data being the new oil for the digital economy is so important to be able to take advantage of these open, free resources. Okay, I
need to jump in on this. So two things. First, I want to back up to a point that Wolfgang made when he talked about the Clinton administration, and the notion of the new economy. One of the things that I think we have to understand is the way in which the understanding of the role of information and data in the information revolution and its impact on the economy has transformed over time, and what the consequences of those different framings are. So for example, in the 1960s it people talked about the computerized society, the 1970s. It became the post industrial economy in the 1980s. We went Going through a series of cycles, information economy, new economy, Internet economy, now digital economy. And each of these different framings can point action and policy in different directions. If you remember back in the 70s, and 80s, when post industrial became a big thing, everybody thought, well, then we don't have to focus that much on how to take care of manufacturing, and agriculture, because the future was all services. And so American economic policy became totally geared towards how do we cultivate our competitive advantage globally, in that, and we didn't worry about the manufacturing sector very much. Of course, what ended up happening, a lot of the manufacturing just moved to other places. And that had consequences long term, including the rise of Trumpism, and all that. So. So how you conceptualize the the economy, and the role of the information revolution and changing it points towards different types of action orientations. And now, we have many people thinking about the digital economy in terms of the farm and the power of the farm, and we must do something about calm. And so you get these discussions around trade policy, data policy, data flow policy, all this other stuff, where everybody's obsessed about, oh, my God, what are we going to do stop the evil horrors of Google apple and so on, without thinking that the kinds of policies and frameworks that you adopt have to actually form foment digitalization throughout the economy and support users and all other sectors, etc. So the orientation gets skewed. And this, we've seen this in recent debates about the way digital trade should be done. The other point I just wanted to make real quick data is not the new oil, this is a ridiculous concept. And indeed, The Economist article that said that pointed out immediately, that while this is a framing that many people are leaning towards, in fact, data does not have the same properties of oil as all okay, is not fixed, it is not some hard resource, you know, some countries have more, and some countries have less, and you have to grab it, etc. And that whole orientation has fed into a kind of techno nationalism, and an obsession with, you know, constraining data flows, forced data, localization, putting up barriers to data, etc, etc, keeping data within national countries, I say this whole time in the WTO, you know, even right now, they will get into the WTO. But as we speak, the WTO is having a ministerial meeting. And at that meeting, one of the big fights is that Indonesia, India, and South Africa, are insisting that they want the right now to impose customs duties and data flows. And they and they insist on this, they they've been, they've come to believe that somehow that they're, they're losing out on the movement of data, that they could be generating all kinds of revenue, by imposing customs somehow on the bitstream. And so they want the ability to do this. And they're insisting that the WTO abandoned its long standing moratorium on imposing customs duties. Okay, why? Because they think of the data economy as being all about well, the Ott, the over the top streamers are sending data into my country, and I'm not getting enough cash from that. So they think they want to impose customs duties, and the import, quote, unquote, of bits. But of course, what that's going to do is simply raise the prices to people within their countries, and introduce all kinds of economic inefficiencies. So long story short, let's be clear, data is not oil. Data is more like light is a ubiquitous resource that can be drawn upon, that can be customized. Sometimes it can be privately held and use for strategic advantage. Sometimes it's it's Commons and public good. You can't put it into a simple box, like new oil. So that's just the first round and the data Academy. Judith, you want to you want to do? Yes. Thanks
so much.
We'll talk about AI.
Well, we'll talk about AI. But I think it's also what you bring out the points on data on value important because all countries need to, what we've shown here is they need to open the data. One of the ways of collecting data from populations is to try to make new new arrangements. We've seen that in the US and other places where people in snowy climates are looking at the data. Where's my snow plow? And they're using the the open data that is out there the companies that are making through the use of GPS and other open data, telling people Oh, your snow plow is coming is actually on this street. And they can do that because each of the plows easy the other tough has is a GPS code and they have the information on where these snow piles are deployed, where the other things are. So we're using so the cool, the idea is now for all countries, to figure out ways of how we can open the data, how we could get deliveries like Ubers, and other stuff, how we can get all that. And that is what is going to revolutionize even in small countries. I mean, I remember seeing about 10 years ago, when I was in Liberia, where some places have might have figured out the logistical challenges and have done the food delivery, which people can do. But a basically, it's a way of figuring out how am I going to use that up? The data is out there to solve any logistical or program that how am I going to help? How am I going to provide new services. So this issue also is on cross border trade, cross border trade is such a big issue, because the idea is we need to start trading between countries. And we need to have smart contracts. We talked a little bit about the smart contracts. But that international trade issue has so many angles that really improve on data. And so before I think we go to AI, I want to touch more a little bit about the data issues. And the governance of these issues of the courts are so related to international trade and E commerce.
Can I just point out that you're talking to a bunch of people in Puerto Rico about snowplows?
No, but I got
my example, though. So when you started to talk, Judith, you know, I was I was thinking about the the oil. I mean, I don't think anybody would have gotten mentioned it. But I don't think anybody thinks data is oil. But when when she started to talk about the snowplow, I was thinking about you think about snow, and how much, there's still a bad example, bad location to talk about snow. But you think about snow that's all around you. And those of us who lived in Syracuse, we know how much snow can be around us. And you need some kind of vehicles, Judith, to be able to get the snow out of your way or to make life usable in the midst of all this snow. So the point that I was making, and I think probably Wolfgang was making is that there's so much data that's around us. And so it's it's like snow that in a in a snow storm, that it's all around us. And again, you know, these, I'm gonna just kind of slow down just a little bit. When we talk about these things, I want to make sure everybody understands what we're talking about. So in the open data movement, many organizations, governments, countries, international organizations, are making massive amounts of data available for researchers like snow all around us. If you have the skills and the tools to be able to analyze that data, you can move from, you know, data as information to knowledge, you can analyze this data to help make decisions to make accurate decisions. So it's not a we're not in a situation of not having the data, the data is all around us. It's becoming increasingly available. The question is, can you access it? And can you develop actionable intelligence from that data. And that's why we're not in a period where data analysts are subject to only being able to analyze the data if they can afford very expensive licensing arrangements for software and, you know, huge expenses to do data visualization and so forth. All of that's free. So free software, open source software, R and Python. Going back to our president opening on the first day, being a bilingual data science is allows you to take that snowfall, Judith, you have a snowplow to be able to analyze this data. The question is, can you do it? And so we can talk about skills development and workforce development a little bit later. But for me, that's the point of the massive amounts of data that are around us and can't do you have the skills to be able to develop actionable intelligence from that data, both numerical data, textual data, and all of these data source video data, all these data sources that are around us. It's free, and the tools to analyze them are free.
