Introduction

In the rapidly evolving landscape of blockchain and artificial intelligence, our project represents a groundbreaking convergence of two technological frontiers.

While smart contracts have been a transformative force in the blockchain domain, their potential remains inherently limited if they cannot integrate AI models into their operational framework.

True to their name, smart contracts can only achieve genuine intelligence and versatility when empowered with the advanced decision-making and analytical abilities of AI. This is the crucial step towards realizing the full potential of Web3.

The need for this integration is especially apparent in real-world applications today. Consider, for example, incorporating real jurisdictional laws into a DAO’s smart contracts. Legal statutes are often fraught with ambiguities and complexities that cannot be directly translated into code. In such a scenario, the nuanced interpretation and judgment akin to a legal expert is essential.

This is where AI, particularly advanced models like Large Language Models, come into play, acting as impartial ‘judges’ to assess and apply legal principles. Indeed, merging AI and smart contracts is not merely a feature or enhancement; it is a fundamental step to bridge the gap between the binary world of code and the multifaceted reality of human laws and societal norms.

DNA Layer is the first Layer-1 autonomous AI oracle network to bring AI to the blockchain in a scalable, secure, and computationally efficient end-to-end system. This article introduces three major innovations distinct to DNA Layer. We begin with a detailed look at the AIVM, the Artificial Intelligence Virtual Machine, our fully integrated, decentralized system for AI model inference queries, tailored to the unique needs of real-world applications.

The AIVM is a revolutionary solution to facilitate AI tasks trustlessly on the blockchain, while query execution occurs off-chain. Unlike traditional mining where participants are rewarded for solving arbitrary cryptographic problems, our system awards fees in our native asset, $DNA, to pools of miners utilizing GPU-powered nodes when they have successfully solved AI model inference queries posted by platform users.

This approach represents a paradigm shift in the mining process, aligning the computational power of miners not just with securing the blockchain but also directly with the practical application of AI problem-solving.

The second technological innovation of DNA Layer is its decentralized inference protocol. This protocol implements dual transactions at the blockchain level which we call bifurcated inference ledgering, and upon release further breaks down its latter transaction into subphases for a commit-reveal mechanism.

This implementation enables the first trustless environment where AI computations are performed transparently and are reliably reported on-chain while safeguarding against dishonest behavior and free-riding.

DNA Layer's final advancement is perhaps its most cutting edge - the system’s cryptographic hybrid-privacy design called Split-Flow. This novel design addresses one of the most significant hurdles in crafting a privacy-focused decentralized inference system - the simultaneous need for confidentiality and verifiability.

Traditional encryption methods, while adept at preserving confidentiality by securing data at rest or in transit, render data unusable for practical computation by obscuring the data from the systems that need to process it. This makes it unintelligible and non-operable for computational algorithms, creating a substantial barrier in a system where primary utility is derived from the ability to perform complex computations and inference on user data.

This necessitates the development of specialized data security techniques such as Homomorphic Encryption or Secure Multi-Party Computation, which are designed to enable computations on encrypted data without ever needing to decrypt it.

However, the introduction of such techniques brings us to the second challenge of such a system: verifiability. Users and node owners inherently desire not just the confidentiality of their data or models but also the assurance that the computations performed are correct, trustworthy, and intended.

For verifiability, Zero-Knowledge Proofs can be employed to demonstrate the correctness of computations without revealing the underlying data. But integrating this into a system already contending with encrypted data adds layers of complexity.

The intersection of these challenges—ensuring confidentiality while enabling computation and providing verifiability—requires a sophisticated balance of cryptographic innovation and system design that DNA Layer has engineered. This is Split-Flow. Our hybrid-privacy system architecture is designed not just as a theoretical model but as a practical solution, researched, conceived of, and built from scratch by a veteran team of researchers in AI and cryptography.

This article delves into the intricacies of our architectural design, these three cryptographic advancements, and the platform’s tokenomics structure, as well as the broader implications of our project in reshaping the landscape of decentralized AI applications.

Introducing the AIVM - The Uniform Execution EnvironmentDecentralized InferenceHybrid Design for Enhanced PrivacyLarge Language Models in the DNA EcosystemDNA Layer's Impact for AIOverview of $DNAThe First Application on DNA Layer: DNA XAddendumBackground TechnologyDefinitions