 I got it, actually. Okay, beautiful. Thanks, folks. Welcome to June 24th, hyperledger supply chain, special interest group session on impact tokenization with Johannes Pulfort from the University in Vienna. It was an economic university in Vienna. Is that more right, Johannes? Correct. Vienna University of Economics and Business. There you go. And I'll let them talk a little bit more. So before we get going with Johannes, here are a couple of things I put in the chat. The hyperledger antitrust policy, we're open here. We want to have all opinions valued. Things that you share are part of the open world there, so don't share any proprietary information that you don't want out there. And you can read it at your leisure. Also, in the chat, you can see a link there from Daniela that has linked to all of the global forum for a couple of weeks ago, the sessions that are now up on YouTube. And there'll be an email going out later today, but if you are anxious and want to see them all now, there's a link to access lots of good presentations, at least when I attended it a couple of weeks ago there. And I'm looking forward to seeing some of the ones that I couldn't see at the same time there. And then one other thing here, we have two small projects going with our group, one around cataloging use cases for supply chain, and they're getting ready to send out a survey, that group there. So they've been meeting weekly over the last few weeks, so look for something there. And let's see, I've had a couple calls here with the RFI small project where let's try to come up with some set of questions and probably be focused more around logistics with a little bit around trade finance and a little bit around sustainability. And we're going to try to maybe do something with the climate action group as well as the trade finance group here. So look for a little bit more there, always looking for more volunteers if you like to participate in one of these together with the leaders, that would be great. So with that, let's move on to our presenter here. Fun fact with Johannes here, he played tennis on scholarship at the University of South Carolina. So he's a game cock. And also, if you ever want to talk tennis, you can talk tennis. But today, he's going to talk about a project that he's been working on for a few years here. Actually, it was funded by the UN Food and Agriculture Organization out of Rome. I became aware of it and I was interested. I thought it'd be valuable because there's been an increasing interest in tokenization in the hyperledger community. And so he's done some work associated with tokenization and specifically in agricultural supply chain. So I thought it'd be good for Johannes to share with us some of his thoughts and hopefully they can spark further thoughts in each one of us on the individual projects that we're working on or group projects that we're working on. So with that, Johannes, I will turn it over to you. Okay, thank you so much, Tom. Thank you so much for having me. I'm delighted to be here. My name is Johannes Britsford. I'm actually a doctoral candidate here at the Vienna University of Economics and Research. And in the last couple of years, I've been focusing mainly in my research on blockchain and supply chains. That is, by nature, a very broad topic. Nevertheless, I broke it down to three supply chains in my research. One is agricultural supply chain, which you will see today. The second one is about plastic supply chains and circular plastic supply chains. And the third one is around automotive supply chain and logistics. And as I said today, I will focus on the agricultural supply chains. So the topic of my research talk will be impact tokenization and innovative financial models for responsible agricultural supply chains. This research has been done directly for the United Nations FAO Food and Agriculture Organization. They have porters in Rome and they are very much looking forward to also contributing and helping to actually solve the or reach the United Nations sustainable development goals. And I will come to that later. Yeah, that's the story of today and that's also my background. And of course, I also work for other startups or research agencies next to my PhD. And I'm happy to talk about that also after the talk. So the agenda for today will be the purpose of the research paper, the key research findings that we got from expert interviews we interviewed over 24 experts from around the world and for the topics of blockchain agricultural supply chains and impact investment models. The second part of the second topic is event tokenization and impact measurement and verification in the agricultural supply chain, then how to monetize tokenized impact as an investment, what kind of results do we get, what kind of recommendations can we give. And then we can also dive into questions and answers. Hopefully I can answer your questions. At least I will try. So the purpose of the paper was actually directly, as I said, from the United Nations. They asked us to do a research on agriculture supply chains. And basically here, you see that we base our research on the guidelines or guidance of the OECD FAO for responsible agriculture supply chains. This document is actually a blueprint of how responsible agriculture supply chains should be done in the future. And our model is then of course to combine tokenization and impact investment models in order to actually enhance these guidelines and actually move towards the real realization of them by the means of technology and finance. So as you can see here, the purpose is clearly to show how these new forms of impact measurement and verification and tokenization can be leveraged to test innovative