Henri Pihkala & decentralized data transportation

Henri Pihkala & decentralized data transportation – Streamr Network

Henri Pihkala

2020-05-05

The discussion around centralised vs decentralised data systems becomes extremely important as data becomes the “New Oil” and the source of value for many businesses. There’s a huge movement behind the idea of decentralisation for a fairer, more sustainable world around data.

One of the companies pioneering in decentralised data systems is Streamr. In this week’s episode, we’re talking to Henri Pihkala, CEO of Streamr about the benefits of decentralised vs traditional data systems, data crowd-sourcing and crowdselling, and how Streamr is creating a fertile ground for new business models around data monetisation and fairer value chains in the data context.

You can also find this episode on Apple Podcasts and Spotify.

Key points with Henri Pihkala

  1. Introduction to Streamr
    Henri Pihkala introduces Streamr as an open-source project that provides a decentralized infrastructure for real-time data sharing and monetization. He states, “Streamr is an open-source project, and it provides a decentralized and open infrastructure for real-time data sharing and monetization.” This sets the foundation for the podcast’s exploration of Streamr’s role and significance.
  2. Decentralization vs. Centralization
    The podcast emphasizes the pivotal role of blockchain and cryptocurrencies in showcasing what decentralized technology can achieve that centralized technology cannot. Pihkala articulates this by saying, “Currently, the sort of blockchain and cryptocurrency scene has been the sort of spearhead of showing what decentralized technology can accomplish that centralized technology cannot accomplish.” This distinction between decentralization and centralization in the tech world serves as a backdrop to Streamr’s unique position.
  3. Streamr’s Position in the OSI Model
    Pihkala explains where Streamr fits within the OSI model. He explains that Streamr occupies a space between the transport layer and the application layer, acting as middleware. This enables data producers and subscribers to remain decoupled, as he clarifies, “They just publish the data using the protocol onto the network. Then whoever is listening, whoever is subscribing gets that data.”
  4. Use Case for Streamr
    Streamr’s primary use case lies in data sharing and data monetization. Pihkala highlights the concept of a data marketplace as a key application enabled by Streamr, stating, “The number one obvious application that this infrastructure enables is a data marketplace.” This marketplace allows various parties to buy and sell access to data streams on the network. Additionally, the podcast explores data crowd sourcing and crowd selling, where users can pool and monetize their data collectively.
  5. Data Crowd Selling
    A significant innovation discussed in the podcast is data crowd selling. Pihkala introduces Data Unions, a framework within Streamr, that facilitates data crowd selling. He explains that Data Unions allow developers and businesses to easily implement this pattern. Pihkala describes the benefits, “It becomes very easy to set up this kind of data crowd sourcing and data crowd selling pattern.” This concept opens doors to various business models and revenue streams, including users offsetting the costs of services by monetizing their data.
  6. Impact on Business Models
    The discussion concludes with insights into how Streamr’s technology can impact business models. It can empower enterprises to engage with their customer base in novel ways, allowing customers to have control over their data and potentially monetize it. Pihkala explains, “It’s a competitive advantage… anyone would choose the phone that lets you earn with your data.” This shift toward user-controlled data monetization represents a fundamental shift in the business landscape.

In the future, Streamr envisions a decentralized data landscape where data sharing and monetization are the norm. With its infrastructure positioned between the transport and application layers of the OSI model, Streamr enables data producers and subscribers to operate independently, fostering a marketplace for real-time data. The introduction of Data Unions and data crowd selling opens doors to innovative business models, allowing users to control and monetize their data. As Streamr continues to iterate and expand its roadmap, it aims to establish a token incentive system, incentivizing network participation and ensuring the long-term decentralization of data transport services. Streamr’s vision promises to transform the way businesses interact with their customers, creating a competitive advantage by putting data control back into the hands of the users.

Transcript

Ben Sheppard  00:00

Welcome, everyone. So, in this week’s podcast, we’ve got our special guest, Henri, who’s the CEO of Streamr project. The topic of today is the Streamr network. So, we’ll kick off as we usually do with a few introductions. I’ve got my co-host, Jarno here, and Henri as well. So, Henri, would you like to introduce yourself?

