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Nov 8, 2024
Crypto and Artificial Intelligence (AI) are two of the most impactful technologies of this era. Together, they are highly complementary.
Blockchain technology benefits AI by providing a foundation for decentralized AI, which is a core pillar of ours at 0G Labs. For example, developers can be incentivized to collaborate globally via fully transparent AI infrastructure that has enforced data rights, fair monetization, smart contract automation, and much more.
Meanwhile, AI benefits blockchain technology by optimizing on-chain operations, such as intelligent smart contract execution, liquidity optimization, and AI-driven governance decisions. It can also provide better data-driven insights, improve on-chain security, and lay the foundation for new Web3-based applications.
In this article, we will provide an overview of 4 of the fastest-moving areas of Crypto AI that anyone interested in the space should be aware of:
Decentralized Compute: Crowdsourced computing for AI models.
AI Agents: Automating complex on-chain operations.
Verifiable Inference: Trustless verification for off-chain AI computations.
Data Infrastructure: New methods for the permissionless trading and sharing of valuable datasets.
Below, we’ll explore each of the key areas, addressing the current challenges facing the industry, such as scalability, data availability, and infrastructure gaps. Additionally, we'll outline how 0G is tackling these issues with its decentralized AI infrastructure, providing solutions that enhance performance, accessibility, and transparency. Finally, we'll highlight how these advancements position 0G as a leader in the evolution of on-chain AI.
Decentralized Compute
AI relies on extensive datasets and substantial computational resources to develop models that deliver actionable insights. Traditionally, GPUs (graphics processing units) have been the go-to for these tasks, but they are expensive to purchase, deploy, and maintain. Instead, they are typically rented from centralized providers like Amazon Web Services or Google Cloud.
Decentralized Compute distributes this computing power globally. For example, platforms like Akash and Render allow individuals or entities with idle resources (such as GPUs or other forms of specialized hardware) to contribute their computing power in a permissionless, decentralized manner. This means that anyone can instantly run AI models using computing resources from any entity globally via an online marketplace for buyers & sellers that’s similar to Uber or Airbnb.
The main benefits include:
Permissionless access: Users can access computing power without relying on gatekeepers.
Increased resource availability: Decentralized networks provide access to niche or rare computing resources that centralized providers may restrict.
Avoiding data lock-ins: No restrictive contracts or hefty transfer fees when switching providers, ensuring user autonomy.
Lower costs: Decentralization reduces overhead, making it more cost-efficient compared to traditional cloud services.
That being said, Akash has ~700 active GPU leases at present, whereas centralized entities have millions. It is a major Crypto AI use case but it will take time for adoption to grow to rival Web2’s metrics.
AI Agents
Web3 is becoming increasingly complex and it will be impossible for humans to handle tasks like liquidity optimization, complex workflows, AI-driven governance, and more.
AI Agents are on-chain computer programs that function autonomously, handling complex tasks related to decision-making, planning, and task execution. On the backend, they tend to leverage Large Language Models (LLMs) like Chat-GPT or Llama 3 to execute intelligent custom logic based on one’s needs, though some projects like Assisterr (a partner of 0G) are focused on Small Language Models (SLMs) or other AI model variations.
The main benefits include:
Automation: Advanced processes such as liquidity management and governance decisions can be automated with complex AI logic built in, reducing the need for human intervention.
User Experience: AI agents can act as user interfaces, translating user intent into precise actions and also guiding users through complex transactions, explaining risks, and providing optimal strategies.
Scalability & Continuous Learning: As the Web3 ecosystem expands, AI agents can easily scale and will improve over time by ingesting more data, including niche datasets.
At 0G Labs, we expect exponential growth in this sector and believe that institutions will increasingly invest in their own proprietary AI agents that handle various on-chain tasks.
0G is already working with Talus, Theoriq, and more to bring more AI Agents on-chain.
Verifiable Inference
Due to the on-chain gas fees and the challenge in storing and processing vast amounts of data on-chain, AI computations primarily occur off-chain at present.
Verifiable inference allows AI model computations to be permissionlessly validated on-chain using privacy-preserving techniques like zkML, which uses zero-knowledge proofs. This entails cryptographically proving on-chain that the off-chain computations were done correctly (e.g. without the dataset being tampered with) while keeping all data confidential.
Although 0G’s decentralized AI OS infrastructure is helping to bring AI fully on-chain, there will always be a need to validate off-chain computations. For example, banks may wish to use the existing databases to run complex models and have those results arrive on-chain for tokenization-related processes such as on-chain credit approval.
Many ZK rollups already incorporate zkML into their workflow, including zkSync, StarkNet, and Scroll. Meanwhile, projects like ZKML are helping to provide this infrastructure to others.
The main benefits include:
Scalability: ZK Proofs can quickly confirm a large number of off-chain computations. Even as the number of transactions scale, a single ZK Proof can validate all of them.
