IOBC Capital

In the digital age, data privacy protection is more important than ever. With the rapid development of big data, cloud computing and the Internet of Things, the collection, storage and analysis of personal information have become increasingly common. However, this also brings the risk of data leakage and abuse.

FHE stands for Fully Homomorphic Encryption, which allows calculations to be performed directly on encrypted data, and the calculation results obtained are consistent with the results of the same calculations performed on the original data. This means that we can process and analyze data without exposing the original data, which provides a new solution for protecting data privacy and integrity.

Web3, as a decentralized, open, and transparent new paradigm of the internet, has an inherent synergy with AI. Under the traditional centralized architecture, AI computing and data resources are subject to strict control, facing numerous challenges such as computational bottlenecks, privacy breaches, and algorithmic black boxes. On the other hand, Web3, built on distributed technologies, can infuse new vitality into AI development through shared computing networks, open data markets, and privacy-preserving computation. Simultaneously, AI can empower Web3 with capabilities like optimizing smart contracts and anti-cheating algorithms, aiding in the ecosystem's construction. Therefore, exploring the convergence of Web3 and AI is crucial for building the next-generation internet infrastructure and unlocking the value of data and computing power.
Data-Driven: The Solid Foundation of AI and Web3
Data is the core driving force behind AI development, akin to fuel for an engine. AI models require ingesting vast amounts of high-quality data to gain profound understanding and robust reasoning abilities. Data not only provides the training foundation for machine learning models but also determines their accuracy and reliability.
In the traditional centralized AI data acquisition and utilization model, several key issues arise:
Data acquisition costs are prohibitively high, making it challenging for small and medium-sized enterprises to participate.Data resources are monopolized by tech giants, creating data silos.Personal data privacy faces risks of leakage and misuse.
Web3 offers a new decentralized data paradigm to address the pain points of traditional models:
Through projects like Grass, users can sell their idle network capacity to AI companies, enabling decentralized web data crawling, cleaning, and transformation to provide real, high-quality data for AI model training.Public AI adopts a "label to earn" model, incentivizing global workers with tokens to participate in data annotation, aggregating global expertise, and enhancing data analysis capabilities.Blockchain data transaction platforms like Ocean Protocol and Streamr provide an open and transparent trading environment for data supply and demand, fostering data innovation and sharing.

Nevertheless, real-world data acquisition also faces challenges such as varying data quality, high processing complexity, and insufficient diversity and representativeness. Synthetic data may be the rising star in the Web3 data realm. Based on generative AI technologies and simulations, synthetic data can mimic the properties of real data, effectively complementing it and improving data usage efficiency. In domains like autonomous driving, financial market trading, and game development, synthetic data has already demonstrated its mature application potential.
Privacy Protection: The Role of FHE in Web3
In the data-driven era, privacy protection has become a global focal point, as evidenced by the enactment of regulations like the EU's General Data Protection Regulation (GDPR), reflecting the strict guardianship of personal privacy. However, this has also brought challenges: some sensitive data cannot be fully utilized due to privacy risks, undoubtedly limiting the potential and reasoning capabilities of AI models.

FHE, or Fully Homomorphic Encryption, allows direct computation on encrypted data without the need for decryption, while the computation results are consistent with performing the same operations on plaintext data.
FHE provides robust protection for AI privacy computation, enabling GPU computing power to execute model training and inference tasks without accessing the original data. This presents significant advantages for AI companies, as they can securely open API services while protecting trade secrets.
FHEML supports encrypted processing of data and models throughout the entire machine learning lifecycle, ensuring the security of sensitive information and preventing data leakages. In this way, FHEML reinforces data privacy and provides a secure computing framework for AI applications.
FHEML complements ZKML, where ZKML proves the correct execution of machine learning, while FHEML emphasizes computing on encrypted data to maintain data privacy.
The Computing Revolution: AI Computation in Decentralized Networks
The computational complexity of current AI systems doubles every three months, leading to a surge in computing power demands that far exceeds the supply of existing computing resources. For instance, the training of OpenAI's GPT-3 model required immense computing power, equivalent to 355 years of training time on a single device. Such a shortage of computing power not only limits the progress of AI technology but also renders advanced AI models inaccessible to most researchers and developers.
Additionally, the global GPU utilization rate is below 40%, coupled with the slowdown in microprocessor performance improvements and supply chain and geopolitical factors contributing to chip shortages, exacerbating the computing power supply issue. AI practitioners find themselves in a dilemma: either purchasing hardware or renting cloud resources, desperately needing an on-demand, cost-effective computing service model.
IO.net is a decentralized AI computing power network based on Solana, aggregating idle GPU resources globally to provide AI companies with an economical and accessible computing power market. Entities demanding computing power can publish computation tasks on the network, and smart contracts will allocate tasks to contributing miner nodes. Miners execute the tasks, submit the results, and receive reward points upon successful verification. IO.net's approach improves resource utilization efficiency, helping to alleviate computing power bottlenecks in fields like AI.
In addition to general decentralized computing power networks, there are platforms dedicated to AI training, such as Gensyn and Flock.io, as well as specialized computing power networks focused on AI inference, like Ritual and Fetch.ai.
Decentralized computing power networks provide a fair and transparent computing power market, breaking monopolies, lowering application barriers, and improving utilization efficiency. Within the Web3 ecosystem, decentralized computing power networks will play a crucial role, attracting more innovative dApps and jointly driving the development and application of AI technologies.
DePIN: Web3 Empowering Edge AI
Imagine your smartphone, smartwatch, or even smart home devices possessing the ability to run AI – this is the allure of Edge AI. It enables computation to occur at the data source, realizing low latency and real-time processing while protecting user privacy. Edge AI technology has already been applied in critical domains such as autonomous driving.