Henri Pihkala  00:24

Sure, why not? Thanks for having me, guys. So, my name is Henri, and co-founder and CEO of Streamr. Streamr is an open-source project that we’re going to talk about extensively, I hope in this podcast, I have a technical background. So, I started as an engineer, I’ve been working on real time data for, you know, my whole professional career and even before, so I started in financial sector where I was building like algorithmic trading platforms and models, and went from there to sort of cloud, IoT, big data infrastructure kind of stuff, and then started the Streamr project to build new type of decentralized data infrastructure that I’m going to tell you all about.

Ben Sheppard  01:18

Great. Thanks.

Henri Pihkala  01:19

That’s pretty much me.

Jarno Marttila  01:22

All right. Yeah. Hi, everyone also here’s Jarno Marttila, TX head of tech and co-host of this podcast. Yeah, like it sounds, ask good questions, but yeah, I mean, we also see a little bit of background with the handling. So, we’re used to seeing in the same office before the Streamr project.

Ben Sheppard  01:40

Great. Thanks, guys.

Henri Pihkala  01:42

Ben is so popular that you don’t need to introduce. Tell your fans know you already?

Ben Sheppard  01:48

Yeah, Ben Sheppard. So, I’m managing director of TX, you get used to my voice if you’re listening to this regularly anyways. Okay, so topic of today is Streamr network, in particular, which is one of the components of the technology stack that the Streamr project is building. So, I wanted to kick off by asking Henri, what is the Streamr network, and you’ve got a technical background? I know that, but I’m going to challenge you now. Can you explain it to us in layman’s terms, so that I can go back and tell my mum tonight what the Streamr network is?

Henri Pihkala  02:30

Yeah, I’ll try. But, you know, you can poke me if I drift off too deep depths. So, basically, the Streamr network is a data infrastructure, right? What it does is it transports messages from data publishers to data subscribers and it does this in real time. Now, by messages, I mean, all kinds of machine data readings from an IoT sensor or other connected device metrics triggered by you know, users interacting with applications, maybe activity Streamr from financial markets, etc. So, just to understand such a message bus is sort of like a basic primitive, it’s a basic building block of pretty much any data pipeline. Why does someone need something like this. So, I’m going to be general, here, not going into the specifics of Streamr just yet. So, in general, this type of system is needed, because usually, it’s not feasible, that the producers of data and consumers of data connect directly to each other and there’s many reasons for that. The producer could be an IoT device, maybe its battery powered behind a firewall, you know, anything, it’s incapable of serving connections in the first place or there could be millions of producers, such as users of an application, and a subscriber who wants all the data they can’t possibly connect to each and every source. So, you need this kind of gateway to serve the data in between. What makes the Streamr network special compared to other message brokering systems is that it’s decentralized. I’m anticipating some further questions about that. So, maybe I’ll stop here and let you know, lead us deeper into what’s especial about Streamr?

Ben Sheppard  05:02

Do you want to comment, Jarno?

Jarno Marttila  05:05

Sure. Yeah. I already wrote some hard questions here. So, this is a bit technical, though, but these are some things like we often the plan I get asked from ourselves, like, you talked about, the messaging itself, but then I’m sure you will talk more about probably the Streamr network and Streamr protocol, but some of the audience is probably not familiar with, like, what’s the difference between these? How would you say and what is the difference between when people talk about Streamr network or Streamr protocol itself?

Henri Pihkala  05:45

Okay, so the protocol is, you know, any network consists of nodes, basically, the nodes form the network, like a graph of sorts and the protocol is the language that they speak with each other, like we are humans, and we speak English now with each other. You know, in a network, you have nodes that speak the protocol with each other. In the Streamr network, there is nodes that speak the Streamr protocol, with each other, and it’s really as simple as that, more or less.

Ben Sheppard  06:27

Okay. Can I pick on something you said, in your earlier statement there, Henri, you said the Streamr network is decentralized. So, what does that mean? Can you just elaborate on that for us?