Privacy Preservation: Data and AI model details are kept private while all parties can verify that the data and models were not corrupted.
Trustlessness: Computations can be confirmed without reliance on centralized parties.
Web2 Integrations: Web2 is, by definition, off-chain, meaning that verifiable inference can help bring their datasets and AI computations on-chain. This helps to increase Web3 adoption.
Data Marketplaces
AI relies heavily on high-quality data for training and fine-tuning models, but a few large corporations often control access to such data.
Decentralized data marketplaces enable individuals and organizations to securely sell and share their data while maintaining privacy and ownership. This means that AI projects can also access datasets of any type without compromising user privacy or security.
An interesting example includes Ocean Protocol, which has tokenized AI models and data and allows them to be accessed via an open marketplace. A tokenized AI model or dataset refers to using blockchain to purchase their respective access rights.
While decentralized compute (discussed above) facilitates the exchange of computing power between providers and users, data marketplaces perform a similar function for datasets.
The main benefits include:
Improved Monetization Methods: Any individual or business with a niche dataset can monetize it for new revenue streams. For example, a food supplier could monetize their supply chain data or consumer food trend data.
Permissionless Access: Global participants can freely collaborate to develop innovative products, build AI models, and generate valuable insights, expanding access to data and resources beyond traditional boundaries.
Lower Costs: A lot of data is hidden behind expensive paywalls. Open data marketplaces match buyers and sellers, leading to better pricing.
Decentralized AI Challenges
Each of the above areas is moving extremely quickly and holds immense promise. That being said, several obstacles must be addressed to make decentralized AI a widespread reality.
They are important to know as it will then become more clear how 0G is solving each of the below, which was a key reason that supported our $35M pre-seed raise.
These include:
Scalability: Right now, it’s very hard to store and process large amounts of data on-chain, as data availability solutions like Celestia cannot handle the high-performance needs of AI. Blockchains need faster throughput, more data storage, and greater computational capacity for large-scale AI operations to thrive. This is one of the core challenges that 0G is solving.
Interoperability Between AI and Blockchain: There needs to be standardized protocols that enable AI and blockchain to interact seamlessly, making integration easy. For example, AI computations often occur off-chain, but proving those computations on-chain through cryptographic methods (let alone doing these calculations on-chain) is still an evolving area requiring more progress.
Lack of AI Infrastructure and Tooling: The infrastructure needed to support decentralized AI is still in its infancy, with many projects facing inherent tradeoffs. For example, decentralized data storage solutions like Filecoin may only support unstructured data or be prohibitively expensive, with throughput too slow to support AI. As well, the tooling to provide a world-class experience for developers does not quite exist, limiting the amount of talent in the space. 0G is also solving this.
High Costs and Resource Limitations: AI computation is expensive, and decentralized networks still need to optimize the economics of providing these services at scale. Until costs are comparable to centralized solutions like AWS, it will be hard to truly onboard the next massive wave of users and applications.
Data Fragmentation and Availability: The decentralized nature of blockchain can lead to fragmented data sources, making it difficult for AI models to access the data they need for training and inference. For example, data is dispersed across countless Web2 databases and Web3 blockchains, requiring a means for individuals and entities to derive meaningful insights and actions using them.
Solving these will help to make decentralized AI a reality, which is what makes 0G’s role as a decentralized AI Operating System (dAIOS) so powerful. For example, 0G solves issues surrounding interoperability, scalability, prohibitive costs, data fragmentation and availability, and more. It’s essentially an all-in-one system that makes on-chain AI truly possible, which is why we’ve been busy integrating with so many partners.
We covered how this works here, but at a high level, 0G’s dAIOS infrastructure has three components:
0G Storage: A decentralized network that handles massive data loads efficiently and securely.
0G DA (Data Availability): An infinitely scalable and programmable data availability layer that provides seamless access and verification of data for end-users.
0G Serving: A framework that supports AI model inference, data retrieval, and training tasks, allowing real-time interaction with decentralized AI applications.
In combination, 0G provides a massively scalable solution that makes crypto AI a reality.
For example, data marketplaces can utilize 0G Storage to securely store all relevant datasets, while 0G DA (Data Availability) verifies and manages access permissions. This setup ensures that only eligible parties can quickly access the required data and provides the infrastructure for anyone else to launch a custom data marketplace.
0G is Making AI a Public Good
In this piece, we covered 4 critical areas of on-chain AI:
Decentralized Compute
AI Agents
Verifiable Inference
Data Marketplaces
We also covered how 0G’s role as a dAIOS means that it can address the inherent limitations that are preventing crypto AI from becoming a reality, such as issues surrounding scalability and cost.
We’re excited for what’s next and wish to work with aspiring developers and projects.
To start building with us, please reach out here.
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