Author: Swayam
Compilation: Deep Tide TechFlow
The rapid development of artificial intelligence (AI) has enabled a few large technology companies to possess unprecedented computational power, data resources, and algorithmic techniques. However, as AI systems gradually integrate into our society, issues of accessibility, transparency, and control have become central topics of technical and policy discussions. In this context, the combination of blockchain technology with AI offers an alternative path worth exploring—a potential redefinition of how AI systems are developed, deployed, scaled, and governed.
We do not aim to completely disrupt existing AI infrastructure, but rather to explore through analysis the unique advantages that decentralized approaches may bring in certain specific use cases. At the same time, we acknowledge that in some contexts, traditional centralized systems may still be the more practical choice.
The following key questions guided our research:
Can the core features of decentralized systems (such as transparency and censorship resistance) complement the needs of modern AI systems (such as efficiency and scalability), or will they produce contradictions?
In what ways can blockchain technology provide substantial improvements at various stages of AI development—from data collection to model training to inference?
What technical and economic trade-offs will different stages face in the design of decentralized AI systems?
Current limitations in the AI technology stack
The Epoch AI team has made significant contributions to analyzing the limitations of the current AI technology stack. Their research elaborates on the main bottlenecks that AI training computational capacity may face by 2030 and uses Floating Point Operations per Second (FLoPs) as the core metric for measuring computational performance.
Research shows that the scalability of AI training computations may be limited by various factors, including insufficient power supply, bottlenecks in chip manufacturing technology, data scarcity, and network latency issues. Each of these factors sets different upper limits for achievable computational capacity, with latency issues considered the most challenging theoretical limit to overcome.
This chart emphasizes the necessity of advancements in hardware, energy efficiency, unlocking captured data on edge devices, and networking to support the growth of future artificial intelligence.
Power Limitations (Performance):
Feasibility of Expanding Power Infrastructure (Forecast for 2030): It is anticipated that by 2030, the capacity of data center parks is expected to reach between 1 to 5 gigawatts (GW). However, this growth relies on massive investments in power infrastructure and overcoming potential logistical and regulatory barriers.
Due to limitations of energy supply and power infrastructure, the global computational capacity expansion ceiling is expected to reach up to 10,000 times the current level.
Chip Production Capacity (Verifiability):
Currently, the production of chips (such as NVIDIA H100, Google TPU v5) for supporting advanced computations is limited by packaging technologies (such as TSMC's CoWoS technology). This limitation directly affects the availability and scalability of verifiable computing.
Bottlenecks in chip manufacturing and supply chains are major obstacles, yet a computational capacity growth of up to 50,000 times is still feasible.
Additionally, enabling secure isolation zones or Trusted Execution Environments (TEEs) on advanced chips in edge devices is crucial. These technologies not only verify computation results but also protect the privacy of sensitive data during the computation process.
Data Scarcity (Privacy):
Latency Barriers (Performance):
Inherent latency limitations in model training: As the scale of AI models continues to grow, the time required for a single forward and backward pass significantly increases due to the sequential nature of the computation process. This latency is an unavoidable fundamental limitation in the model training process, directly affecting training speed.
Challenges in scaling batch sizes: To mitigate latency issues, a common approach is to increase batch size, allowing more data to be processed in parallel. However, there are practical limitations to scaling batch sizes, such as insufficient memory capacity and diminishing marginal returns on model convergence as batch size increases. These factors make it increasingly difficult to offset latency by increasing batch size.
Foundation
Decentralized AI Triangle
The various limitations currently faced by AI (such as data scarcity, computational bottlenecks, latency issues, and chip production capacity) collectively form the 'Decentralized AI Triangle.' This framework attempts to achieve a balance between privacy, verifiability, and performance. These three attributes are core elements in ensuring the effectiveness, credibility, and scalability of decentralized AI systems.
The following table analyzes in detail the key trade-offs between privacy, verifiability, and performance, delving into their definitions, implementation technologies, and the challenges they face:
Privacy: Protecting sensitive data during AI training and inference is critical. Various key technologies are employed for this purpose, including Trusted Execution Environments (TEEs), Multi-Party Computation (MPC), Federated Learning, Fully Homomorphic Encryption (FHE), and Differential Privacy. Although these technologies are effective, they also pose challenges such as performance overhead, transparency issues affecting verifiability, and limited scalability.
Verifiability: To ensure the correctness and integrity of computations, techniques such as zero-knowledge proofs (ZKPs), cryptographic credentials, and verifiable computing are employed. However, achieving a balance between privacy, performance, and verifiability often requires additional resources and time, which may lead to computation delays.
Performance: Efficiently executing AI computations and achieving large-scale applications relies on distributed computing infrastructure, hardware acceleration, and efficient network connections. However, employing privacy-enhancing technologies can slow down computation speed, while verifiable computing may incur additional overhead.
Blockchain Trilemma:
The core challenge faced by the blockchain field is the trilemma; every blockchain system must balance between the following three:
Decentralization: Preventing any single entity from controlling the system by distributing the network across multiple independent nodes.
Security: Ensuring the network is protected from attacks and maintaining data integrity, which often requires more verification and consensus processes.
