OpenAI’s chatbot ChatGPT reached 100 million monthly active users just two months after its launch, making it the fastest-growing app in history. On January 10, Bloomberg said that Microsoft was considering investing $10 billion in OpenAI, the developer of ChatGPT, and all AI concepts of cryptocurrencies were completely detonated, with FET, AGIX, etc. rising by more than 200% in a month.
With the help of capital, can these two cutting-edge technologies, which have attracted much attention, converge together? Artificial intelligence uses computers to solve problems by mimicking the thinking abilities of the human brain. OpenAI provides a large amount of training data to natural language processing (NLP) models, making them even more powerful. In the crypto world built by blockchain technology, the huge amount of on-chain data every day can provide “fuel” for the AI engine, allowing AIGC to feedback better strategies.
Plus, as AI algorithms become smarter, it’s becoming more difficult for people to understand how they make decisions and conclusions. Blockchain is immutable and can help us access an immutable record of the data and processes used by AI in its decision-making process.
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Crypto project > AI concept (Source: Rootdata)
Compared to artificial intelligence such as Stability AI and ChatGPT, which have gained a lot of attention and adoption in traditional fields, the greater imagination of blockchain lies in the economic system that can change the AI model. When the FOMO sentiment fades, this article will explore the characteristics of crypto projects that introduce AI technology, and what kind of chemical reaction can AI combined with blockchain produce?
AI infrastructure
A common feature of AI infrastructure projects is the distribution and sale of traditional AI architectures (data, models, and computing power). They generally use their own native token as a medium of exchange. They tend to act as intermediaries between users and service providers, building a decentralized trading marketplace. These are all tasks that are intermediate to the needs of traditional AI, such as NLP, AI voice, CV and projects that use DApps as an intermediary platform for transactions. Essentially a decentralized marketplace that uses tokens to price and exchange traditional markets.
Openfabric AI
Openfabric is a platform for building and connecting AI applications. Through the platform, collaboration between AI innovators, data providers, enterprises, and infrastructure providers will facilitate the creation and use of new intelligent algorithms and services. The Openfabric ecosystem consists of 4 roles: algorithm creator, data provider, infrastructure provider, and service consumer, where the service consumer needs to pay the other 3 service providers.
Algorithm creators: Leverage their expertise to create AI algorithms that solve complex business problems. Data providers: Ensure that the large amount of data needed to train AI algorithms is distributed. Infrastructure provider: All the hardware that runs the AI platform. Service Consumer: An end user who needs a specific business product or service.
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Oraichain
Oraichain is an AI-powered blockchain oracle and ecosystem. In addition to data oracles, Oraichain aims to become the blockchain with a complete AI ecosystem that serves as a base layer for creating smart contracts and Dapps. With AI as its cornerstone, Oraichain has developed a number of significant innovative products and services, including AI Price Feed, Full On-Chain VRF, Data Hub, AI Marketplace with over 100 AI APIs, AI-based NFT generation and NFT copyright protection, Royalty Protocol, an AI-powered yield aggregator platform, and Cosmwasm IDE.
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Fetch.ai
Fetch.ai is a blockchain platform based on artificial intelligence and machine learning that allows anyone to share or trade data. As an autonomous machine-to-machine ecosystem, any independent party network can become a network proxy for Fetch.ai, recording any protocols generated between proxies on the Fetch.ai blockchain. FET is the native token of the Fetch AI blockchain and is the primary medium of exchange for payment transactions.
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Source: Fetch.ai Blog
SingularityNET
SingularityNET is a decentralized AI platform and marketplace. Developers publish their services to the SingularityNET network for use by any user with internet access. Developers can charge for their services using the native AGIX token. Services can provide cross-domain inference or model training, such as images, video, speech, text, time series, bio-AI, and network analytics.
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SingularityNET ecosystem
The SingularityNET ecosystem will provide AI services to the platform and create large-scale utilization of the AGIX token. These SingularityNET spin-offs are being developed in multiple strategically selected vertical markets, including DeFi, robotics, biotechnology and longevity, gaming and media, arts and entertainment (music), and enterprise-grade AI.
Gensyn
The Gensyn protocol is a Layer1 network for deep learning computing that rewards supply-side participants who invest computing time in the network and perform ML (machine learning) tasks through instant rewards. The protocol does not require administrative oversight or enforcement, but instead facilitates task assignment and payment programmatically through smart contracts. The fundamental challenge for the network is to validate the ML work that has been done. This is at the intersection of complexity theory, game theory, cryptography, and optimization. The Gensyn ecosystem consists of 4 roles: committer, resolver, validator, and reporter.
