Original: Archetype
Compiled by: Yuliya, PANews
In the rapidly evolving fields of artificial intelligence and blockchain technology, the intersection of these two areas is giving rise to exciting innovative possibilities. This article delves into the ten important areas to watch in 2025, from the interactions of intelligent agents to decentralized computing, from the transformation of data markets to breakthroughs in privacy technologies.
1. Inter-agent Interaction
The inherent transparency and composability features of blockchain make it an ideal foundational layer for inter-agent interaction. Intelligent agents developed by different entities for different purposes can interact seamlessly on the blockchain. Some notable experimental applications have already emerged, such as fund transfers between agents, joint token issuance, and more.
The potential for development in inter-agent interaction primarily manifests in two aspects: first, pioneering new application fields, such as new social scenarios driven by agent interactions; second, optimizing existing enterprise-level workflows, including platform certification and verification, micropayments, and the traditionally cumbersome processes of cross-platform workflow integration.
Aethernet and Clanker achieve joint token issuance on the Warpcast platform
2. Decentralized Intelligent Agent Organizations
Large-scale multi-agent coordination is another exciting research area. This involves how multi-agent systems collaboratively complete tasks, solve problems, and govern systems and protocols. Vitalik mentioned the potential use of AI agents in prediction markets and arbitration in his article published at the beginning of 2024 (Prospects and Challenges of Cryptocurrency and AI Applications). He believes that, from a macro perspective, multi-agent systems show significant potential in "truth" discovery and autonomous governance systems.
The industry is continuously exploring and experimenting with the boundaries of multi-agent systems' capabilities and various forms of "collective intelligence." As an extension of coordination between agents, coordination between agents and humans also constitutes an interesting design space, particularly regarding how communities interact around agents and how agents organize humans for collective action.
Researchers are particularly focused on agent experiments where the objective functions involve large-scale human coordination. Such applications require corresponding validation mechanisms, especially when human work is done off-chain. This human-machine collaboration may give rise to some unique and interesting emergent behaviors.
3. Intelligent Agent Multimedia Entertainment
The concept of digital personas has existed for decades.
As early as 2007, Hatsune Miku was able to hold sold-out concerts in venues with 20,000 people;
The virtual influencer Lil Miquela, born in 2016, has over 2 million followers on Instagram.
The AI virtual host Neuro-sama, launched in 2022, has accumulated over 600,000 subscribers on the Twitch platform;
The virtual K-pop group PLAVE, established in 2023, has garnered over 300 million views on YouTube in less than two years.
With advancements in AI infrastructure and the integration of blockchain in payments, value transfer, and open data platforms, these intelligent agents are expected to achieve a higher degree of autonomy by 2025, potentially creating a whole new mainstream entertainment category.
From the upper left corner clockwise: Hatsune Miku, Luna from Virtuals, Lil Miquela, and PLAVE
4. Generative/Intelligent Agent Content Marketing
Unlike the situation where intelligent agents themselves are products as mentioned earlier, intelligent agents can also serve as supplementary tools for products. In today's attention economy, consistently producing engaging content is crucial for the success of any creative, product, or company. Generative/intelligent agent content is becoming a powerful tool for teams to ensure 24/7 uninterrupted content production.
The development in this area has been accelerated by discussions about the boundaries between Meme coins and intelligent agents. Even if "intelligence" has not been fully realized, intelligent agents have already become a powerful means for Meme coins to gain traction.
The gaming field provides another typical case. Modern games increasingly need to maintain dynamism to keep users engaged. Traditionally, fostering user-generated content (UGC) has been the classic method of creating game dynamism. Purely generative content (including in-game items, NPC characters, completely generated levels, etc.) may represent the next stage of this evolution. Looking forward to 2025, the capabilities of intelligent agents will greatly expand the boundaries of traditional distribution strategies.
5. Next-Generation Art Tools and Platforms
The "In Conversation With" series launched in 2024 features interviews with artists active in the cryptocurrency space and its periphery, including music, visual arts, design, and curation. These interviews reveal an important observation: artists interested in cryptocurrencies often also pay attention to broader frontier technologies and tend to deeply integrate these technologies into the aesthetics or core of their artistic practices, such as AR/VR objects, code-based art, and real-time programming art.
Generative art and blockchain technology have historically had a synergistic effect, making their potential as the foundation for AI art infrastructure more apparent. It is extremely difficult to appropriately showcase these new types of artistic media on traditional display platforms. The ArtBlocks platform demonstrates a future vision for displaying, storing, monetizing, and preserving digital art using blockchain technology, significantly improving the overall experience for artists and audiences alike.
In addition to showcasing capabilities, AI tools are also expanding the ability of the general public to create art. This trend of democratization is reshaping the landscape of art creation. Looking forward to 2025, how blockchain technology will expand or empower these tools will be a highly attractive development direction.
Excerpt from (Conversation: Maya Man)
6. Data Market
Since Clive Humby proposed the idea that "data is the new oil" 20 years ago, major companies have been taking strong measures to hoard and monetize user data. Users have realized that their data is the cornerstone of these billion-dollar companies, but they have little control over how their data is used and cannot share in the profits generated from their data. With the rapid development of powerful AI models, this contradiction has become even more pronounced.
The opportunities facing the data market have two aspects: one is to solve the problem of user data exploitation, and the other is to address the shortage of data supply, as increasingly larger and better models are consuming the easily accessible "oil field" of public internet data and require new data sources.