By the way, snow is an enabling resource. It enables skiing, no snow, no skiing,
and whole industries that
again, let me come back to what Bill argued that data is not oil. It's like light or light air. So I think it's more complex. It's more complex. The question of ownership on data is, after 20 years of discussion, it's unclear. So A differentiation, you know, the type of data that we have personal data, I'm from Europe that we have the Data Protection Directive, so which protects personal data, who is the owner of personal data? So the European Commission tries to differentiate between personal data and industrial data. So two categories industrial data, while it's free, so personal data should be protected. So the question is, if, you know, personal data are owned by myself, so I was in a discussion with Eric Schmidt, when he was still the CEO from from Google. And so I moderated a session with Eric Schmidt as the keynote speaker, and then a student asked request, Smith. So if you use my data and make money, then you should pay me, though. And so that means I give you my data, and you know, then we should share the profit, you do make this my data. So Eric Schmidt was not confused, because he expected such question from a very critical students. But you said, Okay, it's a fair deal, you get to service and pay with state or not with money. So and So, this, if you have to pay with money, you are very careful. So if you go into a shop and you want to buy shoes, then you say, oh, probably $100 is too much, I buy issue only for $40. If you go to service, and you have to pay with data, you do not ask these questions, or you try to ask the question, is it worse to give away my personal data just to get the service or if they want to have too many data's that now that it's like to sue for $100? Or something like that. So that means the whole question of ownership? What is the incident data resource? Is it just like air or light or something like that needs more conceptual clarification, I always argue that we are still in the early days of the information economy, the information society. So that means if you remember the early days of the industrial society, it took nearly 100 years to clarify, you know, what are the resources, you know, how manufacturing is going on manufacturing today, and information HS, you know, just AI will pave the way. So AI is also in the very early days. So what will be in 20, and 50 years from now. So I think we need more conceptual clarification for the role of of data. And then comes all the consequences, what Bill mentioned, the WTO era, how to deal with trade, how to trade with data. So and all these secondary questions, but I think the first place is, we have to deepen our understanding the nature of data. And this relates to question like ownership and assets.
So okay, so then, I think we all want to say something about AI. And we all have talked about trade a little bit. So and we want to talk about data. So do that and sequence right. So
I can I can build on what you said to talk about AI? No, I want
to talk about data first. Can we talk about data and talk about data flows? Because I think that's fundamental. And, yeah,
let's talk about data flows first. So, alright, so for
further to the point about, you know, how increasingly governments and certain actors around the world are viewing data as an infringement as a resource that they want to adapt, adopt sort of digital protectionist policies to, to cultivate and keep within their borders, and so on. We've seen the explosion of a variety of new kinds of policy orientations Towards Data Flow, and data localization, data flow policies, it used to be that data flowed fairly openly across borders, with some limitations, particularly in the pre Internet era where much of the data flow was taking place in corporate systems. So companies were able to send data between companies intercorporate, transfers of data, or else commercial transfers. And that was relatively unimpeded, we had debates about transporter data flows in the 1970s and 80s. That ultimately led to a fairly open approach, where because governments came to realize that trying to erect all kinds of barriers to data flows, the cost was too high. The difficulty of maintaining the barriers was too, too technologically taxing and so on. But what we've seen with the Internet is a move towards a much greater level of governmental intervention and so on. Now, we basically have three kinds of regimes out there, a free flow kind of orientation, which is what most of the industrialized countries had historically followed. What ends of the spectrum at the other end of the spectrum, a data limiting kind of approach, the most extreme forms being countries like China, and so on, where you've got actual strong barriers erected by the government saying, what kind of data can come in and come out, there's been some reform of Chinese policy lately, or at least said to be reformed. It's not evident, entirely clear how strong that reform is. But those are the two kinds of polar positions in between is where most countries increasingly are now, which is conditional data flows, they will allow data flows subject to certain requirements. And so the Europeans particularly pioneered this with the GDPR. In saying, Okay, we will allow flows of personally identifiable information about European citizens to other countries, if they meet an adequacy test, that shows that the country to which we're going to send the data have comparable levels of protection to the European Union, guess what almost no country qualifies. After after years of doing this, they've only got like seven countries that the Europeans have decided are good enough. So then in the meanwhile, what you have to do is adopt all kinds of policies to fudge the difference. So we've adopted various kinds of private contract solutions, and so on, to allow data flows to continue. But increasingly, the EU is imposing tighter and tighter limits, and basically pushing big companies to store data within the European Union, keep it resident there, keep a copy of it there, which gets you into the whole realm of data localization, and forcing companies of all sizes, entities of all sizes, to have their data resident within the region, and or country and potentially even processed by national carriers. So to subject to national control. So the idea here is that, contrary to the whole kind of history of the Internet, where the idea was, bits flowed freely, your your data was all over the place, you didn't know where it was. Now, you've got sovereign states seeking to impose territoriality, territorial controls boundaries, over bitstreams enforce all the data activities to occur within those boundaries. And that approach that the Europeans adopted for privacy protection is getting picked up by many other countries for other kinds of data, commercial data, and so on. So we're seeing more and more efforts now, by states to say, we'll allow you to transfer data if, and in many countries, we should just, we shouldn't name particular example, countries, but we've seen cases where this involves corruption, where the state tells big companies that you know, will allow you to transfer data, but you have to make some side payments to us, etc. So we're getting into kind of a difficult area here, where governments are asserting their control over the bitstream. And that's raising all kinds of issues. So I thought maybe we could talk a little bit about that. And then we can go from there to AI and and trade. So you want to pick up on that.