financial model that models that incentivize more responsible agriculture supply chains. So there's a plethora of issues and problems associated with agriculture supply chains on a global scale. And we try to address them with these new, let's say, tools. So basically what we find from the expert interviews we've done around 24 expert interviews with experts from a blockchain and tokenization, but also from impact investment models and the agriculture supply chain. And we try to come up out of those 24 expert interviews, which lasted up to 90 minutes each. That's a lot of, let's say, qualitative information that we got. And we try to summarize it in three key, key research findings that we found out. And the first one being the performance based financial models, where payments are dependent on measurable impact targets. So really connecting impact targets with payments provides significant opportunity to generate both impact and financial return for investors and can attract a larger pool of impact first investors. I will get into the details of what it actually means later on. But that as a guidance for what you should be listening or why you should be listening to me. And this is really combining financial models and really making sure that these financial models are not only looking at financial returns, but at impact returns. And that's going to be actually the currency of the future if you ask me personally, because we're going to have a big or we are having big issues coming up like climate change, plastic pollution, but also actually the agriculture supply chain is going to be affected by the climate change. Second point here is very much around the advancements in technology. So having a, yeah, that you can have real time scalable impact measurement and verification in agriculture supply chain, which then can unlock innovative performance based financial model. So it's the data layer. And of course, it's blockchain that is able to do such a such a real time and scalable impact measurement because it can record data in near real time for the different participants in an agriculture supply chain. And the third one is of course the ideal blockchain based agriculture supply chain solutions that are fit into existing financial frameworks, complimenting current free market incentive structures, and are managed with strong governance practices. So here also the dimension of strategic governance comes into play. What is out there currently? How can we use it? How can we leverage and actually enhance it through blockchain? And how can we use the technology in combination with the financial frameworks in order to promote responsible agriculture supply chains in the future? So these kind of three, if you stop listening now and you've got only these three points from me, let's say on a high level or you should remember, but of course the details also matter and especially in research, we also dig much deeper. But this is kind of, let's say the executive summary. And I of course, encourage you to keep on listening and try to learn more about these subjects as I think they are very important also of interesting, I hope, treatment. So what is event tokenization and what is impact measurement and what does it mean to verify that in an agriculture supply chain? So based on a typical supply chain that we have for agriculture, you have the supplier, the producer, the processor, the distributor, the customer, the retailer, the consumer. And in the end, the consumer mostly doesn't know what's actually where the food is coming from. What has been done to the food? Was it raised biologically? You have some certificates around, but you never know if you couldn't trust these certificates. And with a blockchain based solution, you actually start to implement a very much data driven approach. And we want to tokenize these events along the processes and the steps and measure the impact and verify it. So then we might have, for example, the supplier who is actually saying, okay, here's agriculture and livestock inputs, which are then sold to producers and registered on a blockchain solution or DOT, distributor ledger technology for X, you could try to enter the data. Then the producer is actually taking also the single X or at least the carton above it in order to make sure that this data point then can travel along with it with the good. Then the processor and then the government also needs to inspect the data and see, okay, is this egg or chicken or whatever product you have? Is it actually living up to the compliance and the rules that we have set up? The distributor wants to maybe show that, okay, the way the product is traveled. It always had a certain temperature and it was always safe. It was always measuring the conditions. The customs can see, okay, is this all done correctly? Are the certificates in place? And the retailer then of course can say, okay, maybe it's hot, then less X are used, or maybe if it's less hot, more X are used. So real time forecasting of how to see the demand because you now have data input and you can actually also try to model when do you need a certain product in which store. So it actually also breaks the room for AI. And of course, the consumer in the end wants to know where's the product coming from? How can I get a complete history of the product? And that in the end can be done via a QR code, which is then attached to the final product, which is can be seen by the consumer and just scanned by a regular smartphone. Yeah, I think in this group, I don't need to explain what blockchain is and DAT just very quickly. It's a way of also tokenizing data and making sure that you can actually store it. I will not spend so much time on this