原文标题：Case Study for ‘Better’ Sharing Economy (ft. IO.NET)
Original author: Jay
原文来源： Four Pillars
编译：深潮TechFlow
gist
- The concept of sharing economy, which is to lend idle goods or services to people in need in order to optimize the use of resources, has become a consumer culture and economic system, which has attracted much attention in the context of increasingly rational consumption patterns and information technology advances
- However, with the rapid expansion of the sharing economy market and the increase in the number of users, various negative effects have gradually emerged, including the decline in service quality, the monopoly position of early market participants, the strong opposition of existing industry practitioners, and related regulatory issues
- Blockchain technology enables the digitization of a wider range of assets and expands economies of scale, which can serve as an effective foundation for building a sharing economy. IO.NET successfully opened the way to the utilization of idle computing resources, demonstrating its potential in this regard.
1. Every system is about resource optimization
A system is a collection of elements that interact with each other and are where we live. These systems range from life-sustaining biological systems to systems where social groups share common values and identities, and even include the interaction of stars and planets in cosmic systems such as galaxies. Through a systems perspective, we can gain a deeper understanding of our surroundings, analyze and improve these systems, and create a better world.
The core goal of the system is to operate in an economically sustainable and stable manner. Therefore, the most critical aspect of designing a system is to optimize and arrange its components (i.e., resources). For example, in order to optimize the healthcare system, there is a need to effectively deploy medical staff, use electronic medical records (EMRs), and provide telehealth services to ensure that all patients receive timely and appropriate treatment. Such optimization can improve the efficiency of medical services, reduce the cost of treatment, and improve patient satisfaction and medical outcomes.
However, as the needs of various industries become more diverse, the complexity of each system design increases, so system optimization studies are becoming more and more important. This problem is further exacerbated by the asymmetry in the development of various service systems due to limited resources. The recently emerging concept of the sharing economy, which revolves around the idea of lending idle goods or services to those in need, provides important implications for solving the problem of system resource optimization.
Second, the sharing economy promotes resource optimization
2.1 History of the sharing economy
The act of sharing resources, like the large communal ovens in medieval villages in Europe or the "pumasi" culture (shared agricultural labor) in Korea, has existed throughout history. However, the concept has only recently begun to be business-oriented, with the aim of generating profits (i.e., the sharing economy). Nowadays, there are two main reasons why the sharing economy has attracted much attention: one is that the consumption pattern tends to be rational, and the other is the progress of information technology.
Before the Industrial Revolution (i.e., in agrarian societies), demand for goods was generally high, but supply was limited. After the Industrial Revolution (i.e., industrial societies), although supply increased dramatically, it was not always possible to meet all demand, and demand forecasting occasionally went wrong. As we move into a knowledge-based society, production and technology continue to advance, demand and supply become abundant – it's hard to find a person who doesn't have a mobile phone these days, and new models of mobile phones are still being produced and consumed.
However, this pattern of overconsumption and over-production, which has led to a surplus of resources (i.e., inventories and idle resources), has placed a burden on both consumers and producers (i.e., suppliers) since the late 2000s, prompting a global shift to more rational consumption patterns.
Rational consumption patterns include not only consumers making thoughtful decisions about price and quality when purchasing goods, but also the sharing and reuse of excess or surplus resources. Previously, it was difficult to share these remaining resources due to physical constraints. Now, however, advances in information technology have brought advanced digital platforms that facilitate human connection, which has given a huge boost to the emergence of the sharing economy, connecting individuals who pursue rational consumption with individuals who aim to maximize profits.
The sharing economy goes beyond individual resources and encompasses the resources of the entire industry, and is a new economic system and consumer culture that can drive social wealth and economic development in the digital age. Today, a variety of platform services are emerging, and not only individuals, but also start-ups and large enterprises can take advantage of a variety of shared resources such as the required IT technology, data, software, and hardware.
Ride-sharing and car-sharing services
- Uber and Lyft - Connecting drivers with their own vehicles with passengers
- Turo and Getaround - Individuals are allowed to rent out their personal vehicles to others
Accommodation sharing
- Airbnb and Vrbo - Enables homeowners to rent out their homes, apartments, or rooms to guests.
Peer-to-peer lending
- LendingClub and Prosper - Facilitates peer-to-peer lending, allowing individuals to lend idle funds directly to others without going through traditional financial institutions
Freelance and assignment services
- TaskRabbit and Fiverr - Allow individuals to offer or receive services such as furniture assembly, home repairs, and graphic design.