Yeah, you mentioned the different regimes you know how to manage this. I think I know Brett Ford, in his new book about digital Empire is from Columbia University. argued, okay, we have the free flow concept, the United States, the biggest empire, then the other big empire, China with a state controlled, and then the European Commission with rules based orders, right. So both US and EU are democracies, it's an autocracy, the Russia, Africa is excluded for this way, but we live in a multipolar way. And probably we have a broad spectrum, which goes from one extreme, let's say, China, I would not say to us as that as an extreme, but you know, that totally freedom and totally state control and in a broad mixture in between. And I think that's the big challenge because we live in a borderless world. So but we, at the same time, we live also in a bordered world, because to 193 national jurisdiction did not disappear. After the information revolution, there was a team in the 90s that you know, in the Information Society, you know, everybody will be united, so that it meets the challenge is really to find a balance between on the one hand that really data or information, Internet resources or like air, light borderless, but to bring this in balance with the reality of 193 national jurisdictions, I think India and How's Africa in the WTO? negotiations on a customs free cyberspace? Half an argument and say, Okay, so, at the end of the day, we have countries or companies, which benefit from that, and others, you know, which have to pay a price. So now we can rebalance it to share. So, it comes back to the concept of sharing that we can share the profits and they are looking for traditional means control to hit hits or erecting barriers. So, this is backward thinking. So, what we need more is forward thinking and to say, Okay, what is fair sharing in the information economy, so probably this is also means leads to self restraint, or let's say, I would not say charity from Mehta and Google or others, but they understand that, you know, the benefits they have from the free flow of data is uneven distributed. And, you know, we have to find a way, I do not say that the Indian proposal and the South African proposal into WTO is a good way. So, but they have a point, and we have to recognize its point.
We have somebody raising your hand, or if we were going to take questions at the end, but if you want to jump in now, sure. Go ahead. You seem like you've got Wait, hold on, let's get you a mic.
Okay, why don't you introduce yourself that
okay. So my name is Heidi Vignelli, I'm with at large, I'm afraid a chair. And I think the main problem here is that whose rule or laws apply and on whom. And given that we are talking about a global Internet, in order to talk about an Indian proposal, South African proposal, the US or the EU, I think that won't work. We need to have some common ground, we need to get together to have to decide on some rules and laws that we all agree to abide to and follow on some common ground. But if we keep on thinking, you know, India has a proposal, the US has a proposal, South Africa has a proposal, most probably we will be ending with a fragmented Internet with many different laws regulations, applied in some places and others not. And you mentioned the GDPR and the EU, only allowing the transfer of data according if some countries actually are at the same standard. And they find they can only do that with seven countries. Well, seven countries out of what out of like you have Europe and then seven countries and the rest. What about the rest? Right? Again, you know, that's not a solution. And maybe the solution that we should we should all be up to that standard. Maybe Maybe, but but the thing is, you know, where's the common ground that we can all agree on as nations? Thank you.
Okay. Well, of course every country has it has its position about what the common ground should be. And so that's why we have negotiations. But but there's interesting developments in the WTO, around negotiating style, which speak to that point, which we'll come back to later. But okay, so having talked about Dataflow, do you want to go into AI? Because that's the next iteration of this kind of, you know, the, we've touched on the general point that, you know, for economic development and historical reasons, there was a tendency to want to favor openness. But in closing, increasingly, we're seeing varying levels of closure being enacted around data. And now AI emerges as a next iteration of how the government's conceptualize this space, what kinds of policies they want to adopt, you want to take a lead on that? Yeah,
well, I'll just say a couple of things. So first off, I think, you know, AI is not new. AI has been around since the 50s. And moving towards various ways of computers starting to learn and to think and to move in that direction. What's fundamentally different about what we're calling AI and what we're talking about now, that's relatively new, that started in about 2017 is around this generative AI approach and these transformers, which is fun, too. mentally changing how we get engaged with these large language models, which are trained on enormous amounts of data and increasingly large amounts of data as you move from models, two to 2.5, and three, 3.5, and four. So these models are being trained on larger and larger amounts of data, some of which comes from private sources, you know, New York Times has a recent lawsuit against open AI, for training its model on their published articles. Now, we all three, FYI. Oh, no, we don't. And I'm asking to take their data out of the models, which is not not really is possible in some ways. But these, these companies don't like to describe and confirm what data their models have been trained on for that very reason. Because it can be pulled out. So the key point here for the digital economy, from my perspective, is the impact that this is having on a whole variety of jobs and industries that we have to think about when we when we have these large language models that we can engage with. And that can generate very, very detailed output and responses in terms of generating texts, generating images generating video, it's changing the nature of so many careers. And I think, you know, one key that people have talked about is to think about, you know, is AI going to take my job? Or is it going to eliminate these industries, that kind of structural unemployment that may come from artificial intelligence. And I think it certainly will have disruption in whole industries, if we look at what the writers strike just happened in, in California, and Hollywood, you know, many of the writers were saying, as the studio's want to move to generative AI to create scripts, and stories, and jokes, and so forth, and so on, and pushing them out of their employment opportunity. And yet, the models are trained on the scripts, and the jokes and so forth, that they built. So they legitimately said, Well, what's our role in all of this, and so they use the contract negotiation process to strengthen their role in AI. And this is happening, you know, for actors as well, extras that are being digitized. You know, so and video that's being generated, not from sample video images, but just from text. So you know, the new Sora generative AI that's going to come out, you know, we'll be able to generate whole fluid video footage from just text based descriptions. So the kind of challenges that the workforce is facing is really these some of these industries will be disrupted, these jobs will go away. And so one of the questions is, and kind of a reframing of this is not will AI take my job? But can you learn enough about how to use AI to be able to then position yourself in a new way, within this kind of a digital economy?