Henri Pihkala  06:40

Yeah, for sure. I love to sort of got a bit deeper into that maybe it’s beneficial to understand how this kind of message brokering systems traditionally work and see the way in which they are centralized and then talk a little bit about decentralized and what value that adds into the picture. So, basically, like current systems rely on a server or a cluster of servers, that sits somewhere on the internet, and it’s run by some single party. All the data in a given pipeline goes to that single point. In this case, we say that this is a centralized approach, because there’s a central point, there’s a single point. It creates a single point of failure in terms of both technologies, as well as business risk and it leads to data silos, because these centralized pipelines don’t interact with each other. Now, like I mentioned, the sort of defining special feature of Streamr is that it’s decentralized. It means that the network can be operated by any number of parties simultaneously. They together create the message brokering service. So, it doesn’t depend on any single machine, any single server, or even any single company or party to running. This is very good for security. This is very good for business risk. This is also very good for scalability, and network effects. So, maybe diving a bit deeper into that. So, it’s good for security and business risk because it removes trust from the picture.

Existing systems, current systems rely exclusively on trust. You know, you buy a service from the, you know, Amazon Cloud, and you trust that they won’t spy on your data, they won’t bill you extra, they don’t suddenly stop providing the service or triple their pricing or anything like that. You trust them and, in many cases, it’s reasonable, but in you know, going forward that can be the basis for future data infrastructure, and in contrast, decentralized, they heavily apply cryptography and incentive mechanisms and game theory to completely remove trust in counterparties or remove the need for trust in counterparties, meaning that the only thing you need to trust is basically math, right? If you trust math, you’re good. You don’t need to trust the honesty of any party in the system. I also mentioned, scalability. So, maybe we can talk about that a little bit, it’s quite easy to see how, you know, with the exponential growth of data being produced by connected devices and our increasingly digital lives, it’s pretty essential not to have all that data pass through single points of failure, right, this is, pretty intuitive. These single points, these centralized points, they become huge risks, and they become huge bottlenecks. In contrast, a decentralized network, it can reach any scale, the very extreme of which is that basically, all the machine data in the world could go into such a network. If we reach that kind of stage, it creates also huge network effects. Because, you know, any data stream on the planet is suddenly available via a single interface, using a single protocol. So, it becomes a bit like the World Wide Web, but for real time streams of machine data, instead of, you know, our World Wide Web of websites for humans, kind of thing.

Jarno Marttila  06:43

I guess, with an increase in number of IoT devices, forecast over the coming years, this is going to become even more important, isn’t it, because there’s going to be so much more real time data, you know, sort of being transported around that, you know, the centralized systems, perhaps they’re not as well set up to deal with this, it sounds like a decentralized system, the way it can scaled or explained, is well suited to them.

Henri Pihkala  12:10

You know, this is exactly the beef, I mean, centralized systems work for now, but if you look five years, or 10 years into the future, you’re going to run into problems, either from the scalability perspective, or from the trust perspective, like internally is one by, which gives them a lot of power over things, and our economies and business models and everything. So, sort of based solutions we haven’t had, in better ways for so everything’s falling into the hands, the monolith giants that try to optimize their profits and so on, and not necessarily advance the well-being of mankind, but basically, the step towards decentralized technology can solve that. It will take time, for sure for this kind of technology to be adopted and breakthrough and for companies and people to see the benefits. Again, centralized technology, but there’s a huge movement, standing behind the sort of idea of decentralization, and already started back in the days when the early internet was being imagined and conceived. Then we sort of dropped into this valley of centralization, for practical reasons. I think it’s a temporary stage and temporary phase, and we’ll be able to climb out of this valley of centralization and reach something that’s actually the original idea of the internet and it becomes increasingly important as data becomes the New Oil and data becomes the source of value for many businesses.

Jarno Marttila  14:15

Yeah, it’s interesting, kind of different outcomes that come from decentralization, properly. You’re very familiar 100 with this mechanic wateriness block of meaning of decentralization, dimensions like the different types of decentralization that you have architectural, political, and logical kind of dimensions, like pilot can be centralized, different information systems.