one, rather focus on tokenization. So basically, tokenization means that we can try to bring in the data points from individual events. And then we verify this event space on the data points that is basically needed to create a token and for to be minted into an impact event token, it needs to run through a burn of proof and needs to show that it's actually true. And I will show the models behind that later. But just here as a high level, let's say mind map, if the token or if the data points do not match the criteria, it will not be verified and then actually goes through the trash and the tokenization on individual events provides actually the foundation for impact investment models or for these innovative financing models. Yeah, so event trigger performance based tokens function on a smart contract. So let's assume there's an investor and this investor wants to say I invest in ESG criteria or impact criteria, which is defined, then he will put in the money into a smart contract, which is connected to a scroll account, then the implementer and seller needs to show that the event actually serves the right data points. Only if the event is verified, this information gets locked into the smart contract. And then only then the smart contract gets actually executed and also takes in the event verification data and this impact event token, this trick is actually minted. Yeah, and then this token then can be reassessed or redistributed to the investor who then can show to his stakeholders, okay, look, I have these tokens, these tokens have these data points, and I've invested the sum X and I've made this impact and it can be done on a very individual basis and even also then accumulated, but you can go down to the smallest detail and data level at which makes sense. And of course, traditionally impact financing was more like a black box. So as an investor, you give money, and then you get a nice report at the end of the year saying, yeah, thank you very much for your contribution. This is what we've done with your money, but you actually cannot probe and go into the details of it. And with our solution with impact financing with 100% impact attribution, we can actually link the outcome directly to the performance. So for example, you give money, you say I want to only provide money to let's say the UN STG number one, which is no poverty. And this money is actually just used for these kind of impact events. And that's actually the true beauty of blockchain and tokenization that you can break it down on such an individual level and verified individual events, of course, then serve as a high level impact outcome. So the combination of let's say 1000 or 10,000 small events can have a substantial effect on UN STGs or even globally. So it's also a scalable solution. And that means also that the investor or donor can show what is actually happening with the money. So this is kind of the comparison. Blockchain serves as a tool for tracking and verification. So we start always at let's say 0.0 and say, okay, currently the system works in a centralized fashion. And you have market dominance by huge retailers, maybe the small farmer doesn't get the money that he should get in order to also prosper and not have his kids work for his on his field, but rather go into education and actually develop more over. And blockchain creates for that transparency for the data point. So location, date, photos, confirmation codes, this all can be combined. It's immutable because it's chained together the blocks of information. So the data cannot be manipulated unless the community agrees and the change is public. And each token with associated data is assigned to one owner through the personal organization organization funded that makes also this ownership very important. And on the right hand side, you see, of course, how you can do it. For example, for ether or with the ether scanner for Ethereum, you have a hash from a certain token that is attributed to the Ethereum blockchain. Normally, it's the ERC 20 token, which is a token standard, which is then logging the data behind it, but you only see the hash. And then the hash, of course, contains the information that is needed. There's also a meaningful output measures for impact, the so-called Iris catalog of metrics. And this is very important in order to, for example, measure the right KPIs for the sustainable development goals. So for example, the average client agriculture yield, how much do you make per hectare for a certain period of time? So the organization knows what's going on, what type of scale we look at. And this is very important because it makes it measurable. And you can then say the impact of my investment is some, or I invest some X and the impact is Y. So it makes it very, very specific. Johannes, quick question is Tom, are you saying that this Iris catalog of metrics is one of many? And you just happened to pick this, or you think this is a good one? We think that's a good one because it's a very internationally renown in the impact space. So I think if you use this Iris catalog of metrics, you are on the safe side if you want to measure impact. Okay, good. Thank you. Any other questions out there? Just while I've got a break, we got a break here. Folks want to ask Johannes on some of the token. By the way, Johannes, I did like how you explained exactly how the tokenization and smart contract exactly. It's probably the simplest chart I've seen on that one. Should I keep going or? Keep going. Okay. Yeah. So data is proof of impact. So we know by history, of course, with any data system in garbage out is the main problem. And of course, you can have humanized or overhumanized data, which is coming from multiple human