In the Web3 realm, we have a more familiar name – DePIN. Web3 emphasizes decentralization and user data sovereignty, and DePIN enhances user privacy protection by processing data locally, reducing the risk of data leakages. Web3's native token economy can incentivize DePIN nodes to provide computing resources, building a sustainable ecosystem.
Currently, DePIN is rapidly developing in the Solana ecosystem, becoming one of the preferred public chain platforms for project deployment. Solana's high throughput, low transaction fees, and technological innovations have provided strong support for DePIN projects. At present, the market capitalization of DePIN projects on Solana exceeds $10 billion, with notable projects like Render Network and Helium Network achieving significant progress.
IMO: A New Paradigm for AI Model Publishing
The concept of IMO (Initial Model Offering) was first introduced by the Ora protocol, tokenizing AI models.
In the traditional model, due to the lack of a revenue-sharing mechanism, once an AI model is developed and released to the market, developers often struggle to obtain continuous revenue from the subsequent use of the model. Especially when the model is integrated into other products and services, it becomes challenging for the original creators to track usage and, consequently, generate revenue. Additionally, the performance and effectiveness of AI models often lack transparency, making it difficult for potential investors and users to evaluate their true value, limiting market acceptance and commercial potential.
IMO provides a novel method of funding and value-sharing for open-source AI models. Investors can purchase IMO tokens to share in the subsequent revenue generated by the model. Ora Protocol utilizes the ERC-7641 and ERC-7007 ERC standards, combined with an Onchain AI Oracle and OPML technology, to ensure the authenticity of AI models and enable token holders to share in the revenue.
The IMO model enhances transparency and trust, encourages open-source collaboration, aligns with crypto market trends, and injects momentum into the sustainable development of AI technology. While IMO is still in its early experimental stage, as market acceptance and participation expand, its innovative nature and potential value are worth anticipating.
AI Agents: A New Era of Interactive Experiences
AI Agents can perceive their environment, engage in independent thinking, and take appropriate actions to achieve predefined goals. With the support of large language models, AI Agents not only understand natural language but can also plan, make decisions, and execute complex tasks. They can function as virtual assistants, learning user preferences through interactions and providing personalized solutions. Even without explicit instructions, AI Agents can autonomously solve problems, enhance efficiency, and create new value.
Myshell is an open AI-native application platform offering a comprehensive and user-friendly toolset for configuring bot functionalities, appearances, voices, and connecting to external knowledge bases. It strives to create a fair and open AI content ecosystem, empowering individuals to become super creators by leveraging generative AI technologies. Myshell has trained specialized large language models to make role-playing more humanized. Its voice cloning technology can accelerate personalized AI product interactions, reducing voice synthesis costs by 99%, with voice cloning achievable in just 1 minute. Customized AI Agents created with Myshell can currently be applied to various domains, including video chatting, language learning, and image generation.

In the convergence of Web3 and AI, the current focus is primarily on exploring the infrastructure layer, addressing critical issues such as acquiring high-quality data, protecting data privacy, hosting models on-chain, improving the efficient utilization of decentralized computing power, and verifying large language models. As these infrastructural components gradually mature, we have reason to believe that the fusion of Web3 and AI will give birth to a series of innovative business models and services.

When Huang Renxun gave a speech at WGS in Dubai, he proposed the term "sovereign AI". So, which sovereign AI can meet the interests and demands of the Crypto community?
Maybe it needs to be built in the form of Web3+AI.
In his article “The promise and challenges of crypto + AI applications”, Vitalik described the synergy between AI and Crypto: Crypto’s decentralization can balance AI’s centralization; AI is opaque, Crypto brings transparency; AI requires data, blockchain facilitates data storage and tracking. This synergy runs through the entire industrial landscape of Web3+AI.