Henri Pihkala  14:48

Yeah, go on. Sorry.

Jarno Marttila  14:51

It sounds like this, brought up athletes that take some of those boxes like it’s definitely architecturally decentralized.

Henri Pihkala  15:00

Yeah, and the political aspect is very interesting because there’s analogues in the world. I mean, you know, we have countries here on the planet, and some of them are dictatorships, where power is extremely centralized and some of them are democracies, where power is very decentralized. I don’t have a statistic on this, but I would say that most people might prefer living in under decentralized governance under a democracy. Whereas, of course, dictatorships can achieve certain efficiency benefits, where, for example, things like decision making are very fast, but not necessarily very fair. So, it depends a little bit on what parameters of a system you’re trying to optimize and when it comes to technology, also, centralized systems can be very efficient, you know, players like Amazon and Microsoft, they can sort of achieve the economy of scale, and you know, they can build very efficient data centers and buy electricity, very cheap and produce their own energy and stuff like that, but you know, the power is still concentrated, and the points of failure are still concentrated, and the decision makings are concentrated, which creates risk and while initial decentralized systems can be in sort of slightly more inefficient in the same sense that democracies are more inefficient in making decisions. At the end of the day, I think democracies and decentralized systems lead to a better quality of technology and a better quality of life due to the reduced risk and increased fairness properties.

Jarno Marttila  17:15

Now, this is interesting analogue to my belief of Finland, dictatorship, and kind of centralized systems that in cases they were very well, but also [Inaudible 17:29].

Henri Pihkala  17:33

For sure, any employee takes, I guess, the direction generally has been towards the centralization, and you know, that’s sort of an argument that the same might happen in technology, as well, but who knows, this is our belief. It’s still early days, and it’s almost like a religion. So, you have to fight for what you believe and demonstrate the benefits. Only by doing so you can sort of onboard others onto the bandwagon of decentralization. Currently, the sort of blockchain and cryptocurrency scene has been the sort of spearhead of showing what decentralized technology can accomplish that centralized technology cannot accomplish. It’s been very interesting what happens there, but that’s not nearly everything that exists under the much much bigger umbrella of decentralization in general. So, blockchain and crypto is basically like a special case of decentralization and then there’s many other technologies and systems such as Streamr, that exist under that umbrella.

Jarno Marttila  18:51

Yeah, it’s very interesting view to it. If we go a little bit from this abstract side to more to general level into practice, and it’s a bit technical again, but if you imagine the OSI model, like the general model of what kind of layers do you have on networking, my first memory is the physical layer and the last the highest level is the application layer. Where would the Streamr network in practice like mine on that model?

Henri Pihkala  19:30

So, it sits between the transport layer and the application layer basically, so you have the physical layer? Then you have the TCP IP layer, basically. So, the Streamr network builds on top of those like primitive point to point kind of transports which are the IP transport and TCP IP. So, it sits there, it’s sort of like a middleware, and then applications call next to the Streamr network, to use the services. That service, of course, is the message transport, but what happens is sort of what I explained earlier, this decoupling of the data producers and data subscribers. So, if you only had the protocol stack of the internet, the basic TCP IP stack, then each party would need to connect directly to each other, and they wouldn’t be decoupled, but in the Streamr network, we use a pattern called Pub-Sub or publish subscribe, which means that basically, the data producers, data publishers, they don’t care, they don’t need to know who’s consuming the data. This is why we call it decoupling. They just basically fire and forget, they just publish the data, using the protocol onto the network. Then whoever is listening, whoever is subscribing gets that data. So, you know, they never meet face to face, in a sense. It’s like, I have a package. I want to send a package to you guys. You know, on the internet, the basic internet protocols that I would have to come to you and hand it over to you like, from my hand to your hand directly, but you know, instead, now we have this post office, and I can just take the package to the post office and leave it there and it will sort of magically be transported to you guys and you know, it’s much easier and much safer. I don’t need to catch Coronavirus from you guys because, you know, we never meet face to face, I don’t need to know where you guys live. You know, these kinds of things and the same analog applies in the Texas Old World.