controlled mobile devices. And an example of this would be mobile app data pulled in real time from multiple workers to confirm location. And the other extreme, let's say, is objective data coming from non-human sources such as satellite images of farms, automated sensor data, IoT data, so really automated data. And I will show the difference of how it makes, actually, there's a continuum of verification. And the higher or the more you go to the right and machine verification, the higher the value of the data point. That will make sense in a couple of minutes when I will talk about the burden of proof. Because with this methods for technology-based data collection, we move from self-reported events from human reporting. And you never know if you should trust that. Or you can take it as a data point, but maybe the value attached to it in weighting the average score is less than if it's an IoT generated data. So on the left-hand side, you see the spectrum from basic technology to advanced technology, especially with regards to agricultural supply chains. So smart smartphones can shoot videos or photos to verify a certain point. But then you have drone satellites, IoT data, cross-referencing different data sources. So actually also triangulating data points to make sure that the data that is presented also is actually the truth and should be put into a token and then also be recorded on the blockchain in order to make sure that not false data is entered into the blockchain because then it becomes more or it becomes impossible to edit that again or you, it's very cumbersome. So this is very important to understand because this continuum is kind of also a way to value the data quality. And what does it mean for supply chain from shelf? Basically, let's assume we have an IoT-based supply chain, which is then using the technology-based data collection from the farm so it can monitor the soil moisture, how much water was consumed, leak detection, tracking of farm equipment, livestock tracking. So you can imagine taking constant video or movement of the cows and if the cow or if livestock starts to behave differently from the normal behavior, maybe it's sick or there's something going on and you can directly intervene instead of just measuring the impact and the problems afterwards, but actually you can try to solve the problem at hand. And farm to warehouse, of course, temperature monitoring, humidity monitoring, like exposure location of the truck, when is the product, when is the produce moving from A to B to C and so on. The smart warehousing, again, tracking of equipment, water metering, service buttons, temperature, humidity, motion detection, what is maybe someone taking out something of the warehouse, is there something going on, something fishy. So you can try to understand that the technology can serve as a means to improve the overall supply chain. With technology also, you can optimize the distribution, you can optimize the routes, again, temperature monitoring, occasion of the produce, when it is arriving at the final customer store, on time delivery notification, you can even think about smart contracts being payment releases and so on. So it actually can change the combination of IoT and blockchain can fundamentally transform the agricultural supply chain and I think that's a good strategy to aspire to. And then, of course, a satisfied customer who pays in the end doesn't have to have empty shares, for example, if it's a grocery or retailer, you can always make sure that you have enough produce in your stocks or in your shares, sorry. And then, of course, also maintain the optimal temperature for products where temperature is very important, fridge power, customer satisfaction button. So make sure that end consumer also is happy with the way the information is presented, with the way the information is transparent and so on. And I think that's very important here, of course, we need to take... Excuse me, Johannes, can you go back to that slide? Go back to... Yeah. I'm looking like, for example, on the farm stuff, why does that need to be on the blockchain? That seems... The only person interested in that information is the farmer. And that could... Yeah, you can have this opinion that you say that the data which is on the farm is only relevant to the farmer, but perhaps also the end consumer wants to know the story of the product and the data points. So if it's not only the location, but also the farmer himself, his story, the data points from this farm, so it depends also on the scrutiny that the end consumer might have on how much information they want to have for the final produce. You can also assume, as you indicate or imply in your response, that some consumers might not want to know so much detail, but then there's other consumers that they want to know every single detail, and it's just a big variance, again, in the end consumer space. Yeah, opportunities here. Of course, with a blockchain and IoT-based solution, you have real-time information, or near real-time information available. You have much more granularity in your data. You have increased reliability and validity of the data. We'll come to that in the next slide, how we can do that. We can lower the cost over time and increase efficiency. Also, one aspect is the entire topic of food waste and loss of products. If you have more data, then you can also improve that and not have so much waste and loss of material. Of course, in the beginning, it's expensive to set the system up. It will require an upfront investment, and perhaps this is also why investors are so important here, because they can provide the necessary incentives here in order to do such a project. In the pilot, perhaps, it