Clothing and accessories rental
- Rent the Runway 和 Poshmark - 便于出租或出借二手服装和配饰
Tool and device sharing
- Fat Llama - Allows individuals to rent out the tools they own to the community
Office space
- WeWork and Regus - Flexible coworking spaces for individuals or companies
2.2 Sharing economy platforms face challenges in sustainability
As mentioned earlier, the concept of the sharing economy has penetrated into all walks of life and is beginning to show significant results, allowing the market to expand rapidly. According to Business Research Insights, the global sharing economy will exceed $1 trillion by 2031 and is on the rise year by year.
However, this assumption is presented in an ideal state where the sharing economy platform is running smoothly. At present, with the rapid development of the sharing economy market and the increase in the number of users, there are some negative effects. These include declining quality of services, the monopoly of early market entrants, and strong opposition and regulatory issues from incumbent industry practitioners.
2.2.1 Incompatibility with existing systems
Sharing economy platforms bring innovation to a stagnant supply-centric market that is widely supported by users because they offer services that are different from traditional companies. However, the new system will always come into conflict with the old one. The sharing economy industry faces significant conflicts, both nationally and internationally, especially with government-related licensing regulations and with incumbents over market share.
Uber, for example, connects users with nearby drivers through a smartphone app, which is seen as disrupting the revenue model of the traditional taxi industry, reducing the value of taxi licenses, and leading to regulatory and legal disputes in many cities. Airbnb allows individuals to rent out their homes or rooms, often offering a cheaper and more exclusive accommodation experience than traditional hotels, thus affecting the demand for hotel rooms, especially in tourist cities, and also triggering problems such as changes in living conditions and rising rents.
Therefore, the conflict with the existing system is a major obstacle to the sustainable development of the sharing economy platform, and it is also a serious challenge that must be addressed. Platforms should spare no effort to work with regulators to develop a reasonable regulatory framework for the existing industry, protect the interests of all stakeholders, and promote fair competition in the market.
2.2.2 Unfair Fee Policy
In addition to the above issues, a structural criticism that the sharing economy industry often faces is that while all sharing economy platforms facilitate effective market exchanges, for-profit businesses that operate these services tend to become more centralized. This means that there is a contradiction between the role of sharing economy platforms in distributing the value generated by transactions between providers and consumers and their role as profit-seeking businesses. This contradiction is most obviously reflected in the abnormal fee policy implemented by monopolistic platforms.
A fair fee structure is essential for the financial sustainability of the platform. For example, charging too high can burden service providers and cause them to leave the platform, while charging too low can hurt the platform's profitability. Therefore, the service platform must determine the charging standard that is acceptable to users and meets industry standards through market research and competitive analysis. In addition, platforms should ensure transparency in the fee structure to build user trust, regularly review the fee policy, and flexibly adjust to market changes based on user feedback.
2.2.3 Economies of scale cannot be achieved
Achieving economies of scale on both the supply and demand sides of a sharing economy platform is particularly important for two reasons. First, it can improve customer satisfaction by reducing the expense of keeping the service running, and second, it ensures service continuity.
Typically, traditional systems anticipate demand for service resources and manage provision, potentially ensuring service continuity. However, on a sharing economy platform, the platform does not control the number of resources provided, and the transactions are almost peer-to-peer, so the entire platform must prepare a large amount of supply capacity in advance to ensure demand and generate a large number of transactions.
Therefore, the platform must not only lower the barriers to entry for demanders, such as simplifying the user interface, providing multiple payment methods, and supporting multiple languages, but also improve the accessibility of services by simplifying the service registration process, introducing training/support programs, and providing tools and resources to help providers effectively manage services and optimize revenue.
2.2.4 Counterparty risk and lack of operational policies for quality control
The growth and scale of sharing economy platforms has attracted users and service providers from different backgrounds and skill levels. As the scope and nature of the resources offered on the platform diversify, maintaining consistency in the quality of service becomes increasingly challenging and has the potential to erode user trust. In order to solve this problem, the platform needs to establish a systematic and sound quality control system.
For example, it may be necessary to conduct a thorough pre-selection and training program for all service providers to establish basic standards for service delivery. In addition, an ongoing feedback and performance monitoring system should be implemented to provide further training or guidance when needed. Alternatively, actively using user feedback to quickly address any deficiencies in service quality is also an effective approach – in fact, many sharing economy platforms currently offer a rating system where providers with a rating below a certain level will be penalized for their service behavior.