Data power returns to users
The question of how to leverage decentralized infrastructure to return data power to users is a vast design space that requires innovative solutions across multiple fields. Some of the most pressing questions include:
Data storage locations and how to protect privacy during storage, transmission, and computation;
How to objectively assess, filter, and measure data quality;
What mechanisms to use for attribution and monetization (especially tracing value back to the source after inference);
And what kind of orchestration or data retrieval systems to use in a diversified model ecosystem.
Supply Restrictions
In addressing supply constraints, the key is not simply to replicate Scale AI's model with tokens, but to understand where we can build advantages in favorable technological conditions, and how to construct solutions with competitive advantages, whether in scale, quality, or better incentives (and filtering) mechanisms to create higher value data products. Especially when most demand still comes from Web2 AI, thinking about how to integrate smart contract execution mechanisms with traditional service level agreements (SLAs) and tools is an important area of research.
7. Decentralized Computing
If data is a fundamental element of AI development and deployment, then computing power is another critical component. The traditional large data center model has largely dominated the trajectory of deep learning and AI development in recent years due to its unique advantages in location, energy, and hardware. However, physical limitations and the development of open-source technologies are challenging this pattern.
The first phase (v1) of decentralized AI computing is essentially a replica of Web2 GPU cloud services, with no real advantages on the supply side (hardware or data centers) and limited organic demand.
In the second phase (v2), some outstanding teams are building a complete tech stack on heterogeneous high-performance computing (HPC) supply, demonstrating unique capabilities in scheduling, routing, and pricing, while developing proprietary features to attract demand and cope with profit compression, especially in the inference sector. Teams are also beginning to differentiate in usage scenarios and market strategies, with some focusing on integrating compiler frameworks for efficient inference routing across hardware, while others are pioneering distributed model training frameworks on their constructed computing networks.
The industry has even begun to witness the rise of the AI-Fi market, with innovative economic primitives emerging that convert computing power and GPUs into revenue-generating assets, or leverage on-chain liquidity to provide alternative funding sources for data centers to acquire hardware.
The main question here is to what extent decentralized AI will develop and deploy on decentralized computing infrastructure, or whether, like in the storage field, the gap between ideals and actual needs will always exist, making it difficult for this concept to fully realize its potential.
8. Computational Accounting Standards
In the incentive mechanisms of decentralized high-performance computing networks, a major challenge faced in coordinating heterogeneous computing resources is the lack of unified computational accounting standards. AI models add multiple unique complexities to the output space of high-performance computing, including model variants, quantization schemes, and levels of randomness that can be adjusted through model temperature and sampling hyperparameters. Additionally, AI hardware may produce different output results due to differences in GPU architectures and CUDA versions. These factors ultimately necessitate the establishment of standards to regulate how models and computing markets measure their computing capabilities in heterogeneous distributed systems.
Partly due to the lack of these standards, multiple cases have emerged in 2024 in both the Web2 and Web3 domains where models and computation markets failed to accurately account for their computational quality and quantity. This has led users to run their own comparative model benchmarks and to execute proof of work by limiting the rates of the computing markets to audit the true performance of these AI layers.
Looking forward to 2025, the intersection of cryptography and AI is expected to achieve breakthroughs in verifiability, making it easier to verify compared to traditional AI. For ordinary users, being able to fairly compare various aspects of defined models or computing cluster outputs is crucial, which will help audit and assess system performance.
9. Probabilistic Privacy Primitives
In "Prospects and Challenges of Cryptocurrency and AI Applications," Vitalik points out a unique challenge when connecting cryptocurrency and AI: 'In the field of cryptography, open source is the only way to achieve true security, but in the AI field, the openness of models (and even their training data) greatly increases the risk of adversarial machine learning attacks.'
Although privacy is not a new area of blockchain research, the rapid development of AI is accelerating the research and application of cryptographic primitives that support privacy. Significant advances have been made in privacy-enhancing technologies in 2024, including Zero-Knowledge Proofs (ZK), Fully Homomorphic Encryption (FHE), Trusted Execution Environments (TEEs), and Multi-Party Computation (MPC), which are used in general application scenarios such as private sharing states of encrypted data computation. At the same time, centralized AI giants like Nvidia and Apple are also using proprietary TEEs for federated learning and private AI inference, ensuring privacy while maintaining consistency across hardware, firmware, and models in systems.
Based on these developments, the industry is closely monitoring the progress of privacy-preserving technologies in random state transitions and how these technologies accelerate the practical implementation of decentralized AI applications on heterogeneous systems. This includes various aspects from decentralized private inference to encrypted data storage/access pipelines and fully sovereign execution environments.
Apple's AI technology stack and Nvidia's H100 graphics processor
10. Agent Intent and Next-Generation User Transaction Interfaces
In the past 12-16 months, there has been ambiguity in defining concepts such as intent, agent behavior, agent intent, solutions, and agent solutions, with a lack of clear demarcation from traditional "robot" development in recent years. AI agents autonomously conducting on-chain transactions is one of the closest-to-implementation application scenarios.
In the next 12 months, the industry expects to see more complex language systems combined with various data types and neural network architectures, thus advancing the overall design space. This raises several key questions:
Will agents use existing on-chain transaction systems or develop their own tools and methods?
Will large language models continue to serve as the backend for these agent trading systems, or will entirely new systems emerge?
At the interface level, will users begin to use natural language for transactions?
Will the classic "wallet is the browser" concept eventually be realized?
The answers to these questions will profoundly impact the future direction of cryptocurrency trading. With advancements in AI technology, agent systems may become smarter and more autonomous, better able to understand and execute user intent.