requires training. You need to understand, why is the solution actually good? How can I do it? How is it beneficiary to me as a farmer? For example, it requires perhaps a new workflow, so disrupting the old structures. Of course, there are parts in the planet where we don't have proper internet access. Also, this needs to be taken care of, but it can be a good investment in the future, because it can make sense to connect remote places to the internet, and whatever ramifications that can have for the local community. Now, we'll come to the impact verification with an increasing burden of proof. So, burden of proof is basically a mathematical approach. I will show it later on in a more detailed slide, but this is high level. If you are not confident about the data points, below minimum threshold excluded from market, so the token will not be minted. Exclusion criteria does not pass the due diligence and the vetting criteria. Data only contains text from a manual data collection. So, again, this humanized data problem data fair verification checks. So, if you try to triangulate the data, and someone says it was sunny, and then reality, Google and satellite images show a huge band of rain, then this cannot be true. And then when we move towards completeness and uniqueness of the data points, we have tailored cross-sectional and long, longitudinal validation checks. So, this can be also automated with enhanced validation through self-reported data, which is then cross-checked against independent external online sources, like I was saying, like Oracle's, which can then be implemented, third-party human confirmation. So, imagine like auditors coming in, or other people independent from the producer. They confirm this manually conducted data from human verifiers. And then, of course, the best way to verify data or improve the data points is if you have machine-based confirmations or a third-party administered machine for data collection verification, say, okay, with a 99.5% confidence, we know this data point must be true because it has been meeting all these criterias, and then you can have the highest confidence in the 100% chance of impact occurrence, and you can measure it. So, that's always important to remember that we need to measure also the impact in order to make it attractive for investors. And here are some examples. You go back one chart, Johannes. Yeah. Sorry. And the third-party human confirmation, are you thinking of that like an auditor or certification kind of thing? Yeah. So, people are not related to the farmer and people who might be from the government and so on. So, very much not related to the profit of the produce, which is trying to be minted. Okay. And my general sense is that the thought is anything that comes from a human is less to be trusted than anything that comes from a sensor. Exactly. Is the way you're thinking about it. Okay. I have that going around in my mind a little bit, and I'm not quite there, but I just want to make sure I understood what you were saying here. Yeah. Perfect. I'll let you continue. Perfect. Thank you. So, some examples. For example, here we have a data point that says, okay, it looks quite rainy, right? Background is a location of the Google map, then the Google map zoom matches the background and says, okay, yeah, it looks like that there. Then we have a data point and a photo where someone is picking up something on the 17th of November, 2019. And then the exit meta data file also confirms that, okay, it was actually there. And what we're trying to show here is that pictures or this triangulation with Google maps or satellite images can be a way of minting tokens because you approve the overall score. And now we come to the confidence scoring, which is more for the math interested people perhaps in the call, where we say on the one hand side, we have a number of supporting data points. The quality of the supporting data points, the completeness and the consistency of the data points, the machine-based data collection points, third-party human confirmation. So what I was trying to explain earlier that from auditors or from the government or from certifiers and third-party machine confirmation is kind of the final one and with the highest weight. And then we have different scores per category. And then you can either score one, which is the lowest one and three being the highest one. So we'll not go through all the details here, but it just shows you the mathematical approach of how to come to a final score, which then can be compared to other data points. And over time, this database will also grow and say, okay, if we get these kind of data points, we know that it must be true with the confidence of 99.5%. And you see also this weighted score. So you can also play around here with the weights. We have decided to do it in such a fashion where, of course, third-party. You're honest, you're froze, I think. Am I having that too? Everyone else, have them freeze? Yes, I don't hear them anymore. Okay, oops, the bits from Vienna, man, are working. You might have to, okay, he got off a video at least. And of course, this is a point at which bias enters the picture. So this is the most important aspect. Can you hear me? Okay, now we're nice back. You froze for a while. Oh, sorry, what was the last thing you heard? We were just talking about how you can drive all these scores and the fact that you could weight them. Yeah, okay, yeah, then you didn't miss anything. I'm at university, but there's a lot of thunderstorms today in Vienna, so I'm not sure whether that can be somehow in making some interference to the internet. Sorry about that. No worries. Why don't you continue here before the thunderstorms get worse? Okay, I