2.3 Use blockchain to develop a better sharing economy
However, in reality, it will not be easy for sharing economy platforms that are poised for significant growth to fully meet these challenges. In addition, the belief that these platforms, especially those that are profit-oriented, will not increasingly allocate the value generated by transactions to themselves will make us pay a constant price of uncertainty.
One idea to make the sharing economy more sustainable by structurally weakening the authority of intermediary platforms is to use blockchain. Blockchain, based on its own incentive mechanism, can inspire unexpected spontaneity among a large number of participants, enabling rich resource sharing and enabling service systems to operate in a more transparent and trusted manner.
2.3.1 True P2P resource sharing
As mentioned earlier, one of the goals of profit-seeking sharing economy platforms is to protect the platform profits (i.e., fees) generated from individual transactions. As a result, traditional platforms limit users to the platform, do not disclose contact information, and almost prohibit contact outside the platform. Including these examples, operating a platform involves various management costs in addition to basic infrastructure operation and maintenance costs.
However, the decentralized nature of blockchain networks can facilitate true peer-to-peer transactions without additional management costs and high intermediary fees, and minimize the impact of centralized server failures or attacks.
2.3.2 Ensure abundant idle resources through incentives
The basic principle of system optimization is to maximize the use of idle resources. Often, the vicious circle that hinders the sustainability of the sharing economy system stems from a lack of resource supply. If you build a system using blockchain and design a good incentive mechanism, you can easily attract many providers with idle resources. This provides a buffer to meet the overall demand and greatly improves the efficiency of the sharing economy platform.
Sufficient supply ensures the continuity of services, and since there is no intermediary platform, the cost is also reduced, and the platform is more likely to attract service users. Under these conditions, economies of scale on both the supply and demand sides contribute to the formation of a sustainable sharing economy system, provided that the quality of service is maintained at a certain level.
2.3.3 Use smart contracts to define a code of conduct
Smart contracts on the blockchain automate the execution of specific transaction conditions, making transactions in the sharing economy more efficient and transparent. In the case of a car-sharing service, for example, once the user has finished using the vehicle, the smart contract can automate the payment process and, if necessary, automatically refund the deposit. As a result, smart contracts significantly reduce service management costs, improve user experience, and ensure that all transactions comply with global standards/policies.
Smart contracts can also design appropriate incentives and punishment rules to discourage malicious behavior and promote fair competition within the system. This both motivates users to follow the rules and plays an important role in maintaining the quality and reliability of the entire service system. Essentially, the use of smart contracts allows sharing economy platforms to provide more stable and sustainable services, creating a trading environment that is beneficial to both users and providers.
2.3.4 More trustworthy and open services
Today's sharing economy platforms are challenged to adapt to different regulations and environments across service sectors. Of course, this can be a limitation as many services are still dominated by physical resources such as shared vehicles or space. However, if physical assets can be digitized through blockchain technology, the sharing economy platform of the future can facilitate cross-border transactions of various digital assets. In addition, paying with cryptocurrencies can reduce transaction costs, enable small transactions, and provide low-income earners with the opportunity to rent low-value goods. As a result, these changes could further democratize the sharing economy, enabling transactions of the same value across the globe.
3. Case Study: IO.NET Computing Resource Sharing
As mentioned earlier, most of the existing sharing economy services are still largely confined to physical resources and are highly dependent on local regions, making it difficult to apply the same rules and operate on a cross-border scale. However, digital resources (i.e., digital assets) managed online are less affected by this regional feature and are therefore easier to operate on an international scale.
In this section, we will explore the operation and long-term vision of IO.NET, which aims to democratize computing resources in the context of blockchain. We'll also explore how IO.NET compares to traditional cloud computing platforms like AWS in terms of efficient use of computing resources.
3.1 Computing resource supply and demand challenges
The development of AI technology has greatly increased the demand for high-performance computing resources such as GPUs. According to Precedence Research, the global AI hardware market is expected to grow at a compound annual growth rate (CAGR) of 24.3% to exceed USD 473.53 billion by 2033.
