Original text: Archetype

Translation: Yuliya, PANews

In today's rapidly evolving landscape of artificial intelligence and blockchain technology, the intersection of these two fields is giving rise to exciting innovative possibilities. This article delves into the ten important areas to watch in 2025, from interactions between intelligent agents to decentralized computing, from transformations in data markets to breakthroughs in privacy technology.

1. Interaction Between Agents

The inherent transparency and composability of blockchain make it an ideal foundational layer for interactions between agents. Intelligent agents developed by different entities for various purposes can seamlessly interact on the blockchain. Some notable experimental applications have already emerged, such as fund transfers between agents and joint token issuance.

The future potential of agent interactions is primarily reflected in two aspects: first, the creation of entirely new application domains, such as new social scenarios driven by agent interactions; second, the optimization of existing enterprise workflows, including traditionally cumbersome processes like platform authentication and verification, micropayments, and 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 can collaborate to complete tasks, solve problems, and govern systems and protocols. In an article published by Vitalik in early 2024 titled "Prospects and Challenges of Cryptocurrency and AI Applications," he mentioned the potential use of AI agents in prediction markets and arbitration. 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 capabilities of multi-agent systems and various forms of "collective intelligence." As an extension of coordination between agents, the coordination between agents and humans also constitutes an interesting design space, particularly in 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 verification mechanisms, especially when human work is done off-chain. This human-machine collaboration may give rise to 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 attendees;
• The virtual influencer Lil Miquela, born in 2016, has over 2 million followers on Instagram.
• The AI virtual streamer Neuro-sama, launched in 2022, has accumulated over 600,000 subscribers on Twitch;
• 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 gain a higher degree of autonomy by 2025 and may create a new mainstream entertainment category.

From the top left, clockwise: Hatsune Miku, Luna from Virtuals, Lil Miquela, and PLAVE

4. Generative/Intelligent Agent Content Marketing

Unlike the previous discussion where intelligent agents themselves are products, intelligent agents can also serve as supplementary tools for products. In today's attention economy, continuously producing engaging content is crucial for the success of any creative endeavor, 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 around 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 sector provides another typical case. Modern games increasingly need to maintain dynamism to sustain user engagement. Traditionally, fostering user-generated content (UGC) has been a classic method for creating game dynamism. Purely generative content (including in-game items, NPC characters, fully generated levels, etc.) may represent the next stage of this evolution. Looking ahead 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 interviewed artists active in music, visual arts, design, and curation within the cryptocurrency space and its periphery. These interviews revealed an important observation: artists interested in cryptocurrency often also pay attention to broader cutting-edge 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 the potential of generative art as AI art infrastructure even more apparent. It is extremely challenging to appropriately showcase these new artistic media on traditional display platforms. The ArtBlocks platform illustrates the future landscape of digital art display, storage, monetization, and preservation using blockchain technology, significantly improving the overall experience for artists and audiences.

In addition to display capabilities, AI tools have expanded the ability of the general public to create art. This trend of democratization is reshaping the landscape of artistic creation. Looking ahead to 2025, how blockchain technology will expand or empower these tools will be an attractive area of development.

Excerpt from "In Conversation: Maya Man"

6. Data Markets

It has been 20 years since Clive Humby coined the phrase "data is the new oil," and companies have been taking strong measures to hoard and monetize user data. Users have become aware that their data is the cornerstone of these multi-billion dollar companies, yet 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 data markets are twofold: first, addressing the issue of user data exploitation; second, addressing the shortage of data supply, as larger and better models are consuming the easily accessible "oil fields" of public internet data and require new data sources.

Data Power Returning to Users

The question of how to leverage decentralized infrastructure to return data power to users presents a vast design space that requires innovative solutions across multiple domains. Some of the most pressing issues include:

• The location of data storage 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 in tracing value back to the source after inference);
• And what kind of orchestration or data retrieval systems to use in a diverse model ecosystem.

Supply Constraints

In addressing supply constraints, the key is not simply to replicate the Scale AI model with tokens, but to understand where we can build advantages under favorable technological conditions and how to construct competitive solutions, whether in terms of scale, quality, or better incentive (and filtering) mechanisms, to create higher-value data products. Particularly, as most demand still comes from Web2 AI, thinking about how to combine 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 for AI development and deployment, then computing power is another key component. The traditional large data center model, with its unique advantages in location, energy, and hardware, has largely dominated the trajectory of deep learning and AI development in recent years. However, physical limitations and the development of open-source technologies are challenging this paradigm.

• The first phase (v1) of decentralized AI computing is essentially a replica of Web2 GPU cloud services, where there are no real advantages on the supply side (hardware or data centers) and organic demand is limited.
• In the second phase (v2), some outstanding teams are building a complete technology stack on the basis of 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 on the inference side. Teams are also beginning to differentiate in their use cases 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 is even beginning to see the rise of the AI-Fi market, with innovative economic primitives emerging that convert computing power and GPUs into revenue-generating assets, or utilize 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 sector, a gap between ideals and actual needs will always exist, making it difficult for this concept to fully realize its potential.

8. Computing Accounting Standards

A major challenge in the incentive mechanisms of decentralized high-performance computing networks is the lack of unified computing accounting standards for coordinating heterogeneous computing resources. AI models add multiple unique complexities to the output space of high-performance computing, including model variants, quantization schemes, and levels of randomness adjustable through model temperature and sampling hyperparameters. Additionally, AI hardware can produce different output results due to variations in GPU architecture and CUDA versions. These factors ultimately lead to the need for standards to regulate how models and computing markets measure their computational capabilities in heterogeneous distributed systems.

Partly due to the lack of these standards, several cases emerged in 2024 in both Web2 and Web3 domains where models and computing markets failed to accurately account for their computational quality and quantity. This forced users to run their own comparative model benchmarks and execute proof of work by limiting the rate of computing markets to audit the true performance of these AI layers.

Looking ahead 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, the ability to fairly compare various aspects of defined models or computing cluster outputs is crucial, as it will help audit and assess system performance.

9. Probabilistic Privacy Primitives

In "Prospects and Challenges of Cryptocurrency and AI Applications," Vitalik pointed 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) significantly increases the risk of adversarial machine learning attacks.”

While privacy is not a new area of research in blockchain, the rapid development of AI is accelerating the research and application of cryptographic primitives that support privacy. Significant progress has already 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 for encrypted data computation. At the same time, centralized AI giants like Nvidia and Apple are using proprietary TEEs for federated learning and private AI inference, ensuring privacy while maintaining consistency of hardware, firmware, and models across systems.

Based on these developments, the industry is closely monitoring advancements in privacy-preserving technologies in random state transitions and how these technologies can 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, as well as fully sovereign execution environments.

Apple's AI technology stack and Nvidia's H100 graphics processor

10. Agent Intent and Next-Generation User Transaction Interfaces

Over the past 12-16 months, there has been ambiguity in the definitions of concepts such as intent, agent behavior, agent intent, solutions, and agent solutions, lacking clear distinctions from traditional "robot" development in recent years. Autonomous on-chain transactions by AI agents are one of the closest applications to realization.

In the next 12 months, the industry expects to see the combination of more complex language systems with different data types and neural network architectures, advancing the overall design space. This raises several key questions:

• Will agents use existing on-chain transaction systems, or will they develop their own tools and methods?
• Will large language models continue to serve as the backend for these agent transaction systems, or will entirely new systems emerge?
• At the interface level, will users begin to conduct transactions using natural language?
• Will the classic idea of "wallet as browser" ultimately 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.

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