try to do my best. All right, let me move on. So I think just a question I had there was, it sounds like you're advocating a probabilistic weighting as opposed to, I think a lot of us, you know, especially if you think about it from a DeFi, it's either here or there, it's a black and white type of thing. So you're saying probabilistic is probably a better way to trigger some sort of impact event? Yeah, because we believe that different data points have different values and different weights. And why should it be black and white if there's a possibility for a huge gray scale, as we saw in the way you can verify information in the continuum of human versus machine. And there's a lot of gray area in between, right? So we're trying to make sure that the token that is minted is true, and then also for the investor as an asset. But yeah, that's kind of also the nature of it. Okay. Yeah, I think I would try to speed up a little bit looking at the time. But yeah, this is basically the case study where we incentivize sustainability as supply chain. So this is a fictive case study where we tried to showcase what we did with our approach. So bamboo is a furniture brand specializing in retail sale of sustainably produced bamboo furniture. Sustain chain is actually the bamboo furniture supplier needs upfront investment. And the sustainable agri fund is an impact investor who wants to give capital to sustain chain at 7% interest. And then the goal, of course, is to get sustain chain gets rewarded for sustainable production. But Boozer promotes impact to customers and minimizes risk and supply chain and sustainable agri fund makes financial return via investing in impact. So this is kind of the best case scenario. Then we have this kind of impact metric selection, where we could look at fair wages, work hours, condition packaging, we look at different data points that we can have from sustain chain. And then we prove this data points, we do it via technology, perhaps there's cameras, but also other means of verifying and collecting the data. We verify it against the model that I proposed. And then we tokenize the data and then we get also sustain chain hits the target from Boozer continues to order purchase orders and a sustainable agri fund reduces interest rate on the loan because it can actually take the value from the from the ESG or from the impact criteria, which was at defined in the beginning. And yeah, I think it's important here to understand that we can monetize these tokenized impact as an investment. And then we come to, and I will go through this just rather quickly. On the left hand side, we have different, the, let's say financial return oriented model, which is a traditional capitalistic one, where you only look for the money and the profit. And that's only thing you care about. And nowadays, most towards ESG, and let's say, sustainability and responsibility and impact and philanthropy is on the right hand side who doesn't care about the about the financial return, but rather about impact only. And yeah, this is just to keep in mind. And sorry, if I go quickly now, but also want to reach some time for question and answers. The interest bearing performance based model for pay for success is the one that we identified as being the, the one which we have in our analysis for our results. And the financial model is called interest bearing pay for success model. And it works the following way, the payer or the agrees to fund if verified impact if impact targets are achieved, then the investor pays upfront from impact delivery. So this is the stage where the money gets unlocked. Then the implementer uses upfront funds to deliver impact within defined time period. Then the verifier verifies data to ensure impact was achieved. The investor and payer receive impact data. And the payer pays back investor investors principle plus interest if the impact was achieved. So this is really much focusing on impact and making sure the impact was achieved. And that only then unlocks the funds to go back to the investor and also the implementer. And yeah, I will not go through this. This would take too much time now also to explain this, but just in insurance models, impact insurance models might be also a way in the future to think about these financial models. So I mentioned it, but let's focus on what I presented today. And here we also just show a model of how we came to the conclusion that the interest bearing pay for success model is the one that we seem to have the best score. So we looked at financial return, accessibility, replicability and regulatory feasibility. And we thought, okay, the interest bearing pay for success model is performing better than the other impact investment models. And finally, last but not least, I will come to the final recommendation. So support the development of a democratized pay for success investment platform and promote the piloting of impact-based loans because this can be a game changer of how to create responsible and agricultural supply chains. All right, a little bit more than 30 minutes. Sorry about that. Any questions from the group or any comments or anything unclear, which I should explain again. Yeah, Johannes, if you could go back a couple slides. Probably one more. Sorry, towards the end, go towards the end. Okay. One more. Actually, one more after that. There you go, right there. So two things. One is it sounds like you're thinking because of all these different factors, doing some interest bearing pay for success model, that that's the one to kind of go for from a tokenization perspective. Were any of these a capital improvement model? I couldn't quite tell. And what I'm thinking of this is people, yeah, they get