Author: MIIX Capital Source: medium

1. Project Status

1.1 Business Overview

io.net is a decentralized GPU network designed to provide computing for ML (machine learning). Computing power is obtained by assembling more than 1 million GPUs from independent data centers, cryptocurrency miners, and projects such as Filecoin or Render.

Its goal is to combine 1 million GPUs into DePIN (Decentralized Physical Infrastructure Network) to create an enterprise-level, decentralized distributed computing network, and provide AI engineers with lower-priced, more accessible, and more flexible network computing resource services by aggregating idle network computing resources around the world (currently mainly GPUs).

For users, it is equivalent to a decentralized global idle GPU resource bazaar, where AI engineers or teams can customize and purchase the required GPU computing services according to their needs.

1.2 Team Background

Ahmad Shadid is the founder and CEO, and was previously a quantitative system engineer at WhalesTrader.

Garrison Yang is the Chief Strategy Officer and Chief Marketing Officer, and was previously the Vice President of Growth and Strategy at Ava Labs.

Tory Green is the COO, previously the COO of Hum Capital and the Director of Corporate Development and Strategy of Fox Mobile Group.

Angela Yi is the Vice President of Business Development, a graduate of Harvard University in the United States, responsible for planning and executing key strategies such as sales, partnerships and supplier management.

In 2020, when Ahmad Shadid built a GPU computing network for Dark Tick, a machine learning quantitative trading company, because the trading strategy was close to high-frequency trading, a large amount of computing power was required, and the high GPU service fees of cloud service providers became their problem.

The huge demand for computing power and the high costs they faced prompted them to decide to do decentralized distributed computing resources, and then gained attention at the Austin Solana Hacker House. Therefore, io.net belongs to the team that starts from the pain points they face, proposes solutions, and implements and expands their business.

1.3 Products/Technology

Problems faced by market users:

Limited availability, accessing hardware using cloud services such as AWS, GCP, or Azure usually takes weeks, and popular GPU models on the market are usually not available.

There are few options, such as GPU hardware, location, security level, latency, etc., where users have almost no choice.

High cost: It is very expensive to obtain high-quality GPUs, and it costs hundreds of thousands of dollars per month for training and inference.

Solution:

By aggregating underutilized GPUs (such as independent data centers, crypto miners, and crypto projects such as Filecoin and Render), these resources are integrated into DePIN, enabling engineers to gain massive computing power in the system. It allows ML teams to build inference and model serving workflows across distributed GPU networks and leverage distributed computing libraries to orchestrate and batch training jobs so that they can be parallelized across many distributed devices using data and model parallelism.

In addition, io.net leverages a distributed computing library with advanced hyperparameter tuning to check for optimal results, optimize scheduling, and simply specify search patterns. It also uses an open source reinforcement learning library that supports production-grade, highly distributed RL (reinforcement learning) workloads with a simple API.

Product composition:

IO Cloud, which aims to deploy and manage decentralized GPU clusters allocated on demand, seamlessly integrates with IO-SDK, and provides a comprehensive solution for expanding artificial intelligence and Python applications. It can provide unlimited computing power while simplifying the deployment and management of GPU/CPU resources.

IO Worker, provides users with a comprehensive and user-friendly interface to efficiently manage their GPU node operations through intuitive network applications. The scope of the product includes functions related to user account management, computing activity monitoring, real-time data display, temperature and power consumption tracking, installation assistance, wallet management, security measures and profitability calculation.

IO Explorer, mainly provides users with comprehensive statistics and visualizations of various aspects of the GPU cloud, allowing users to easily and instantly monitor, analyze and understand the complex details of the io.net network, providing comprehensive visibility into network activities, important statistics, data points and reward transactions.

Product Features:

Decentralized computing network: io.net adopts a decentralized computing model to distribute computing resources around the world, thereby improving computing efficiency and stability.

Low-cost access: Compared with traditional centralized services, io.net Cloud provides lower access costs, enabling more machine learning engineers and researchers to obtain computing resources.