Yeah, I think this is really not new. So if we go back to the early days of the industrial society, you know, my grandfather can't grandfather in Germany had a business, a taxi business was horse carriages. So he was rusted successful in this field. But his business collapsed. Bankrupt, we stay merchants have a car. So that means it wasn't probably not so difficult to change, you know, from horse carriages to a car, and to rebuild business and to taxi service, though it was done in the 1920s. So it was not so successful in this new business. But anyhow, you know, and this is what we see now, but it's much more complicated. Because if you have been, let's say, a script writer in Hollywood, and then you have to be now a programmer, working with algorithm that's, that's, that's different. It's more complex. It's more complicated, but the general let's say changes and challenges are not so different, but we have seen in history and to look back and, and I think it's also a challenge to do correctional system what we discussed yesterday. So that means it's part of the government not to control and to regulate, but to enable the next generation to get the right knowledge so that they are, you know, find their way of life in the, in the future. And so the problem I want to say is, okay, it's also relationship between quantity and quality. You mentioned, AI is around us, you know, for more than 50 years, but if it reached a certain quantity, suddenly, we did see a new quality is this large language models and all this. So, and that's the problem now, and this goes back to the question of, you know, who produces the data, who owns the data, you know, more data, better services. So I follow since a couple of years, the development of AI based autonomous weapons systems. So, you know, say could train this weapon system so far, you know, only with very limited data, because, you know, now they have a war. And so immediately, you see a push, because now they have data from real war scenarios. And a huge amount of data is produced in this stupid Ukrainian war on AWS in the Gaza war, what I see now, the Israelis are using, you know, face recognition data, which they have collected from public cameras, on traffic lights, in the streets of Gaza, to identify Hamas fighters. So let me say, not for drone identify to face and looking then where the guy is, and then killing him. So and this are, you know, take question of quantity and quality that you get suddenly new services, if you have more data. And the question is then of the US to do we have a debate about the peaceful use for the benefit of mankind? What is the benefit of mankind? So if we just get answers, you know, from Chatri, PT, or things like that, that's very good. But the question is, how is this trained? Who has control? So that's the human in the loop that human control over this, so all this has to be debated? And this is much more than digital tweet. Right?
So okay, so in light of what both my colleagues have talked about, in terms of the range of Prop dislocations and challenges, introduced by AI, let me spin back then to the question of governance, since we're here to talk about governance. So basically, the, you have, in broad terms, again, three kinds of positions that we've seen emerge that are associated with the three most powerful blocks in the global digital economy. And I don't think of them as empires, I don't really understand what that term is supposed to mean in this context. But what we have is a clash of digital capitalism's, the fight, the style of digital capitalism that's practiced in China is different from the one that's practiced in EU, which is different from the one that's practiced in the United States. And these are increasingly coming into tension. So in the Chinese kind of modeled the mark controlled, centrally planned kind of approach, their response to AI has been very much state driven. And they have attempted, but by virtue of the fact that so many of the companies are, in fact, either state owned, or heavily state influenced. In China, the government has tried to exercise a great deal of lead in pushing companies to respond to particular challenges, invest in particular areas, and seek clearance from the government to roll out large language models and all this other kind of stuff. And this has led to some problems. I think for them, I mean, together with the fact that the US has imposed all these bans, export limitations, and other kinds of policies to inhibit Chinese access to the kinds of chips and other technologies you need to really do this. The Chinese centralized approach, according to most things I've read, seems to be limiting the ability of a lot of Chinese companies that wish to be global players to be able to really innovate at the same level and catch up with what's going on in Silicon Valley and so on. So one of the long term trends, challenges for them is, as with data more generally, to decide how much closure do they want to have how much top down incenting by the state to perform in particular ways? And what cost is that impose versus how much do you want to try to open up and allow more access to the whole world? Because of course, obviously, the Silicon Valley companies or a Washington state companies, they're doing this They have vast access to high quality data that they've pulled from all over the Internet without the kind of restrictions that Chinese firms face. So at one end, you've got the Chinese centralized model. And the other end, historically, you've had the kind of more market driven laissez faire kind of approach that the US had taken that the OECD countries had embraced and their principles for AI and so on Japan, others, etc, where you try to allow market incentives to kind of flourish, etcetera, the US has recently begun to shift away from that approach a little bit, which I'll come back to. And then in between, you've got the European approach, which, surprise, surprise is all based on ex ante regulation, and precautionary principles. So the EU has adopted this is in the process of adopting the AI Act, which differentiates all kinds of different behaviors, or uses of AI to different buckets, high risk, medium risk, low risk with opposing with different types of restrictions and requirements on each of those. And now that the Biden administration is starting to move a little bit from its old laissez faire approach towards a little bit more engaged, trying to establish guideposts standards, etc. They had a big executive order that Biden put out recently that mandates a wide variety of different actions to try to track monitor and encourage particular kinds of behaviors and set standards, etc. So what kind of policy approach, what kind of governance model is best to balance all the different types of considerations that Derek and Wolfgang talked about is a big, outstanding issue. My concern with all of this is just to express a quick bias. There's a tendency when people talk about data and AI, to sort of depict them as if they're one single kind of bounded phenomena. And you can impose a set of rules to that phenomena. So people talk about, you know, I've heard people talk about the data sphere, right, as if the data as if the data sphere, we, we know, somebody who has a project on this, as if the datasphere was something like the cyberspace is some kind of coherent, identifiable thing that you could imply apply a certain fixed set of rules. But data is, you know, highly decentralized, and you utilize it in billions of different environments. And the kinds of rules that might be appropriate for the data that's on your phone are different from the rules that might be appropriate for machine to machine communications in an industrial IoT kind of setting. So and it's the same thing with AI, they talk about AI like is this one bounded fixed thing. And we will establish a solid uniform set of rules for when AI is effective, highly amorphous, broadly diverse range of applications in all kinds of different arenas. So one has to wonder whether they're getting it right, in terms of the governance approach, but these are the kinds of issues that are in play. Do we want to finish by going to digital trade and then open to the academic