Scalability: Rapidly and cost-effectively processing a large number of transactions, but this often means making trade-offs in decentralization (reducing the number of nodes) or security (lowering verification intensity).
For example, Ethereum prioritizes decentralization and security, which results in relatively slower transaction processing speeds. A deeper understanding of these trade-offs in blockchain architecture can be found in related literature.
AI-Blockchain Collaborative Analysis Matrix (3x3)
The combination of AI and blockchain is a complex process of trade-offs and opportunities. This matrix illustrates where friction may occur between the two technologies, where harmonious fits may be found, and how they can sometimes amplify each other's weaknesses.
How the Collaboration Matrix Works
Collaboration strength reflects the compatibility and influence of blockchain and AI attributes in specific domains. Specifically, it depends on how the two technologies jointly address challenges and enhance each other's functionalities. For instance, in the area of data privacy, the immutability of blockchain combined with the data processing capabilities of AI may lead to new solutions.
How the Collaboration Matrix Works
Example 1: Performance + Decentralization (Weak Collaboration)
In decentralized networks, such as Bitcoin or Ethereum, performance is often constrained by various factors. These limitations include resource volatility among nodes, high communication delays, transaction processing costs, and the complexity of consensus mechanisms. For AI applications that require low latency and high throughput (such as real-time AI inference or large-scale model training), these networks struggle to provide sufficient speed and computational reliability, failing to meet high-performance demands.
Example 2: Privacy + Decentralization (Strong Collaboration)
Privacy-preserving AI technologies (such as federated learning) can fully leverage the decentralized characteristics of blockchain to achieve efficient collaboration while protecting user data. For example, SoraChain AI provides a solution that ensures data ownership is not compromised through blockchain-supported federated learning. Data owners can contribute high-quality data for model training while maintaining privacy, achieving a win-win between privacy and collaboration.
The goal of this matrix is to help the industry clearly understand the intersection of AI and blockchain, guiding innovators and investors to prioritize feasible directions, explore promising areas, and avoid getting caught up in merely speculative projects.
AI-Blockchain Collaboration Matrix
The two axes of the collaboration matrix represent different attributes: one axis consists of the three core features of decentralized AI systems—verifiability, privacy, and performance; the other axis represents the blockchain trilemma—security, scalability, and decentralization. When these attributes intersect, a series of synergies are formed, ranging from high compatibility to potential conflicts.
For example, when verifiability is combined with security (high collaboration), powerful systems can be constructed to prove the correctness and integrity of AI computations. However, when performance demands conflict with decentralization (low collaboration), the high overhead of distributed systems can significantly impact efficiency. Additionally, some combinations (such as privacy and scalability) lie in the middle ground, possessing both potential and facing complex technical challenges.
Why is this important?
Strategic Compass: This matrix provides clear direction for decision-makers, researchers, and developers, helping them focus on high-collaboration areas, such as ensuring data privacy through federated learning or utilizing decentralized computing for scalable AI training.
Focusing on impactful innovation and resource allocation: Understanding the distribution of collaboration strength (such as security + verifiability, privacy + decentralization) helps stakeholders concentrate resources on high-value areas, avoiding waste on weak collaboration or impractical integrations.
Guiding the evolution of the ecosystem: As AI and blockchain technologies continue to develop, this matrix can serve as a dynamic tool to evaluate emerging projects, ensuring they meet real needs rather than fueling trends of excessive hype.
The following table summarizes these attribute combinations by collaboration strength (from strong to weak) and explains how they operate in decentralized AI systems. At the same time, the table provides examples of innovative projects that illustrate the real-world applications of these combinations. Through this table, readers can gain a more intuitive understanding of the intersection points between blockchain and AI technologies, identifying truly impactful areas while avoiding those that are overhyped or technically infeasible.
AI-Blockchain Collaboration Matrix: Key Intersection Points of AI and Blockchain Technologies Classified by Collaboration Strength
Conclusion
The combination of blockchain and AI holds immense transformative potential, but future development requires clear direction and focused effort. Projects that truly drive innovation are shaping the future of decentralized intelligence by addressing key challenges such as data privacy, scalability, and trust. For example, federated learning (privacy + decentralization) enables collaboration while protecting user data, distributed computing and training (performance + scalability) enhance the efficiency of AI systems, and zkML (zero-knowledge machine learning, verifiability + security) ensures the credibility of AI computations.
At the same time, we need to approach this field with caution. Many so-called AI agents are merely simple repackaging of existing models, with limited functionality and a lack of depth in their integration with blockchain. True breakthroughs will come from projects that fully leverage the strengths of both blockchain and AI while committing to solving real-world problems, rather than merely chasing market hype.
Looking ahead, the AI-Blockchain Collaboration Matrix will become an important tool for evaluating projects, effectively helping decision-makers distinguish truly impactful innovations from meaningless noise.
The next decade will belong to projects that can combine the high reliability of blockchain with the transformative capabilities of AI to solve real-world problems. For instance, energy-efficient model training will significantly reduce the energy consumption of AI systems; privacy-preserving collaboration will provide a safer environment for data sharing; and scalable AI governance will promote the implementation of larger and more efficient intelligent systems. The industry needs to focus on these key areas to truly unlock the future of decentralized intelligence.