However, despite booming demand and increasingly complex AI/ML models, the global AI hardware market is struggling to keep up with production challenges, which are exacerbated by cross-border political/diplomatic conflicts, leading to a supply crisis. As a result, getting popular computing chips can be costly, have long wait times, and have limited options for renting. This is particularly hindering AI start-ups from expanding the scope and capabilities of their projects.
While AI has the potential to change and improve our lives in a variety of ways, to observe more diverse innovations, many AI startups need to be backed by cost-effective, scalable computing resources. If these computing resources are not addressed, the AI industry may face the danger of stagnation in various innovation efforts, and the asymmetry in the development of the AI market may lead to significant societal impacts.
3.2 IO.NET Solutions
3.2.1 Overview of IO.NET
At the end of the day, the key question is how to achieve an adequate supply of computing resources at the same time that is cost-effective and quality-comprehensible. IO.NET is a Solana-based coordination layer that solves this problem by integrating idle computing resources from data centers, miners, crypto projects (e.g., Render Network, Filecoin), and consumers – providers of computing power are rewarded with tokens, which users use to economically provision various types of idle computing resources for their GPU clusters*.
*Currently, prior to the launch of $IO tokens, payment methods are available in fiat currencies and USDC, but in the future, other network tokens, including $IO tokens, will be supported, and non-$IO tokens will be subject to a 2% fee.
IO.NET leverage clustering frameworks such as Ray and Kubernetes*. Users can configure clusters on IO Cloud and process workloads in parallel by setting processor type, location, communication speed, security compliance level, and duration. Clusters configured through IO.NET can be used for general computing purposes, but are primarily optimized for AI/ML development tasks such as batch inference, model serving, parallel training, parallel hyperparameter tuning, and reinforcement learning.
IO.NET has partnerships with the Render Network and Filecoin's mining networks, and users can benefit from both networks as well as IO.NET as a cluster provider. Supported processors include a variety of GPU and CPU options, such as the NVIDIA RTX series, AMD Ryzen series, and Apple's M-series, providing users with a high degree of flexibility in configuring processors and the ability to organically adjust the number of GPUs in a cluster as demand changes.
*Frameworks for a variety of purposes, such as Ludwig, PyTorch, Unreal Engine 5, Unity Streaming, and more, will also be supported soon
At the time of writing (April 21, 2024), users can configure clusters in approximately 140 countries, with a total of nearly 970,000 GPUs available on the platform. However, the actual number of users using GPUs through the (nascent) IO.NET platform is only 15,000. IO.NET the actual number of users using GPUs is still small. Most GPUs have 0% workload, while popular processors tend to have a limited initial supply or are already used to more than 90% capacity (e.g., A100 PCIe 80 GB K8S (NVIDIA), H100 80GB HBM3 (NVIDIA), RTX A5000 (NVIDIA)). According to IO Explorer, about 6,270 clusters have been created to date, with payments amounting to about $910,000.
In fact IO.NET isn't the only project in the Web3 market that offers computing resources, there are various other leading projects in this space, including Akash, Render Network, and Filecoin. However, as mentioned earlier, IO.NET's competitive advantage lies in providing dedicated computing power for AI/ML and introducing clustering instead of single instancing, which allows for dynamic allocation of compute resources among connected GPUs and optimizes the distribution of workloads.
To become a GPU vendor (i.e., IO Worker) and generate revenue, users can first add new devices in the Worker tab. After filling in the details of the operating system and devices that the user wishes to provide, the process continues, as shown in the following image, where the user can manage multiple devices and view the status of earnings on the Worker tab.
3.2.2 Structure of the IO.NET
IO.NETIO.NET uses a modular architecture consisting of multiple layers, each optimized to perform its unique function. In addition to each layer of the technology stack listed below, IO.NET incorporate reverse tunneling and mesh VPN networking, enabling engineers to securely access and smoothly control data.