one, one comment? So. So the governance models bill, I think are really interesting and challenging? I think you're absolutely right. And one of the things Wolfgang that I don't want to say worries me, but it's certainly something I'm thinking about, when you look at these autonomous drones. And you look at the way in which weapons systems are being starting to take advantage of these kinds of large language models to help make decisions and so forth. The more you study these things, the more you are, I'm absolutely fascinated by what they can do. If people use something like Chet GPT, as a search engine, you're going to get real limited kind of narrow responses. And if you think that's all that the load language model can do, you are sadly mistaken, you know, because what they can produce is amazing. Plus the fact that you can fine tune a large language model based on additional types of text data, to be focused on speed, specific types of conflict, and so forth. So now it's able to make even more interesting decisions within that context. And we had a we had a big debate the other day about AGI. So this idea of artificial general intelligence and is that possible or not? And is and for many people in the industry, that's their goal is to move to this space, where these kinds of tools can make decisions can make any decision that a human could be able to do and the problem is when they get aligned with these weapon systems, where, you know, and again, I'm not promoting this, I'm just I'm acknowledging that the people that are deeply involved in this have some major concerns about the impact of weapons autonomous weapons systems being driven by or used by These models and the potential danger that could come from that. And if you look at open AI, and the major leadership challenges that they had over the last six months or so, when the CEO was pushed out by the board, and then pushed him, you know, went to Microsoft, and then was pushed back in, part of the debate was, how fast do you want to go? So do you want to push the innovation and development around these tools in kind of an unfettered manner? You know, risk be done, you know, and, you know, what can you what can you accomplish? Or do you want to do so in a way that is so restrictive, that you lose the ability to innovate, and, and so forth? Or do you come down somewhere in the middle, and he was the one who was pushing for unfettered, you know, move as quickly as you can and forget about the consequences, he was pushed out. But then he came back in and so that shows you where even this unique quasi private sector private entity is driven by the same kinds of concerns. So it's a challenging period that we're living in.
Judas, do you want to want us to do a round on digital trade? Open it up? Yes.
It is a really important issue here, especially with as a concerns freedom, rights and freedoms of expression. And the US and a lot of Western countries are pulling back on what used to be the hallmark of open and free open and free the Internet. And now going back and saying, Okay, well, we want digital trade. So where we have to lessen our feelings or our strong points, and there's a lot of concern within the industry and within civil society in that, no, the US was correct, and what they were thinking and, and so I think this also plays into a lot of the digital trade and Internet freedom issues. So okay,
so why don't I just start because I've done a lot of work on digital trade site, if I could just do a little framing up quickly. So historically, since 1993, the main international framework for transactions, commercial transactions at a global level, among all countries, has been the general agreement on trade and services, or GATs, which was agreed with the establishment of the World Trade Organization in 1993. And that became a vehicle for liberalisation of flows of data and information, and all kinds of services that can be digitized and sent across national across networks across borders, and was really an important thing in the liberalization of telecommunications, the growth of the Internet and so on. And there have been recent efforts then to try to say, Okay, how do we upgrade the policy frameworks to take account of the specificities of the digital new digital trade environment where you've got plant forms and other kinds of business models and changes in the way the industry structure operates? And so on? What are the big problems was that a lot of governments did not accept, particularly countries like China did not accept that the commitments they had made under the GATT framework to liberalisation of their economy also applied to these new digital kinds of services with platforms, operations, a fan, etc, etc, the flow of data, so and so they there was an argument that we had to have new bespoke arrangements established. And negotiation process started in 2017, called the joint services initiative, which now involves over 100 countries trying to negotiate a new digital trade framework, that would be sort of a plurilateral deal, SSA, open to all WTO members, but not everybody had to join, it could be a smaller group that agreed to make commitments and so on. So the idea has been to try to establish a negotiating tax that would allow them for states to sit down and trade concessions to each other you. I'll open up my market for this, if you open up your market for that. That's how trade negotiations historically work. They've been trying to do this in the JSI. Now since 2017, and they've run into massive roadblocks and biggest issues have been over data flows, data localization, forced disclosure of source code, and Things like this. And the biggest problem was that there was almost no way to have a meaningful, strong agreement that cater to the needs of all the different major blocks. The US was pushing for a very free trade open market kind of approach. The EU was saying, Well, yes, but not no move into private personal data, we want a big carve out or anything like that. And the Chinese were saying, well, we don't want any kind of commitments on data flows and data localization, and those kinds of issues. We just want a sort of narrowly tailored e commerce Facilitation Agreement that deals with E contracting and technical matters, without making real hard commitments. So we went into these negotiations, trying to figure these things out. And suddenly, everything blew up this in a couple of months ago, when the Biden administration reversed course, and said, after, you know, seven years of pushing everybody for his strong commitments to free flow of data, and so on. So you know, we're abandoning those positions. Backtrack, completely took off the table American proposals for flow data, data, localization and source code. And discourse. This led to a huge controversy, I ran a webinar about this in a webinar series I do, where people talked about it in Great Lengths. And there's a great deal of consternation in Washington, which Judith alluded to. What's happened now is that they're in the negotiation in the UAE, trying to decide can we launch then the final round of negotiations around a common text, and the text that's on the table, dropped all the things that were originally intended by the United States and other proponents of free flow of data and so on, nothing about cross border data flow, nothing about forced data localization, nothing about source code. It's a very minimalist proposal that focuses just on technical facilitation of digital trade. And so a lot of people are saying, well, it looks like the multilateral system cannot deliver an open global digital environment. And we're going to end up instead, with a whole series of regional and plurilateral deals being agreed to by different blocks, rather than a broad, inclusive, multilateral framework, that raises real problems for developing countries who could easily be excluded from all that. So that's where we are at the Digital trade side. You want to answer?