into yield farming with cryptocurrencies, but most of them are in for the capital appreciation. Bitcoin's going to go to the moon, blah, blah, blah, you know, that kind of stuff. So I'm just wondering if you spend some time on the capital appreciation aspects of this. So we believe the capital appreciation effect is actually in the fact that you invest in something that is, let's say, sustainable and good for the overall planet. Okay. So that's the way we think about it. And I think if you keep on doing what we're doing now, you will not have any, any fields to, or any way to, to, to have sustainable agriculture supply chains, because you simply destroy the way nature is currently, and then it cannot grow anything anymore, because it's too hard, or there's too little precipitation, or you have other problems. So we actually need to shift to, to responsible agriculture supply chain in order to be also able to have value in the future. So if you ask me about the capital appreciation here, it's not maybe in the product itself, and in the way you say, okay, it's not financial, it's not other places in the triple bottom line. Exactly. And that has an impact actually in the end on the value of the, of the capital of the agriculture supply chain itself. So long term view, not, not like your quarter to quarter thinking, which you have in, let's say, publicly traded companies, but rather long term looking, okay, planet Earth is providing us with a habitat planet, and we're not using it appropriately. And then actually in the long run, maybe the kids of me or the grandkids of me might not be able to, to, to, to live on planet Earth anymore, because it's too hot. And then they might have to go to a different planet. Yeah. Yeah. Good. Thank you. Maybe I'll sell there. Questions, thoughts? Statement. Statement's okay, too. So my name is... It's non-controversial, Brett. Joe, go ahead. Yeah. Okay. I kind of have a dry sense of humor, so you're gonna have to, you're gonna have to give me a pass on that one. So yeah. And so in your presentation was, you know, exhilarating to a large part. And I feel that the focus on investment is something that is appropriate in today's time with Brexit and how the central banks are working together and how investors are going to be able to profit in all of these things. And so it just raises an issue with me with regards to taxation. Have you given any thought to taxation? Taxation for the model you mean? Well, I think if you have, you know, conglomerates, you know, I'm not trying to be controversial, but if you have a conglomerate and you have various subsidiaries, you have in this new tax regime essentially intro firm taxes that I feel can be solved with chain codes. So the tokenization I think is constantly introduced, but I think the chain codes or the smart contracts are really promising to deal with taxation. And have you given any thought to that? I mean, I'm not a taxation professional, but by shedding more light into what was previously a black box and by tokenizing individual events, I believe that the taxation regime or taxation schemes can be applied much more fair because you now shed light into what was previously unknown or not very transparent. So by having this data layer which provides the transparency into the different steps of the agriculture supply chain, I believe that the taxation system can then check, okay, at this location, it was changing perhaps the country, at this location, it was perhaps or at this step, it was changing not from one company within the company to another company. So yeah, yeah, so let me let me just stay specific because we're talking about tax regimes and I'm talking about corporate tax. And so if I have an intro firm, let's say, let's say I have a farm in Mexico and they're able to produce something for less than they are able to produce in Michigan, for instance, there's going to be a taxable event from one intro firm to the next. And I feel that the chain code could essentially potentially, well, that's my project essentially, but that is what I was thinking you could possibly have an interest in also exploring the intro firms like in, you know, like you have one corporation and they have little corporations or they have little companies, little intro firms, you know, within their larger corporation, and to be able to manage all of those taxable events is where I was going. Yeah, I think with tokenization, you can make that measurable and through tokenization and the smart contracts, you can even not only think about what I was trying to explain today, but also about payment processes, tax processes, you can actually load that up in the smart contract and say, if it leaves this company and goes to this other intra company, then this needs to be executed or released in terms of payments or authorship, and this can be done through blockchain and tokenization. That's the beauty of it. Right. Thank you. You're welcome. Good. Anybody else out there before we close up shop here for today? We have eight more minutes, right, Tom? We'll close up shop early if there's no more questions out there. I mean, once comments, critique, feedback, anything. Okay. Beautiful. Well, we're going to close up shop here. Johannes, thank you very much for sharing your thoughts here and your research and your sponsored research, search actually, and giving us some ideas that we can use to implement in future projects as we're going forward here, there. Folks that are listening and recording, thanks for listening here. Thanks, folks that participated live here, and we'll look forward to re-gathering in two weeks. So enjoy the rest of the day, everybody, wherever you are in the world. Thanks a lot. Bye. Thank you.