- User interface layer The gateway to IO.NET service as a gateway for user access. (e.g., ReactJS, Tailwind, web3.js, zustand)
- Layers of security ensure the integrity and security of the system. (e.g., firewall (pfSense, iptables), authentication (OAuth, JWT), log service (ELK Stack, Graylog)
- API 层--充当通信枢纽的中间件层。 (例如,FastAPI、Python、GraphQL、RESTful API、gunicorn、solana)
- Backend layer – management vendor (worker), cluster/GPU run, customer interaction, monitoring, etc. (e.g., FastAPI, Python, Node.js, Flask, solana, IO-SDK (fork of Ray 2.3.0), Pandas)
- The database layer manages the primary storage of structured data and the caching of transient data. (e.g., Postgres (primary storage), Redis (cache)
- The task layer coordinates asynchronous communication and task management to ensure an efficient data flow. (e.g. RabbitMQ (message broker), Celery (task management)
- 基础层 管理 GPU 池、部署、计算和机器学习任务。 (例如,协调(Kubernetes、Prefect、Apache Airflow)、执行/ML(Ray、Ludwig、Pytorch、Hard、TensorFlow、Pandas)、监控(Grafana、Datadog、Prometheus、英伟达 DCGM)等)
3.2.3 Tokens for the $IO and IOG networks will be released soon
IOG Network & Ecosystem
IO.NET's long-term vision is to create a computational operational currency (i.e., $IO tokens) and build an ecosystem on top of that. As a result, the $IO token, designed according to the Solana Token Standard (SPL), will be launched around Q2, while the "IOG Network", also based on Aptos and Solana, is also under development. The IOG network will be equipped with a full suite of services for building, training, and deploying different machine learning models. IO.NET kits will also be connected to the network. In addition, a universal ID (IO ID) for the IO ecosystem and an open-source SDK for developers to deploy their services are also under development.
The operation of the IOG network will be managed by nodes that provide on-demand computing resources. These nodes must stake at least $100 of IO in order to earn a running reward – availability rewards (i.e., idle rewards). Like other Delegated Proof-of-Stake (DPoS) protocols, $IO token holders can also pre-set a maximum stake on each node (i.e., the maximum stake per node) and ensure the fidelity of the network in exchange for rewards (excluding the 5% node fee).
Per Node Max Stake = Device Max Stake x (2 + 3 x [SUM of Modifier Options])
Maximum stake per node = max stake x (2 + 3 x [sum of modifiers])
*Once staked, it takes 7 days for $IO to unstake, and $IO cannot unstake while the node is leased.
Demand for $IO tokens and vendor rewards
$IO tokens can be used not only to reserve GPU clusters through IO.NET, but also to deploy various types of applications (or instances) hosted on an IOG network using the SDK, as well as to use multiple inference models. When booking or renting a GPU, the network charges a 0.25% fee. After a review of the performance and uptime of the reserved nodes based on the cut conditions, a reward will be paid to the vendor – known in the IOG network as a "lease fee or hire rate". If a vendor receives a deposit from a $IO token holder, 1% of that reward will be returned to the given deposit holder.
Total Hire Rate=Current # of Cards on the Worker x Current Card Price x Computation Hour Reserved x (1 + SUM of Modifier Options) x 99% Supplier Share*
Total Hire Rate = Current Number of Work Cards x Current Card Price x Calculated Hours Retained x (1 + Sum of Modifier Options) x 99% Vendor Share *
*Modifier options are items (such as bandwidth, location, GPU interconnect options, node disk properties) that IO.NET identify as additional services and hardware that the provider deploys to improve the performance of the entire cluster.
The previously mentioned "availability reward (hourly)" is a separate node running reward introduced in addition to the "rental fee" to encourage sufficient supply on the network. These rewards are calculated on a per-node basis and take into account various factors such as bandwidth, percentage of uptime, type of hardware, and more. As with the "Rental Fee", if a vendor receives a deposit from a $IO token holder, then 5% of these rewards will be returned to the given deposit holder. In addition, to prevent nodes from constantly connecting and disconnecting, there is a 12-hour cooldown period to activate availability rewards every time a node is shut down or reconnected after being suspended.
Approximate Per Node Hourly Availability Rewards = Staker Collateral Multipler x (Hardware CapEx / Hours in 18 Months) Uptime Percentage x 95% Supplier Share
Hourly availability reward per node = staker additional multiplier x (hardware capital expenditure/hours in 18 months) uptime percentage x 95% vendor share
$IO Token Distribution Plan
$IO has a total fixed supply of 800 million, of which 500 million are initially used for circulation and the remaining 300 million are distributed to suppliers and subscribers (i.e., community rewards). These community rewards start at 8% in the first year and decrease by approximately 1.02% per month (approximately 12% per year) until they reach a total of $800 million in IO tokens, using a deflationary model.
As mentioned earlier, the IOG network generates revenue by charging booking and rental fees (i.e., 0.25%) as well as payment and withdrawal fees (i.e., 2% when withdrawing in USDC). The network then determines the amount of $IO to burn based on the price of $IO and uses the revenue to buy and burn $IO tokens.