Yeah, more or less, what Bill just described is the sad reality of the second decade of the new millennium. So 20 years, or 25 years ago, there was more or less the expectation that you know, the pressure of the globalization will lead to more harmony will not lead to the disappearance of conflicts, or different interests, but that everybody understands that working together hand in hand, Win Win scenarios will be good for everybody. So but as we have seen that a lot of big players did not play according to the rules, and probably the stakes were too high or national interests prevail. It was not only Maga and make America great again. So when, if you know and China is its cyber serenity. So it's first, it's China first, more or less. The China approach is China first, though I was last year in India and had a discussion in the, with people from the Indian parliament and the government and asked, you know, isn't this a moment for a digital non aligned movement as it was in the 1950s when the Indian Prime Minister Nehru said, we do not want to take the site of the Soviet Union with the Warsaw Pact or the United States with NATO. So we create an non aligned movement, though not alignment was not to be aligned with NATO and not to be aligned with was a certain way and non aligned movement. And my question was, you know, India now isn't the time ripe for a digital non aligned movement? And the answer was, No. So we are going to the g7 and to work with us. We are going to the BRICS and working you know, with Russia and other countries, it's India first. So and if you go through now, though, it's America first. It's China first. It's India. First. It's your first it's Brazil first. So what we do is this, you know is everybody is thinking do you know, our country fails probably Puerto Rico fails, why not? And at And this is the reality. So the reality is in conflict or the political reality is in conflict with the needs because things like climate change the pandemic and others do not know the frontiers, though. And I think this is the big difficult, your question of can we find common ground? This would be the best thing to find common ground, but how to do it? I think this is the challenge for the next decade. And it will depend also, you know, from the leader, so, do we have leaders who understand this issue? And, you know, I was impressed by the last article, Henry Kissinger wrote in foreign affairs, I think it was in October, November last year, just before he died in December. And he said, you know, it was the big achievement of the United States that form Walton 70 years at Bretton, which was available, was not used. And he said, Okay, it was because we had leaders who understood that we have to make arrangements with our enemies. And we have a common ground with them. We disagree and 100 issues, but we have a basic agreement and common ground. And he said, Okay, the best thing what President Biden should do now is to go to China, and to find arrangement to get AI under control. I was very impressed by this conclusion in the late, Kissinger gave a couple of weeks later, a Biden matxi in San Francisco. And they have agreed it's not big in the headlines, but they have agreed to establish a Chinese American working group on AI. So nobody knows what this outcome, there is no information about whether there was a meeting. But I think this is what we need, even if the next president is Trump or Biden. So this will not go away this conflict. And either we all will lose, or we will build found a certain common ground to reality at the moment is unfortunately, everybody wants to be first.
So we're supposed to be talking about hope today. So hopefully, we can end on a hopeful note, before we open it up to the audience. I just wanted to say that I think, you know, there is an opportunity under the guests to think about, well, let me step back for a second. One of the silver linings of the pandemic was our understanding of our better understanding and deepening and entrenching of distributed collaboration tools. You know, some of us remember when we first started doing these things, you know, having a, you know, a webcam we tried early on, but to have a web conference like this integrated, were the moderator for the darn panel, you know, is in Washington, and we're here in Puerto Rico that well, now that's very commonplace. But it took a long time to get to that point, and the pandemic deepen that, so that it becomes infrastructure becomes commonplace and accepted, that you can have deep rich interactions with people that are not physically in the same place. And I think that the GATS allows for these kinds of services, this global trading services and companies to be able to emerge, to take advantage of this kind of infrastructure, every one of you that are here, could be working on teams, you know, going after really interesting projects. And working together. Some of you I know, who have already talked to already doing those kinds of things. And so I think that my hopeful note will be how you can use these kinds of AI tools and others to keep deepening your knowledge base, you can learn almost anything you want to learn by using some of these tools that are out there. When I asked, you know, raise hands about programming, and only a few of you raised your hands, you can learn how to program using these generative AI tools, which makes you even stronger and more competitive working in these kinds of global spaces. So that's my hopeful note to end on.
Thanks so much. We only have like 10 minutes left. So I want to do go the questions. But first, Mildred, who's been moderating our chat here, has, it's gonna give a little summary of the value active conversation that we've been having on chat and then go start with that first question from remote. We'll do it.
Yes. Thank you very much. There was some really good comments around here. For the beginning, Marita and Novick was telling us regarding the digital economy that now our data or our self are actually the goods and services is is not that anymore. I have physical but our information in our time. So that's the big change of in the economy is our information that they want. And then nothing is actually for free if it's for free is because they want your data and your time. Then David boasted some really good things about that. Media, a social media advertising by a Nobel Prize winning economist. And the economy name was Paul Romer. And then we have some other co forcings married that was saying that. That sorry, no, no lonely, Marita, I just got who was But talking about every Yes, about the human rights and how it's an issue that we need to keep looking at it. Okay, for now. That's all and we are waiting for q&a.