Distributed cloud cluster: The platform provides a distributed cloud cluster where users can choose appropriate computing resources according to their needs and assign tasks to different nodes for processing.

Support for machine learning tasks: io.net Cloud focuses on providing computing resources for machine learning engineers, enabling them to more easily perform tasks such as model training and data processing.

1.4 Development Roadmap

https://developers.io.net/docs/product-timeline

According to the information published in the io.net white paper, the project product roadmap is: January-April 2024, V1.0 will be fully released, dedicated to the decentralization of the io.net ecosystem, enabling it to achieve self-hosting and self-replication.

1.5 Financing Information

According to public news information, on March 5, 2024, io.net announced the completion of a $30 million Series A financing, led by Hack VC, with participation from Multicoin Capital, 6th Man Ventures, M13, Delphi Digital, Solana Labs, Aptos Labs, Foresight Ventures, Longhash, SevenX, ArkStream, Animoca Brands, Continue Capital, MH Ventures, Sandbox Games, etc. [1] It is worth noting that after this round of financing, io.net's overall valuation is $1 billion.

2. Market Data

2.1 Official Website

From January 2024 to According to the official website data in March 2024, the total number of visits was 5.212M, the average monthly visit was 1.737M, the bounce rate was 18.61% (low), the user access data in each region was relatively uniform, and direct visits and search visits accounted for more than 80%, which may indicate that the proportion of dirty data in the visiting user data is not high. They have a basic understanding of io.net and are willing to learn more about and interact with the website.

2.2 Social Media Community

3. Competition Analysis

3.1 Competition Landscape

io.net's core business is related to decentralized AI computing power. Its biggest competitors are traditional cloud service providers represented by AWS, Google Cloud, and Microsoft Intelligent Cloud Business (represented by Azure). According to the "2022-2023 Global Computing Power Index Assessment Report" jointly compiled by International Data Corporation (IDC), Inspur Information and Tsinghua University Global Industry Research Institute, the global artificial intelligence computing market size is expected to grow from US$19.5 billion in 2022 to US$34.66 billion in 2026. 【2】

Compared with the sales revenue of the world's mainstream cloud computing vendors: in 2023, AWS cloud service sales revenue will be US$9.08 billion, Google Cloud sales revenue will be US$3.37 billion, and Microsoft Intelligent Cloud business sales revenue will be US$9.68 billion. 【3】The three companies account for about 66% of the global market share, and the market value of these three giant companies is over one trillion US dollars.

https://www.alluxio.io/blog/maximize-gpu-utilization-for-model-training/

In sharp contrast to the high revenue of cloud service vendors, how to improve GPU utilization has become a focus issue. According to a survey on AI infrastructure, most GPU resources are underestimated - about 53% of people believe that 51-70% of GPU resources are underestimated, 25% believe that the utilization rate reaches 85%, and only 7% believe that the utilization rate exceeds 85%. For io.net, the huge demand for cloud computing and the problem of insufficient effective utilization of GPU resources are market opportunities.

3.2 Advantage Analysis

https://twitter.com/eli5_defi/status/1768261383576289429

io.net's biggest competitive advantage is reflected in its niche advantage or first-mover advantage. According to official data: io.net currently has more than 40K GPU clusters, more than 5600 CPUs, more than 69K Woker Nodes, and takes less than 90 seconds to deploy 10,000 GPUs. The price is 90% cheaper than competitors, and the valuation is $1 billion. io.net not only provides customers with a low price of 1-20% compared to centralized cloud service providers and instant online services without permission, but also provides additional startup incentives for computing power providers through the upcoming IO tokens, jointly helping to achieve the goal of connecting 1 million GPUs.