Submitters: Provide the tasks that will be calculated and pay for the completed units of work. Solvers: Performs model training and generates proofs for validators to check. Verifiers: The key to linking the non-deterministic training process to deterministic linear calculations, copying a portion of the solver proof, and comparing the distance to the expected threshold. Whistleblowers: Check the work of validators and make challenges in the hope of earning a jackpot.
Gensyn’s vision is to reduce Dapps’ reliance on Web2 infrastructure by providing critical infrastructure components for Web3 applications through decentralized ML computing.
Application Scenarios
In such use cases, the project aims to address the emerging needs arising from the development of blockchain in recent years in an AI manner.
These requirements can be to enable blockchain game users to skip tedious operations, enable developers to quickly develop blockchain games, socialize on blockchain platforms, generate virtual humans with their own personalities, or detect fake NFT projects, etc. Different from traditional AI platforms, such projects have strong irreplaceable demand, which makes them have a deep moat, and at the same time, the development difficulty of platforms with emerging demand as selling points lies in customer acquisition, and how to attract enough customers to prove that the needs of their platforms are sustainable and objective, has become a major problem encountered in the development of such platforms.
Chain Direction
Under the mainstream financial system of the “P2E” model of crypto games, users are faced with ever-changing gameplay and a large number of repetitive basic operations, and AI can provide players with a stable automated process and formulate game strategies with a higher probability of winning. rct AI is a complete solution for the gaming industry using AI, and its core technology, Chaos Box, is an AI engine based on deep reinforcement learning. rct AI has developed an AI-trained DRL (Deep Reinforcement Learning) model for Axie Infinity, which has improved efficiency and win rate in a large number of simulated battle data due to the fact that there are about 10^23 combinations of all cards in Axie Infinity, as well as the characteristics of in-game games.
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In addition, AI can provide action prototypes for developers, Mirror World, a Solana-based game matrix virtual world that has used AI technology to launch Mirrama with roguelike gameplay, and Brawl of Mirrors, a PVP-based arena game. In addition, Mirror World has launched a collection of NFTs that can be interoperable in the game, and the prototypes of these NFTs are done using AI action algorithms.
Related Reading: Talking to RCT AI: It’s Time to Think About How Blockchain Has Changed Game Publishers
Social orientation
PLAI Labs, which is focused on leveraging AI and web3 to build a next-generation social platform for users to play, talk, fight, trade, and adventure together, received $32 million in funding from a16z in January 2023. Currently, PLAI Labs has presented 2 products to the outside world:
Champions Ascension, a massively multiplayer online role-playing game (MMORPG) where players can choose to own their own characters in the form of NFTs and be able to fight in large Colosseum arenas, do quests, build and compete in custom dungeons and trade digital items. An AI protocol platform that will help with everything from user-generated content (UGC) to matching to 2D to 3D asset rendering.
PLAI Labs plans to launch the V2 whitepaper this year, including details of the core economic cycle (leveraging NFTs and blockchain to enhance the experience), UGC toolkit (including AI) plans…
Related reading: “Entrepreneurial Veterans Start Again, Plai Labs Talks Why Web3”
NFT direction
Aletha AI came up with the concept of iNFT, a technology that combines artificial intelligence and blockchain. When integrated with AI, NFTs are interactive, generative, scalable, and unique in their variety of personality traits.
To put it simply, if an NFT is a digital human work, after being integrated with AI, it becomes an iNFT, an NFT work with the ability to chat with users. On June 10, 2021, the world’s first iNFT Alice was auctioned at Sotheby’s for $478,800.
Altered State Machine (ASM) is an innovative project that combines NFTs, artificial intelligence, and machine learning to power the training of AI-powered NFTs, with a vision to become an ownership and monetization protocol for AI using NFT technology. In the ASM ecosystem, AI-based avatars are called agents and are made up of two parts: a brain and an avatar. The project also issued ASTO tokens to power the ASM ecosystem.
Related Reading: Altered State Machine Explained: An Innovative Exploration of Evolving NFTs with AI and Machine Learning
Optic is building an AI-powered NFT verification protocol focused on NFT fraud analysis and NFT value discovery within the community, with the aim of helping the entire NFT market achieve greater authenticity and transparency. The Optic Intelligence Engine learns from real NFT collections and then retrieves NFT collections on the marketplace. Optic then returns a match score that indicates how well the inspected NFT matches the real one.
In July 2022, Optic closed an $11 million funding round led by Pantera Capital, Kleiner Perkins, with participation from Circle Ventures, Polygon Ventures, and others. Currently, OpenSea has adopted Optic’s Copymint detection service.