You also have a question from the one on the panel from one on the remote participants. I can read it. His day from David Mackay is Is it time to move away from tech stack centered words like data and Internet to the human stack? Focus concept, like knowledge and knowledge governance, rather than data Internet governance. So take a couple of questions from the audience. And then we'll answer the panel will answer them. So because we only have like 12 minutes, five, eight minutes left?
Can we can we extend it a couple of minutes Mr. MC? If there's okay. We don't want to we don't want to cut people off if there's other people. Okay.
Hello. Yeah. Thank you very much. My name is Abraham Selby. And I'm happy for this discussion, because the panel panelists are more into academia. And my question is moving into academia, government that global digital economy, does student have hope, do we have hope in that, I was very happy many dimensioning about the artificial intelligence aspect of the global digital economy. The industry that we are coming up the people come in in the industry are from the academia, they are from the academic community. And now Moscou, even bands to them from using AI in terms of their academic learning and academic processes. Some even sanction students from using the AI. How can we address this to the academic community to know that not AI is not something that is bad for student to use, but they can rather take the student how to use the AI, because if a school is teaching the student how to use AI to be more competitive in the market, you can get away with a first class or any higher distinction and go out and you made that that job that is an industry and other students is using about few minutes to do and you use your your traditional way to do and they will pick the one who is more efficient and faster, using the new emerging technology tools to do it, but on you. So how do we make sure that AI does not become a threat to the academic community professors and most lecturers who embrace AI, and even used more to teach students how they can use it very well, in terms of the academic research and writings. And that thank you very much.
So just a quick response to that. I appreciate the question. So you're right. Many, many schools and universities have wrestled with this question of AI. It has, it has, again, the newness of this was really 2017 When you know, these, these transformer models started to come out and make it so easy to access a chatbot on your phone, to be able to answer questions. So the concerns that have emerged immediately were things like cheating and, and plagiarism and not using your own work and so forth. So the initial response by many, many educational institutions was to ban it exactly, as you said, going back to blue books and handwriting everything, well, that's not in line with the digital economy that we know exists. And so there have been movements now to try to encourage universities and high schools and maybe even below that, to understand how to harness these tools for for their students to think about how can you how can you help students to be better global digital economy participants by understanding how to use these tools. And so it means not running away from it. But also not recognize not ignoring the negative side of it. But to really think about policies and procedures that will enable it. So for example, in my class is starting last year. Unlike unlike some of my colleagues who were banning the use of AI, on all of my syllabi, I have a statement and AI statement that says, you can use generative AI in this course in any way that you think helps you. You just have to cite it, you have to show which prompts you use, which tools did you use? What was your input versus what was, you know, the generative AI input? So there are ways to, to use these tools to accelerate education, rather than just running from them. So I appreciate the question.
Sure, so I students since you can't see the room. I'll do we have more questions from students, and then I'll come to Jonathan, but it'd be good to hear from the students as much as we can. Yeah, so better. Yeah, well, okay. Hello. Yeah. Okay. Yeah. We'll take her first and then you afterwards. Okay. Thank you.
I can follow that. Yeah,
just take one at a time. Whichever you go ahead. Okay.
Hi, my name is yours. For the record. I'm an ASIC fellow, and a student as well. I'm glad we're having this discussion. Because it came up yesterday about whether we should actually encourage students to use AI, we came to somehow a consensus that perhaps for some courses, it will be irrelevant if they don't really use that. And we picked it up from the fact that if you ask students to use AI to let medicine or aviation, personally, I wouldn't want an AI student to operate on me in the theater. Because I don't know how safe it is. And we are still learning the system. So I guess the point I'm trying to say is that maybe we can consider what sort of training we want them to have. I do agree that the professor mentioned yesterday about AI illiteracy, I still believe we are so young. In terms of that. We are still doing research in that field. So so that's my contribution to to that side. But the question here is, we spoke so much about data, do young people know what it means to have data on the Internet? Do we know or we are just being taught how to? I mean, do they know that there's so much advocacy that don't put your data out there? But do they really know what it means because you wake up regularly. And then a young person is putting their whole itinerary on the Internet daily, from Monday to evening. I love taking photos I have virtually and tagging everything. And I still know that it's not safe to get myself I suppose there. But perhaps I don't know how dangerous it is to be there. So I just forget that. We'll put it out there. And I think those are some of the things. So again, what do we do? Do we just tell them to take care of yourself? Don't put it out there? Do we show them how I don't use dangers, but for lack of better words, I will still use that like how dangerous it is to have it out there and the after effect it would have on us. And the reason why we should limit what we put out there and what sort of content we should go out there. Do we have that? This
is a really important question. And I think it's something that people struggle with a lot and across countries, right? In the US, I think people have tended to sort of very freely give up their data. And it's gotten to be so much so that companies are constantly kind of pushing you to divulge stuff. And people do without thinking, you know, I mean, everywhere, even like I live in, I live in New York, I went and bought an ice cream at historic. I said, Okay, give me the receipt. This is where you have to give us your email, and we'll email you the receipt for the ice cream for a $5 purchase. My data then goes into some data bit base that's then going to be sold, you know, to a Data Broker. And this is how you end up like I've had multiple times where my data ended up in breaches. And suddenly I'm getting spammed, and God knows what my data is all over the place. You have no control. And people give up their data very freely without thinking about it. It's just like casual. Everybody's gathering your data. You go to the grocery store. I mean, I go on my grocery store app, and it tells me everything I bought in the past year. Do you want another can of soup? All that data is again being aggregated and it's all being sold and moved around. So obviously we Want to have some kind of appropriate privacy protections and limitations on the ways in which all this kind of stuff works. But we don't want it to be so onerous that we totally break business models and make innovation difficult. And I don't think anybody's gotten that, right. The US is way too free in letting companies do it, or though they want what data, I think the EU is a little too restrictive. And that has an impact on whether or not you get EU businesses that are really dynamic and innovative, and competing on a global stage. So this is a challenge for everybody, for sure.