In addition, compared with other DePIN computing projects, io.net focuses on GPU computing power, and the scale of its GPU network is more than 100 times ahead of similar projects. io.net is also the first in the blockchain industry to integrate the most advanced ML technology stack (such as Ray clusters, Kubernetes clusters, and giant clusters) into GPU DePIN projects and put them into large-scale practice, which makes it not only in the number of GPUs, but also in the ability of technology application and model training. In a leading position.

As io.net continues to develop, if it can increase its GPU capacity to 500,000 concurrent GPUs across the entire network to compete with centralized cloud service providers, it will be able to provide services similar to Web 2 at a lower cost, and have the opportunity to gradually establish its core position in this field through close cooperation with major DePIN and AI players (including Render Network, Filecoin, Solana, Ritual, etc.), becoming the leader and settlement layer of the decentralized GPU network, and bringing vitality to the entire Web 3xAI ecosystem.

3.3 Risks and Issues

io.net is an emerging computing resource integration and distribution platform that is deeply integrated with Web3, and the businesses involved are highly overlapped with traditional cloud service providers, which makes it face risks and obstacles in both technology and market.

Technical security risks, io.net as an emerging platform, has not undergone large-scale application testing, nor has it demonstrated the ability to prevent and respond to malicious attacks. In the face of a huge amount of computing power resources, there is no corresponding experience or practical verification for access, distribution and management, which is prone to common compatibility, robustness, security and other problems of technical products. And once a problem occurs, it is likely to be fatal to io.net, because customers are more concerned about their own security and stability, and are unwilling to pay for them.

Slow market expansion, io.net has a high degree of overlap with traditional cloud service providers, which requires it to compete directly with traditional AWS, Google Cloud, Alicloud, etc., and even with second-tier or third-tier service providers. Although io.net has a more favorable cost, its service system and market system for Class B customers are just beginning, which is very different from the existing market operation of the Web3 industry. Therefore, at present, its progress in market expansion is not ideal, which is likely to directly affect its project valuation and the market value performance of tokens.

Latest Security Incidents

On April 25, Ahmad Shadid, founder and CEO of io.net, tweeted that the io.net metadata API suffered a security incident. Attackers used the accessible mapping of user IDs to device IDs to cause unauthorized metadata updates. This vulnerability did not affect GPU access, but it did affect the metadata displayed to users by the front end. io.net does not collect any PII and does not leak sensitive user or device data.

Shadid said that the io.net system design allows for self-healing, constantly updating each device to help recover any erroneously changed metadata. In light of this incident, io.net has accelerated the deployment of OKTA's user-level authentication integration, which will be completed within the next 6 hours. In addition, io.net has also launched Auth0 Token for user verification to prevent unauthorized metadata changes. During database recovery, users will be temporarily unable to log in. All uptime records are unaffected, and this does not affect the computing rewards of suppliers.

4. Token Valuation

4.1 Token Model

io.net Token Economic Model will have an initial supply of 500 million IO at Genesis, divided into five categories: Seed Investors (12.5%), Series A Investors (10.2%), Core Contributors (11.3%), R&D and Ecosystem (16%), and Community (50%). As IO is issued to incentivize network growth and adoption, it will grow to a fixed maximum supply of 800 million in 20 years.

The rewards adopt a deflationary model, starting from 8% in the first year, and decreasing by 1.02% per month (about 12% per year) until the 800 million IO cap is reached. As rewards are distributed, the share of early supporters and core contributors will continue to decrease, and the community's share will grow to 50% after all rewards are distributed. [4]

The functions of its tokens include giving IO Worker allocation incentives, rewarding AI and ML deployment teams for continued use of the network, balancing some demand and supply, pricing IO Worker computing units, and community governance.

In order to avoid payment problems caused by IO coin price fluctuations, io.net has developed a stablecoin IOSD, which is pegged to the US dollar. 1IOSD is always equal to 1 US dollar. IOSD can only be obtained by destroying IO. In addition, io.net is considering some mechanisms to improve network functionality. For example, it may allow IO Workers to increase the probability of being rented by pledging native assets. In this case, the more assets they stake, the greater their probability of being selected. In addition, AI engineers who stake native assets can use GPUs with high demand first.

