The AI-Blockchain Collaborative Matrix will become an important tool for project evaluation, effectively helping decision-makers distinguish between truly impactful innovations and meaningless noise.
Author: Swayam
Compiled by: Deep Tide TechFlow
The rapid development of artificial intelligence (AI) has allowed a few large tech companies to wield unprecedented computing power, data resources, and algorithmic technology. 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 and AI offers an alternative path worth exploring—one that may redefine the development, deployment, scaling, and governance of AI systems.
We do not aim to completely disrupt existing AI infrastructure, but rather wish to explore the unique advantages that decentralized approaches may bring in certain specific use cases through analysis. 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 characteristics of decentralized systems (such as transparency and censorship resistance) complement the demands of modern AI systems (such as efficiency and scalability), or will they create contradictions?
In what ways can blockchain technology provide substantive 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 details the main bottlenecks that may affect the scalability of AI training computations by 2030, using Floating Point Operations per Second (FLoPs) as the core metric for measuring computational performance.
Research indicates that the scalability of AI training computations may be constrained 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 data captured on edge devices, and networking aspects to support the growth of artificial intelligence in the future.
Power Constraints (Performance):
Feasibility of expanding power infrastructure (2030 forecast): It is expected that by 2030, the capacity of data center parks may reach 1 to 5 gigawatts (GW). However, this growth will rely on large-scale investments in power infrastructure, while also overcoming potential logistical and regulatory barriers.
Due to limitations in energy supply and power infrastructure, the global computing capacity expansion ceiling is expected to reach up to 10,000 times the current level.
Chip Production Capacity (Verifiability):
Currently, the production of chips that support advanced computing (e.g., NVIDIA H100, Google TPU v5) is constrained by packaging technologies (e.g., 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, but it is still possible to achieve a growth in computing capacity of up to 50,000 times.
Moreover, enabling secure enclaves or Trusted Execution Environments (TEEs) on edge devices is crucial for advanced chips. These technologies not only validate computational results but also protect the privacy of sensitive data during computations.
Data Scarcity (Privacy):
Latency Barrier (Performance):
Inherent delay 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 delay is a fundamental limitation that cannot be bypassed during the model training process, directly impacting 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 limits to batch size scaling, such as insufficient memory capacity and diminishing marginal returns on model convergence as batch size increases. These factors make it more challenging to offset delays by increasing batch size.
Foundation
Decentralized AI Triangle
The various limitations currently faced by AI (such as data scarcity, computational capacity bottlenecks, latency issues, and chip production capacity) collectively form the 'Decentralized AI Triangle.' This framework attempts to achieve a balance among privacy, verifiability, and performance. These three attributes are core elements to ensure the effectiveness, trustworthiness, and scalability of decentralized AI systems.
The following table analyzes the critical trade-offs among privacy, verifiability, and performance in detail, exploring their definitions, enabling technologies, and the challenges they face:
Privacy: Protecting sensitive data is crucial during the training and inference processes of AI. Various key technologies are used for this purpose, including Trusted Execution Environments (TEEs), Multi-Party Computation (MPC), Federated Learning, Fully Homomorphic Encryption (FHE), and Differential Privacy. While effective, these technologies also pose challenges such as performance overhead, transparency issues affecting verifiability, and limited scalability.
Verifiability: To ensure the correctness and integrity of computations, technologies such as Zero-Knowledge Proofs (ZKPs), cryptographic credentials, and verifiable computing are employed. However, achieving a balance between privacy and performance with verifiability often requires additional resources and time, which may lead to computational 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 speeds, and verifiable computing may also add extra overhead.
Blockchain Trilemma:
The core challenge facing the blockchain space is the trilemma, where every blockchain system must balance among the following three aspects:
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 often requires more verification and consensus processes.
Scalability: Quickly and economically processing a large number of transactions, but this often means making compromises in either decentralization (reducing the number of nodes) or security (lowering verification strength).
For example, Ethereum prioritizes decentralization and security, leading to relatively slow transaction processing speeds. A deep understanding of these trade-offs in blockchain architecture can be found in relevant 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 these two technologies may experience friction, find harmonious alignment, and sometimes amplify each other's weaknesses.
How the Collaborative Matrix Works
Collaborative strength reflects the compatibility and influence of blockchain and AI attributes in specific domains. Specifically, it depends on how the two technologies work together to address challenges and enhance each other's capabilities. For example, in terms of data privacy, the immutability of blockchain combined with the data processing capabilities of AI could lead to new solutions.
How the Collaborative 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 volatility of node resources, high communication latency, transaction processing costs, and the complexity of consensus mechanisms. For AI applications that require low latency and high throughput (e.g., real-time AI inference or large-scale model training), these networks often struggle to provide sufficient speed and computational reliability to meet high-performance demands.
Example 2: Privacy + Decentralization (Strong Collaboration)
Privacy-preserving AI technologies (e.g., 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 retaining privacy, achieving a win-win situation for 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 those feasible directions, explore promising areas, and avoid getting caught up in projects that are merely speculative.
AI-Blockchain Collaborative Matrix
The two axes of the collaborative matrix represent different attributes: one axis represents the three core characteristics of decentralized AI systems—verifiability, privacy, and performance; the other axis represents the blockchain trilemma—security, scalability, and decentralization. When these attributes intersect, a range of collaborative effects are formed, from high alignment to potential conflicts.
For instance, when verifiability and security are combined (high collaboration), powerful systems can be built 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) occupy a middle ground, having 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 achieving scalable AI training through decentralized computing.
Focusing on impactful innovations and resource allocation: Understanding the distribution of collaborative strength (such as security + verifiability, privacy + decentralization) helps stakeholders concentrate resources in high-value areas, avoiding waste on weak collaborations 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 for evaluating emerging projects, ensuring they meet real needs rather than fueling trends of excessive hype.
The following table summarizes these attribute combinations by collaborative 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 showcase the real-world applications of these combinations. Through this table, readers can gain a more intuitive understanding of the intersection of blockchain and AI technologies, identify truly impactful areas, and avoid those that are overhyped or technically unfeasible.
AI-Blockchain Collaborative Matrix: Key intersection points of AI and blockchain technologies categorized by collaborative 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) achieves collaboration while protecting user data, distributed computing and training (performance + scalability) enhances the efficiency of AI systems, and zkML (zero-knowledge machine learning, verifiability + security) provides assurance for the trustworthiness of AI computations.
At the same time, we need to approach this field with caution. Many so-called AI agents are merely simple wrappers around 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 advantages of both blockchain and AI, and are dedicated to solving real problems, rather than merely chasing market hype.
Looking to the future, the AI-Blockchain Collaborative Matrix will become an important tool for project evaluation, effectively helping decision-makers distinguish between truly impactful innovations and 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 example, 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 drive the realization of larger-scale, more efficient intelligent systems. The industry needs to focus on these key areas to truly unlock the future of decentralized intelligence.