And I think users have to be educated into the dangers of what they're giving up and what can be done with it. You know, and we talked a little bit yesterday about predictive modeling. So you know, when data is collected about you, a model can be built about what motivates you to act or not act. And so if you make all that data available, this is the level of manipulation about making giving you just what so the social media sites are using this to keep you connected. They know if you like funny things, or if you like angry things, they know exactly, they have a model that about you that can say feed this person, this stuff, and they'll stay connected. If and businesses are doing the same thing with consumers, what will it take to get you to buy X, Y or Z. So users need to be educated as well, we can't just rely on regulation users need to be clear about what they're doing when they're giving up so much information.
You're learning in the past force learning facts. Today, it means understanding processes, though that's different. So that means and you have to start very early to teach children so that they understand not just knowing the facts, but I have to understand the process. Now I can tell you a story a couple of years ago, from a Swedish teenager, a girl who put her diary into the Facebook page, and shared all her dreams and secret wishes, you know, in the hope that somebody will come and give her love and happiness. But it was not the prints on the white horse who knocked at the door, it was the better view. So if she would have understood this, she would have avoided doing this. So that's why you know, it's really early stage get what I said yesterday, you have to start into kindergarten.
Okay, so we're running late. So let's just take a couple more questions real quick. We'll shut up. And then we'll give final, very concise responses. We're all very verbose people here, but alright, so go ahead. Yes, no question. Yeah.
My name is Karima Nasik. Hello, thank you so much for a great discussion and comprehensive details on the digital economy. So my question is, like we discussed like some countries, India, Indonesia and South Africa, they are learning to impose the customs on the flow of the data, which will definitely impact on the consumers. So do you think that it can impact on the gap on in the digital divide? And you know, that these countries, peoples, they don't have the they are very poor, and they they will be impacted for this policy? So, I have another question like the ITU. Do you think that itu can play a significant role to our to enforce enforce all of these countries to avoid all of these policies? Because it can definitely impact on the people you're on in all of these nations?
Okay, so customs duties 90? Do we have any other questions anybody else want to get in? Who hasn't had an opportunity? Jonathan, do you want to ask your question? And then we'll then we'll respond to everything. Are you? Okay, fine. So then let's
go ahead. So I'll just take, I'll take part of your question. So, you know, I know that we see ourselves in a world that is, is is moving away from the multilateral institutions in terms of cooperation to, you know, to a large degree for many people. I still believe that there are a role for some of these institutions and some of these collaborative platforms and so forth. So within the ITU, both in the BDT and other areas is not about unnecessarily enforcing or forcing these countries to follow suit, but to use its convening power and its knowledge power to be able to bring the right people together with resources and facilities to be able to solve some of these problems. So I think that there's a role to play for the ITU and other similar organizations. So
yeah, so let me just do the customs duties part very quickly. The problem is again, thinking about data flows in the same way we think about the material world. So these governments are saying, hey, stuff comes shipped across the border, a car, textiles, whatever, I can stop it at the border, we take it off, we apply a customs duty to it, and then it moves on into the supply chain. This is not the way things work with data. And so try to impose these kinds of restrictions and charges on inbound data is going to require a lot of technical interventions in the way data flows work. And the costs, they if they think that this is like, the way you when I hear some of these governments talk, and I read their their position papers, it's like they think there's this free pot of gold out there. All those guys that go fine, they got all this money. And we'll just put a requirement that they pay us more money for the stuff to come in. But guess what those does senders of the data will simply raise their prices to consumers. So it will be the Indian customer of an OTT supplier who pays that, that charge if there is one, right? Because then the companies are not going to give up their profit margin, they're going to raise the prices to the inbound to the consumer at the end. That impacts then the ability of companies throughout the economy to utilize data and information that's come over the network to vitalize their economies. So it's actually counterproductive. It I think it does not help digital development. Instead, it will end up being gamed. And and and it'll be to the benefit of certain protected companies and some ministries who have bureaucrats who want to hire lots of people with little inspector caps and flashlights to go around looking at the data flows and going okay, now this one we want, we want X amount of money from it. You know, it's crazy to last but I just want an add on that I didn't get to respond. I forgot when when the woman initially was asking about common ground stuff. One thing that is interesting is there's been an effort to try to rethink negotiating styles. There was actually even an article about this in the New York Times today, that in the WTO, they're trying to move away from the old traditional approach for certain kinds of negotiations where each party comes in with its fixed position. And then you say, line in the sand. This is what I want. And everything deadlocked towards the more interest based discussion where each party is saying the other one, well, what is it you're trying to achieve? What how would the power, what kind of measures would be useful to you? Are there other kinds of things besides trade barriers, which might serve that purpose, they're trying to change the negotiating style of it. And we'll see if that can maybe ease the movement towards solutions on things like customs duties.
My final word is the ITU is a great organization. It's one of the oldest in the system of inter governmental organizations. And what I've seen is a very positive trend in the last positive shift in the last two or three years. Itu is moving away from trying to get control over the Internet, and is investing much more in infrastructure development. And this would be an enabler to help countries, you know, to make use of, you know, to make the right decisions, to enable more people to come online to build and people are still offline. And here there if it will concentrate all its resources and ideas into that direction. This is a much better investment and to fight the battle this ICANN about of controls the Domain Name System.
And I just want to end by saying, This is my third book giveaway. Third day. And so the question for today's book, this book is on researching Internet governance. It's also open access at MIT Press so you can get it yourself. But for those of you in the room, the fellows The question is the first person to give me three research methodologies that are used in researching Internet governance.
Thanks so much for everyone on today's panel. So ran 10 minutes over. But as you could see, we could be discussing for hours here, there's so much to talk about. But I will then return it back. And we'll see some of some of Bill again and another panel. So thanks so much, and turn it back over to our moderator Jonathan suck. Thank you