Jarno Marttila  22:01

So, let’s say if someone was Ben’s tech savvy grandmother of 100th day, I start to use a Streamr network in practice.

Henri Pihkala  22:10

Usually, you use an application that uses the network, like I said, Streamr network is middleware. So, an end user, like your grandma, even if she’s tech savvy, she probably wouldn’t use the Streamr network directly unless she’s a developer. So, the people who use the Streamr network directly are developers, they build some kind of message pipelines or applications that need the message transport service. Then that constitutes the Application Layer of the OSI model. Then over there, you have the grandmas, and whoever, you know, business users and whatnot, depending on the application who use that application, which uses the services of the network to do this data transport.

Ben Sheppard  23:04

So, on that note, then what sort of apps can be built on top of the network? If you could name two or three apps that are sort of well suited to using the network, what would they be Henri?

Henri Pihkala  23:15

Yeah, I think that’s a good question. So, before naming applications, I think it makes sense to think about patterns first, like what kind of use case patterns are well suited for this network, what kind of patterns are hard or even impossible using traditional centralized solutions. Then the individual use cases are found under these patterns. So, since such a network is an open platform, which anyone can use, it lends itself really well for use cases that involve data sharing, right? Now, data sharing can happen for free, for example, publicly in case of open data, such as produced by smart cities, or governments or whatever, or data sharing can happen privately within a company or within a consortium of companies. So, that’s free. Alright, data sharing can happen against payment, which brings us to the general pattern of data monetization. Now within these sort of high level, primary patterns of data sharing and data monetization, I think the number one obvious application that this infrastructure enables is a data marketplace. A data marketplace is an application that different parties can use to buy and sell access to data streams on the network. This idea, it’s such a key application, an idea that we included building a basic implementation of a data marketplace, within the scope of the Streamr project, in addition to the network itself. So, it’s an open-source application, anyone can run a marketplace. So, we’re expecting that ecosystem will sort of consist of many marketplaces, maybe they’re focusing on different kinds of data or different business verticals and whatnot, but basically, that’s one application, and sort of going a bit like one step further, from there, like now that we’ve established the concepts of data sharing and monetization, and that there can be this application that facilitates data monetization, namely the marketplace.

There exists an interesting pattern of data, crowdsourcing, or even crowd selling. That means that, for example, everyone using a particular connected device, or application, like, you know, maybe you’re driving a Tesla connected car, or maybe you have a certain brand of phone like Samsung, or you use an application like a web browser. Users of these things could combine the data that they’re generating into a pool of data, and even sell it on the marketplace in a way where they share the revenue from that data and split it among all those data producers. This crowd selling pattern is something that we’ve been exploring, and building infrastructure for, sort of, on top of the Streamr network, again, layers upon layers upon layers, unlock new cases. There’s a couple of pilot applications being created for data crowd selling in both the enterprise sector as well as sort of mainstream and user, your grandma etc. For example, there’s an application called swash, which is a browser plugin that observes your web behavior, such as what you search for on Google and then it anonymizes that data and sends it to the Streamr network. This data is being sold on via the marketplace application. It rewards each user with a share of the proceedings. Now, this is a very interesting application were because it’s fully transparent. So, even though the idea of sharing your web behavior might sound a bit scary at first, it’s important to understand that these centralized giants like Google, you know, they collect that data anyway.

They’re the only ones that can access that data actually, like only Google knows what you search for on Google and breaking those silos. That seems like an interesting idea and the big difference from the privacy point of view is that here, you’re in control, and you get the rewards, instead of like, sending your data off to some black box, and you know, it gets sold to advertisers, and so on. So, it’s very interesting what kind of disruptive applications can be built with this kind of decentralized infrastructure that enables both data transport as well as payments, typically using blockchain and cryptocurrency to be carried out in a peer-to-peer fashion instead of having these centralized giants in control of those data flows and value flows.