Related Reading: Optic: An Artificial Intelligence NFT Verification Protocol
Trend Analysis
From the perspective of the current development path of blockchain AI projects, the infrastructure of AI is composed of three parts: data, algorithms, and computing power. In order for a normal AI project to realize the ability to generate or analyze artificial intelligence, it needs to have models and datasets, as well as software ontologies and GUIs that call models. Then the distribution of models and datasets in this field, the training of models (computing power leasing), and the development of software front-end have the formation of intermediaries, which will give birth to blockchain AI projects aimed at efficiently meeting customer needs.
For example, in the above, Fetch.ai acts as an intermediary to allow customers to trade datasets using their native token. SingularityNET allows customers to purchase computing power training services from developers, and Openfabric AI customers need to obtain services such as models (algorithms), datasets, and infrastructure (software) from providers, Humans.ai essentially AI models trained with datasets encapsulated in NFTs, and users use native tokens to purchase.
Gensyn is essentially a decentralized computing power leasing platform. These are all tasks that are intermediate to those that traditional AI needs to complete, such as natural language processing, AI speech, and image generation, and projects that use DApps as an intermediary platform for transactions.
Then the decentralized application in the blockchain has generated new needs, so the AI project based on the direction of chain games, social and NFT aims to solve the pain points of users in the blockchain, such as rct.ai solves the problem of manual repetitive operations of chain game users, Mirror World solves the development of chain games, and other projects are developed for blockchain social and NFT.
Currently, in the early stages of Web3 socialization, AI is being introduced more as a narrative device. In the future, there are some possible directions for AI project development:
Enhance data privacy: Web3 can maximize data privacy through the use of zk technology, while AI can analyze data without compromising it.
Smart contracts: Web3 technology can integrate AI applications into Web3 applications through smart contracts, so as to achieve controllability over AI models. This type of application can be used to trade models and datasets to automate the trading process. And ZK technology is used to protect the user’s data. However, this type of project faces the impact of open-source datasets and open-source models, just imagine: if users can obtain open-source data and models on Hugging face and use auto train training, why will they trade on the blockchain platform? Faced with the onslaught of Web2 companies, Web3 AI models and dataset transactions do not have enough moats.
More efficient machine learning: Web3 technologies can make AI applications faster and more reliable by making machine learning more efficient in a decentralized way. This has been used in traditional AI training, such as KataGo, an improved version of AlphaGo, which uses distributed training technology to enable people around the world who want this AI update to voluntarily provide computing training. The application in the blockchain can be similar to Gitcoin, and the donation of computing power can obtain POAP, or similar to AMM, which provides an incentive for liquidity and becomes a platform for renting computing power for a fee, but due to the high volatility of the currency price, this kind of application does not have an advantage over traditional GPU computing power leasing. Unless the platform itself is engaged in financial business and enough to subsidize users from the value captured by the protocol, such as Numerai, which uses AI technology to profit from the stock market, then enough users are willing to provide the three elements of AI into the platform.
Summary
At present, both blockchain-native AI infrastructure and crypto projects that realize application scenarios with the help of AI engines are in their infancy, and the main goal is to create a suitable underlying infrastructure to hone in the integration of tokenomics with AI solutions such as hardware providers, data providers, and AI algorithms.
However, there are many challenges to the integration of the two. First of all, the trend of blockchain towards complex technologies such as Rollup and ZK will bring challenges to AI to obtain data. Second, there is not enough continuous experimental data to support the applicability of AI in the blockchain ecosystem and the ability of AI engines to adapt in response to emergencies. Finally, there are frequent fake projects in the crypto field that rub on the concept of AI, making it easy for people to lose confidence in exploring the field.
All blockchain AI projects that solve traditional AI problems need to answer the question: why does this platform need to introduce tokens on the blockchain, which makes the transaction target of the existing target in the Web2 market, such as the platform, of model, data and computing power, have the disadvantage of onboarding.
Tokenomics, like a flywheel, can change the rise and fall cycle of a project. At present, if you want a positive flywheel, you need to take into account the actual users of the platform, that is, the problem of customer acquisition. The irreplaceability of demand is the moat of a project, and a project without a moat can achieve short-term success, but it will not have enough users and a strong developer ecosystem. When demand is a false proposition, economic incentives are unsustainable and the life cycle of the project becomes shorter. We expect that there will be more AI+Web3 projects based on real users and irreplaceable needs. They are designed to fulfill requirements that are not or poorly fulfilled in Web2 and thus natively need to be introduced to Web3.
In any case, the integration of AI into Web3 is a future technology trend, and there are already some examples of Web3 applications incorporating artificial intelligence at this stage. Over time, more relevant Web3 infrastructures and new models will emerge.