Looking back on the past 2023, the total market value of Crypto has returned to US$1.7 trillion, with an annual growth of more than 110%. Cryptocurrency has passed the cold winter of the cycle.
During this year, the cryptocurrency industry mainly had the following impressive events:
1. Binance reached a settlement with US regulators, and Crypto corporate compliance has gradually become a mainstream trend;
2. The Bitcoin ecosystem leads the new paradigm of Fair Launch, mainly due to the feasibility brought by Taproot upgrade.
3. Ethereum’s LSD/LSDFi defines “risk-free returns” in the cryptocurrency industry, and ETH Staking returns are positioned similar to “Crypto Treasury Bond returns”;

DePIN, Decentralized Physical Infrastructure Networks, decentralized physical infrastructure network.
DePIN is a paradigm innovation in physical infrastructure deployment and maintenance. DePIN is built in a distributed fashion by individuals and companies around the world and can be used by anyone. In return, contributors to these physical infrastructure nodes receive financial compensation and token incentives for the network they are building. By leveraging Crypto, Internet, IoT and blockchain technologies, DePIN enables a more efficient, decentralized and fair way to deploy infrastructure.

The large-scale adoption of a new technology takes a long time. In the United States, it took 78 years for cars to reach 92% penetration, 48 years for household electricity to reach 100% penetration, and 26 years for the Internet to reach 88% penetration.

The time required for large-scale adoption of these technologies is getting shorter and shorter, but why is it that the concept of blockchains such as Bitcoin and Ethereum and Crypto has successfully penetrated into the global public consciousness, but most people have never actually used Crypto? service? There may be five main reasons: first, there are no channels for institutional funds to enter; second, there are no channels for ordinary users to enter; third, there is a lack of investment targets that meet public taste; fourth, it is inconvenient for most developers to enter the industry; fifth, Infra cannot support large-scale application.

Imagine this scenario: If you own 1,000 USDT on the Ethereum mainnet and want to buy $OP on the DEX of the Optimism second-layer network, what should you do?

First, you need to choose a wallet that supports Optimism and add the corresponding network settings; then, through a secure cross-chain bridge, you can bridge 1000 USDT and some ETH used for gas fees to Optimism, and then connect the wallet on the corresponding DEX and Execute the transaction. The entire process, while intuitive, involves multiple interactions and wait times for confirmations, as well as uncertainties such as potential network fees and transaction slippage. For newcomers to on-chain transactions, every step may be a challenge, which also limits the large-scale promotion of on-chain applications. So, can the complexity of on-chain interactions be reduced to the same level of simplicity as centralized exchange (CEX) operations?

In the first half of this year, the innovative narrative of the Bitcoin ecosystem—BRC20, Ordinals NFT, and Bitcoin Layer2—became one of the major topics in the industry. More and more people are paying attention to the development of the Bitcoin ecosystem. In the article "Narrative Innovation: A Brief Analysis of Recent Bitcoin Ecological Experiments", we outline the development status of BRC20, Ordinals NFT and several leading Bitcoin Layer2.

Ideological struggles have broken out in the Bitcoin community more than once around the development path of Bitcoin. In the process of divergent development, the Bitcoin ecosystem has gradually revealed a huge opportunity: decentralized social payment combining Lightning Network and Nostr.

The Bitcoin spot ETF is expected to be approved, triggering heated discussions in the market. According to the timetable of the 21Shares Bitcoin ETF, perhaps the first Bitcoin spot ETF will be born on August 11. However, some people believe that the SEC may be more willing to allow BlackRock’s iShares Bitcoin Trust to become the first approved Bitcoin spot ETF.

1. What is an ETF?

ETF, that is, Exchange-Traded Fund. An exchange-traded fund (ETF) is an investment vehicle that tracks the price of an asset, security, or index. ETFs pool investors' money and aim to achieve the same returns as the underlying assets.

preface:

With the rapid development of digital technology, AI and Crypto have become the two hottest topics. As a technological revolution, AI represents the most advanced productivity; Crypto is based on blockchain technology and represents the fairest production relationship. AI and Crypto are constantly changing the way we live and work. This article will explore the convergence of AI and Crypto and how together they will shape our future.

AI: state-of-the-art productivity

AI (Artificial Intelligence) is a technology that enables computer systems to imitate human intelligence and perform intelligent tasks. It covers several sub-fields including:

Bitcoin’s Sustainability Narrative

The industry has always regarded Bitcoin as "digital gold" and Ethereum as the "world computer". People's optimistic judgments on the long-term rise in Bitcoin prices mainly come from "constant total volume", "value storage", and "cyclical production reduction".

Under this rule, the foreseeable future of Bitcoin is: block rewards are reduced to zero, and on-chain transaction fees will become the only security budget of the Bitcoin blockchain. The block reward (ie Coinbase reward) is halved every four years and will eventually be reduced to zero. This is an established fact in the future that cannot be changed. By then, Bitcoin’s on-chain transaction fees will be the only source of income miners can think of at the moment.

MEV (Miner/Maximum Extractable Value) refers to the potential value that miners or verifiers can obtain in blockchain transactions. It is the profit generated by the transaction sequence and packaging selection method. The sources of MEV include Front Running, Back Running, Sandwich Attack, etc. For more information about the concept of MEV, please read "A Brief Analysis of the Development Status and Trends of MEV".

Flashbots has launched a series of MEV solutions and is committed to establishing a fair, transparent, and secure trading environment. This article will take stock of Flashbots’ efforts to decentralize MEV.