4.2 Token Mechanism

IO tokens are mainly used by two groups: demand side and supply side. For the demand side, each computing job is priced in US dollars, and the network will retain the payment until the job is completed. Once the node operator configures its reward share in US dollars and tokens, all US dollar amounts will be directly allocated to the node operator, and the share allocated to the token will be used to burn IO coins. Then, all IO coins minted as computing rewards during the period will be distributed to users according to the US dollar value of their coupon tokens (computing points).

For the supply side, it includes availability rewards and computing rewards. Among them, the computing reward is for jobs submitted to the network. Users can choose the time preference "duration of cluster deployment in hours" and receive cost estimates from the io.net pricing oracle. In terms of availability rewards, the network will randomly submit small test jobs to evaluate which nodes run regularly and can accept jobs from demanders well.

It is worth mentioning that both the supply side and the demand side have a reputation system to accumulate scores based on computing performance and network participation to obtain rewards or discounts.

In addition, io.net also has an ecological growth mechanism, including staking, invitation rewards and network fees. IO coin holders can choose to stake their tokens IO to node operators or users. Once staked, the staker will receive 1–3% of all rewards received by the participant. Users can also invite new network participants to join and share part of the new participants' future income. The network fee is set to charge 5%.

4.3 Valuation Analysis

We are currently unable to obtain accurate revenue data for projects in this track, so we are unable to make an accurate valuation. Here we mainly compare it with Render, which is also an AI+DePIN project of io.net, for your reference.

https://x.com/ionet/status/1777397552591294797

https://globalcoinresearch.com/2023/04/26/render-network-scaling-rendering-for-the-future/

As shown in the figure, Render Network is currently the leading project in the decentralized GPU rendering solution in the AI+Web3 track, with a total GPU resource of 11,946 and a current market value of $3 billion (FDV of $5 billion); while io.net has a total GPU resource of 461,772, which is 38 times that of Render, and is currently valued at $1 billion. For both io.net and Render projects, the core key capabilities of both are decentralized GPU computing power. Therefore, from the perspective of GPU supply as the core comparison dimension, io.net's market value after listing is likely to exceed that of render, or at least be comparable.

https://stats.renderfoundation.com/

Render network's Frames Rendered in 2022 is 9,420,335, and GMV is US$2,457,134. At present, Render Network's Frames Rendered is 31,643,819, from which it is estimated that the entire GMV is approximately US$8,253,751.

Compared to io.net's 4-month GMV of 400,000, assuming that io.net grows at an average rate of 400,000 GMV in 4 months, and 12-month GMV is 1,200,000, if io.net wants to reach the current GMV of Render Network, there is still 6.8 times of growth space. Now io.net is valued at 1 billion US dollars. Based on the above analysis, io.net's market value is expected to reach more than 5 billion US dollars in the bull market cycle.

5. Summary

The emergence of io.net fills the gap in the field of decentralized computing and provides users with a novel and potential computing method. With the continuous development of fields such as artificial intelligence and machine learning, the demand for computing resources is also increasing, so io.net has high market potential and value.

On the other hand, although the market has given io.net a high valuation of $1 billion, its products have not been tested by the market, and there are uncertain risks in terms of technology. Whether it can effectively match its supply and demand relationship is also a key variable that determines whether its subsequent market value can reach a new high. From the current situation, the results of the io.net platform on the supply side have already begun to show, but it has not yet fully exerted its strength on the demand side, resulting in the current platform's overall GPU resources not being fully utilized. How to more effectively mobilize the demand for GPU resources is a challenge that the team has to face.

If io.net can quickly access market demand and does not encounter or encounter major risks and technical problems during the operation process, with its AI+DePIN entity business attributes, its overall business will start the growth flywheel and become the most dazzling project product in the Web3 field. This also means that io.net will be a high-quality investment target for the branch. Let us continue to follow up and observe and verify carefully.