Ben Sheppard  29:15

So, with my business hat on for a minute. So, what I heard in there is is potentially a way that an enterprise that has a large customer base could use this technology stack to basically empower their customer base to have control over their data to sort of opt in to say, yeah, we’re willing to monetize and sell this data. Then the enterprise could work with its customer base jointly in partnership if you’d like to sell that data through one of these data marketplaces and everyone seeks to win from this. Everyone can receive parts of those rewards. So, it’s a whole new business model, isn’t it for these activities.

Henri Pihkala  29:57

It is. It’s a competitive advantage as well. Like imagine that, you know, you’re shopping for a new phone or whatever. You know, you have two options. There’s two options, there’s two phones in the store, and one of them lets you earn with your data. While the other one just, you know, sends your data off to a centralized giant, or a dictator, country, or whatever. So, of course, anyone would choose the phone that lets you earn with your data, maybe you’re able to offset the costs of your mobile plan, with the revenues you’re earning from your data or whatever, maybe even becomes free, at some point, if the value of the data becomes greater than the cost of transport.

Ben Sheppard  29:58

Maybe it could be a donation, maybe the deal could be, you know, you agree to monetize and sell your data and to donate it to a charity.

Henri Pihkala  31:12

Yeah, exactly. It’s possible and all kinds of models can be explored and built. So, what we are trying to build in the Streamr project is basically this fertile ground, where that unlocks these new kinds of patterns and which applications exactly get built. You know, it’s sort of up to the creativity of the world, our goal is just to make it possible. Then others, such as you know, independent innovators are companies like TX, can try and find where the business value is, and what kind of new business models and business processes and revenue streams can be unlocked by applying this kind of new technology.

Jarno Marttila  32:10

It’s interesting discussion on there’s like to evolve with my business and also on honestly, I’m like, kind of two topics that we’re discussing is like direct data monetization. Then there’s also this indirect data monetization. If I changed my software developer hat on, I would say that, oh, this is very interesting. Like, if I came, let’s say, [Inaudible 32:30], game, I would want the creator say, in game chat. Maybe I could leverage this to communicate cheaper to each other, using decentralized peer to peer network, instead of like, being must say, for our centralized party, to host my chat messages.

Henri Pihkala  32:48

Yeah, sure. I mean, chats are a very good match for this kind of messaging transport, it’s basically conceived to be a chat among machines, usually, and applications, of course, count as machines. Whether it’s sort of human-to-human messaging or machine to machine messaging, that doesn’t really matter. All these patterns require the same kind of infrastructure and either it goes via a centralized server, which, especially in case of human-to-human messaging has pretty strong privacy concerns, or it goes over a decentralized network, where the privacy concerns are much less.  Can I ask you a question? So, you might direct monetization and indirect monetization and I’m now putting my grandma hat on. So, could you explain what you mean by indirect monetization?

Jarno Marttila  33:57

Sure. Direct one, let’s start with that. First, I see that you’re literally earning on selling your data.

Henri Pihkala  34:07

Yeah, right. So, you’re someone’s buying, someone’s selling data is being transacted, okay?

Jarno Marttila  34:14

You get something, you get value straight from that. Maybe it’s tokens, maybe it’s cash money, maybe it’s part of goals in a way that you’re gaining revenue out of it also, or value, but it’s not directly cash money on your bank account, but it might be lower costs of running a service or it might be new business models or new type of revenue streams that you can measure but would create money in the end.

Henri Pihkala  34:50

Yeah, okay. I got it. I guess like instead of hard cash or revenue, also things like you know, lower risk, or increased privacy or things like this also count as value, you know, depending on the use case in some use cases, you know, some of these are not that important, but in some use cases they definitely are. So, there’s many forms of value that this kind of systems can capture, not just like raw revenue from direct data monetization.

Jarno Marttila  35:32

Skewed question.

Henri Pihkala  35:34

Yeah. Sorry, for hijacking.

Ben Sheppard  35:41

Okay, so, back to asking you difficult questions, then Henri. Let’s flip it back round, you know, before we get too many. So, last one, actually, before we wrap up the podcast, looking ahead, then what exciting things can we expect on the roadmap? What’s coming up next, Henri on the Streamr project?

