The rise of ChatGPT has been spectacular. Within two months of its launch, the artificial intelligence (AI)-based app reached 100 million unique users. In January 2023 alone, ChatGPT recorded around 590 million views.
Apart from AI, blockchain is another disruptive technology with increasing adoption. Decentralized protocols, applications, and business models have matured and gained ground in the market since Bitcoin (BTC) was published in 2008. Much remains to be done to advance these two technologies, but it will be exciting to see the areas of convergence between the two.
While the hype revolves around AI, a lot happens behind the scenes to create a strong data infrastructure that enables meaningful AI. Poor quality data stored and shared inefficiently would lead to poor insights from the intelligence layer. As a result, it is critical to look at the data value chain holistically to determine what needs to be done for high-quality data and AI applications using blockchain.
The key question is how web3 technologies You can take advantage of artificial intelligence in areas such as data storage, data transfers, and data intelligence. Each of these data capabilities can benefit from decentralized technologies, and companies are focusing on delivering them.
data storage
It helps to understand why decentralized data storage is an essential component for the future of decentralized AI. As blockchain projects scale, every vector of centralization could come to haunt them. A centralized blockchain project could suffer from a governance collapse, regulatory restrictions, or infrastructure issues.
For example, the Ethereum "Merge" network, which moved the chain from proof-of-work to proof-of-stake in September 2022, could have added a centralization vector to the chain. Some have argued that major platforms and exchanges like Lido and Coinbase, which have a large share of the Ethereum betting market, have made the network more centralized.
Another centralization vector for Ethereum is its reliance on Amazon Web Services (AWS) cloud storage. Therefore, storage and processing power for blockchain projects must be decentralized over time to mitigate the risks of a centralized single point of failure. This presents an opportunity for decentralized storage solutions to contribute to the ecosystem, providing scalability and stability.
But how does decentralized storage work?
The principle is to use multiple servers and computers around the world to store a document. Simply, a document can be divided, encrypted and stored on different servers. Only the owner of the document will have the private key to retrieve the data. In retrieval, the algorithm extracts these individual parts to present the document to the user.
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From a security perspective, the private key is the first layer of protection, and distributed storage is the second layer. If a node or server on the network is hacked, you can only access a part of the encrypted data file.
Major projects within the decentralized storage space include Filecoin, Arweave, Crust, Sia, and StorJ.
However, decentralized storage is still in a nascent state. Facebook generates 4 petabytes (4096 terabytes) of data a day, but Arweave has only handled about 122TB of data in total. It costs around $10 to store 1TB of data on AWS, while on Arweave, the cost is around $1350 at press time.
No doubt, decentralized storage has a long way to go, but high-quality data storage can power AI for real-world use cases.
Data transfer
Data transfer is the next key use case in the data stack that can benefit from decentralization. Data transfers using centralized application programming interfaces (APIs) can still enable AI applications. However, adding a centering vector at any point in the data stack would make it less effective.
Once decentralized, the next element in the data value chain is the transfer and exchange of data, mainly through oracles.
Oracles are entities that connect blockchains to external data sources so that smart contracts can connect to real world data and make transaction decisions.
However, oracles are one of the most vulnerable parts of the data architecture, and have been extensively and successfully attacked by hackers over the years. In a recent example, the Bonq protocol suffered a loss of $120 million due to an oracle hack.
In addition to smart contracts and cross-chain bridge hacks, Oracle vulnerabilities have been easy fruit for cybercriminals. This is mainly due to the lack of decentralized data transfer infrastructure and protocols.
Decentralized Oracle Networks (DON) are a potential solution for secure data transfer. DONs have multiple nodes that provide high-quality data and establish end-to-end decentralization.
Oracles have been widely used within the blockchain industry, with different types of oracles contributing to the data transfer mechanism.
There are input, output, cross-chain, and compute-enabled oracles. Each of them has a purpose in the data landscape.