3.3 Impact and Benefits of Cloud Services
First, one of the most obvious advantages of IO.NET over traditional cloud services is that anyone, regardless of technical expertise, can easily share their idle computing resources and earn rewards with just a few clicks. This greatly lowers the barrier to entry, meaning that individuals can effortlessly share idle resources as long as the rewards are properly distributed, and it also facilitates the easy launch of abundant computing resources from the supply side. As a result, users can quickly set up clusters with the GPUs they need without having to commit to long-term contracts or endure the long wait times common with traditional cloud services.
In addition, IO.NET offers a relatively cheaper and more flexible development setup. The resources shared on IO.NET are mostly individual idle computing resources, which means they don't incur the same high maintenance costs as large data centers, reducing the marginal cost to the vendor. In addition, the diversity of vendors ensures a wide choice of computing resources and eliminates the risk of system downtime. IO.NET further supports a variety of frameworks with IO Cloud, increasing the development efficiency of users and going beyond the general possibilities of cloud services.
The third advantage is the thriving ecosystem of the IOG network, which is aided by the widespread demand for $IO tokens. As mentioned earlier, $IO tokens can be used not only to book computing resources, but also to build and use various applications or instances hosted on the IOG network. The massive demand for the token increases the demand for the supply of $IO tokens, which in turn facilitates the liquidity of the abundant computing resources within the ecosystem.
In addition, the success of the IO.NET has enabled small investors to actively participate in the computing resources market. To date, the allocation of computing resources has been monopolized by a handful of large companies such as AWS, GCP, and Azure. However, IO.NET is a good option for small investors who consider computing resources as investment assets, IO.NET provide a marketplace where anyone can easily buy and trade idle computing resources. If one of the core goals of finance is democratization, then IO.NET is a prime example of achieving this financial inclusion.
In addition, if large-scale idle computing resources can be effectively managed, their utilization rate will also increase, thereby contributing to a green environment.
3.4 Challenges and Reflections
As has been repeatedly emphasized, the premise of the positive outlook for a blockchain-based sharing economy system IO.NET is that the incentive mechanism is properly designed to achieve economies of scale. In other words, if these mechanisms are not properly designed, or if the delicate balance between initial supply guidance or supply and demand is not well managed, it can lead to a vicious cycle of token value, resource supply, and service quality collapsing at the same time.
Therefore, system designers must not only ensure that the current level of resource supply is adequate, but also be vigilant to prevent oversupply. If there are more suppliers active on the network than there is demand, it could lead to a significant dilution of the token's value in the short to medium term, which could lead to a large outflow of suppliers or increased volatility in the supply of resources.
Similarly, it's crucial to ensure that incentives for suppliers aren't exorbitant. Excessively generous incentives may encourage potential suppliers to buy additional resources beyond idle assets simply for higher profits, which can upset the supply-demand balance of the entire computing resources market and potentially lead to market failure.
Finally, excessively expanding access to financial resources can encourage speculation and obscure the original purpose of demand. Therefore, it is also crucial to ensure that token liquidity is primarily circulated among actual users.
4. Looking forward to another "Uberization"
In the concept of blockchain, a large number of unspecified individuals gather around a P2P network centered on valuable assets, which coincides with the concept pursued by the sharing economy. Or, if we assume that there is more value that can be reliably digitized on the blockchain in the future, then we might as well assume that the future of the sharing economy is not in the physical realm, but in the blockchain space.
IO.NET is the best example of this potential. It has succeeded in launching a valuable digital commons resource that many people can take advantage of. As mentioned in the article, it will be crucial to maintain a delicate balance between token demand and supply in order to consistently provide high-quality, abundant resources on the platform.
In the face of the ongoing uncertainty of the global economy and the persistence of value-based consumption patterns, resource optimization has become an inevitable task. To effectively scale the sharing economy approach to this area, it is essential to build a larger economy around a wider range of assets.
Digital resources can transcend physical limitations and connect with people on the other side of the globe in an instant. Blockchain technology has the ability to liquidate the value of various assets, bringing together many people in this value flow to build an efficient economic system. Starting with the example of IO.NET, we hope to soon begin experimenting with optimizations for a wider range of resources on the blockchain, potentially bringing innovation similar to another Uberization into our lives.