Henri Pihkala  36:05

Yeah, happy to talk about that. So, maybe just to sort of create the frame work like Streamr is definitely like a long-term ambitious project. We’re only like around halfway through our initially conceived roadmap, but that sad, we have everything up and running. It’s sort of iterative improvement from here. The sort of big new thing that we’re launching, within a couple of months, or maybe in a couple of months from now means basically, like, late Q2, or so is our framework for data crowd selling. So, I talked a little bit already about data crowd selling and data crowd sourcing and how this revenue sharing among many, many data publishers can occur. So, we’re building a framework for this called Data unions and what that enables is for developers and businesses to easily create implementations of this pattern, so basically, all they need to do is to create the data gathering part of it. As long as they can integrate the data, send it off to the Streamr network and sort of connected to the infrastructure that we’re building, then, you know, everything will happen almost magically, from there in a technical perspective, but of course, you still need to go out and you know, market your data product to get buyers for it, and so on, but from a technical perspective, it becomes very easy to set up this kind of data crowdsourcing and data crowd selling pattern. So, that’s one big thing and you know, it might be sort of killer application or killer framework that drives adoption of the Streamr technology and decentralized technology in general, because it’s something that cannot be done easily with centralized solutions and fiat money, because they’re just not flexible, not scalable enough, and there’s too many middlemen, and stakeholders and restrictions and that kind of things in there. Another important topic that we’ll be sort of focusing on between now and the final stage of the roadmap is creating the sort of token economics or token incentives on the Streamr network layer.

So, right now using the network is free. I mean, data can be published and subscribed on the network level for free. Of course, you can ask for payment on the marketplace level, but basically, the transport the sort of postal service that I described before, where I send a package to you guys at the moment that’s free. What we want to create is basically a mechanism that allows us to pay the mailman for delivering that package to you guys and what that means in the Streamr network is that like I said, the network consists of nodes and together those nodes provide this data transport service. What we want to create is a mechanism that allows value to be transferred from the users of the network, who are data publishers and data subscribers. To those who are providing the service of the network, who are the parties running the nodes. So, basically, by running a node, you can trade some of your idle bandwidth for tokens for value, so you’re basically like getting something out of your idle capacity in terms of bandwidth. That bandwidth gets used for brokering data in the network, from publishers to other nodes, to subscribers, basically. So, that incentivizes people to act as the mailman in this service, which leads to decentralization, because now many different parties are incentivized to participate in running network, which is exactly what we want in order to create a decentralized network with multiple independent parties, producing a service, right? So that’s a difficult thing to build. You know, it involves some in depth research, coming up with novel mechanisms. It requires simulating and testing things at scale. It requires considering that some parties in the network might be malicious or, you know, assuming that all parties in the network are greedy, and selfish and that kind of things and making sure that we build a mechanism that well basically doesn’t self-destruct in the real world, right? You know, it’s tricky, and it will still take time. That’s in one way, like one of the major remaining milestones of the Streamr project that we are slowly but steadily heading towards.

Ben Sheppard  42:07

Okay, great. Well, it sounds like we should get you in for another podcast after that’s launched and we can talk about that in some more detail.

Henri Pihkala  42:15

Sure. Happy to do that.

Ben Sheppard  42:18

Okay, great. Well, that’s the end of this podcast. Thanks very much for coming on the show, Henri.

Henri Pihkala  42:27

Thanks for having me. I’ve been having a lot of fun.

Ben Sheppard  42:30

If you want to hear more information about the crowdsourcing, the third podcast that we put out with [Inaudible 42:41],  talks about data unions in more detail. So, you can hear it from him and what he’s been doing on that. Also, the first podcast we put out, which is about Tracy, our traceability app also looks at the business case that surrounds data unions and crowdsourcing of data. So, there’s a few different angles out there if anyone who’s listening wants to hear some more information about that. Thanks for listening. Yeah, hope to see you out there. Hope you subscribe to our future podcasts as well. Thank you.

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