Input oracles transport and validate data from off-chain data sources to a blockchain for use in a smart contract. Exit oracles allow smart contracts to transport data off-chain and trigger certain actions. Cross-chain oracles transport data between two blockchains, which could be critical as blockchain interoperability improves, while compute-enabled oracles use off-chain computing to deliver decentralized services.
While Chainlink has been a pioneer in developing oracle technologies for blockchain data transfer, protocols like Nest and Band also provide decentralized oracles. In addition to purely blockchain-based protocols, platforms like Chain API and CryptoAPI provide APIs for DONs to consume data off-chain securely.
data intelligence
The data intelligence layer is where all infrastructure efforts to store, share, and process data come to fruition. A blockchain-based application that uses AI can still get data from traditional APIs. However, that would add a degree of centralization and could affect the robustness of the final solution.
However, various applications are taking advantage of machine learning and artificial intelligence in crypto and blockchain.
trade and investment
For several years, machine learning and artificial intelligence have been used within fintech to offer robotic advisory capabilities to investors. Web3 has been inspired by these AI applications. The platforms obtain data on market prices, macroeconomic data and alternative data such as social networks, which generates specific information for the user.
The user typically sets their risk and return expectations, and the AI โโplatform's recommendations fall within these parameters. The AI โโplatform obtains the data necessary to provide these insights through oracles.
Bitcoin Loophole and Numerai are examples of this AI use case. Bitcoin Loophole is a trading application that employs artificial intelligence to provide trading signals to users of the platform. He claims to have an over 85% success rate in doing so.
Numerai claims it is on a mission to build "the world's ultimate hedge fund" using blockchain and AI. It uses AI to collect data from different sources to manage an investment portfolio like a hedge fund would.
AI Market
A decentralized AI marketplace thrives on the network effect between developers creating AI solutions at one end and users and organizations employing these solutions at the other end. Due to the decentralized nature of the application, most of the business relationships and transactions between these stakeholders are automated using smart contracts.
Developers can configure the pricing strategy through smart contract inputs. Payment for using your solution could occur per data transaction, data information, or simply a fixed retention fee for the period of use. There could also be hybrid approaches to the pricing plan, with usage being tracked down the chain as the AI โโsolution is used. On-chain activities would trigger smart contract-based payments for using the solution.
SingularityNET and Fetch.ai are two examples of such applications. SingularityNET is a decentralized marketplace for AI tools. Developers create and publish solutions that organizations and other platform participants can use through APIs.
Fetch.ai similarly offers decentralized machine learning solutions to build modular and reusable solutions. Agents build peer-to-peer solutions on this infrastructure. The economic layer across the entire data platform is on a blockchain, enabling usage tracking and smart contract transaction management.
NFT and metaverse intelligence
Another promising use case is non-fungible tokens (NFTs) and metaverses. Since 2021 many Web3 users have seen NFTs as social identities using their NFTs as Twitter profile pictures. Organizations like Yuga Labs have gone a step further, allowing users to log into a metaverse experience using their Bored Ape Yacht Club NFT avatars.
As the narrative of the metaverse increases, so will the use of NFTs as digital avatars. However, digital avatars in today's metaverses are neither intelligent nor anything like the personality the user expects. This is where AI can add value. Smart NFTs are being developed to allow NFT avatars to learn from their users.
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Matrix AI and Althea AI are two companies that develop AI tools to bring intelligence to the avatars of the metaverse. Matrix AI aims to create "avatar intelligence" or AvI. Its technology allows users to create metaverse avatars as close to themselves as possible.
Althea AI is building a decentralized protocol to create intelligent NFTs (iNFTs). These NFTs can learn to respond to simple user signals through machine learning. The iNFTs would become avatars in their metaverse called "Noah's Ark". Developers can use the iNFT protocol to create, train, and earn with their iNFTs.
Several of these AI projects have seen a rise in token prices along with the rise of ChatGPT. However, user adoption is the true litmus test, and only then can we be sure that these platforms solve a real problem for the user. It is still early days for AI and decentralized data projects, but the green shoots have emerged and they look promising.
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