1. Agent-to-Agent Interaction
The inherent transparency and composability of Blockchain make it an ideal foundation for agent-to-agent interactions. Intelligent agents developed by different entities and serving various purposes can seamlessly interact on the Blockchain. Some promising experimental applications have already emerged, such as agents transferring funds or jointly issuing Tokens.The future development potential of agent-to-agent interactions lies in two main areas: First, creating entirely new application domains, such as novel social scenarios driven by agent interactions; Second, optimizing existing enterprise-level workflows, including platform authentication and verification, micro-payments, and cross-platform workflow integration - traditionally more cumbersome processes.
2. Decentralized Intelligent Agent Organizations
Large-scale multi-agent coordination is another exciting research area. This involves how multi-agent systems can collaborate to accomplish tasks, solve problems, and govern systems and protocols. In an early 2024 article "The Prospects and Challenges of Cryptocurrencies and AI Applications", Vitalik mentioned the potential of using 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 capability boundaries 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, especially in terms of how communities interact around agents and how agents organize humans to take collective action. Researchers are particularly focused on agent experiments where the objective function involves large-scale human coordination. Such applications require corresponding verification mechanisms, especially when humans are working 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 internet celebrity Lil Miquela, born in 2016, has over 2 million followers on Instagram;
- The AI virtual broadcaster 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.
4. Generative/Intelligent Agent Content Marketing
Unlike the case of intelligent agents as products themselves, intelligent agents can also serve as complementary tools for products. In today's attention economy, continuously 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 the discussion on the boundary between Meme Tokens and intelligent agents. Even without fully achieving "intelligence", intelligent agents have already become a powerful means for Meme Tokens to gain traction. The gaming industry provides another typical case. Modern games increasingly need to maintain dynamism to sustain user engagement. Traditionally, cultivating user-generated content (UGC) has been a classic method for creating game dynamics. Pure 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 the cryptocurrency field and its periphery, including music, visual arts, design, and curation. These interviews revealed an important observation: artists interested in cryptocurrencies often also focus on a wider range of emerging 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 live-coding art. Generative art and Blockchain technology have long had synergistic effects, making their potential as AI art infrastructure more apparent. On traditional display platforms, it is extremely difficult to properly showcase these new art media. The ArtBlocks platform has demonstrated a future vision for using Blockchain technology for digital art display, storage, monetization, and preservation, significantly improving the overall experience for artists and audiences. In addition to display functions, AI tools have also expanded the ability of the general public to create art. This democratization trend 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.6. Data Marketplaces
It has been 20 years since Clive Humby proposed the idea that "data is the new oil", and major companies have been taking strong measures to hoard and monetize user data. Users have become aware that their data is the foundation of these multi-billion dollar companies, but they have almost no control over how their data is used and cannot share in the profits generated by their data. As powerful AI models are rapidly developed, this contradiction has become more pronounced. The opportunities facing data marketplaces have two aspects: one is to solve the problem of user data exploitation, and the other is to solve the problem of data supply shortage, as increasingly larger and better models are consuming the easily accessible "oil field" of public internet data and require new data sources.Returning Data Power to Users
Regarding how to use decentralized infrastructure to return data power to users, this is a broad design space that requires innovative solutions across multiple domains. Some of the most pressing issues include:- Data storage location and how to protect privacy during storage, transmission, and computation;
- How to objectively evaluate, 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 system to use in a diversified model ecosystem.
Supply Constraints
In addressing supply constraints, the key is not simply to replicate Scale AI's token model, but to understand where we can build advantages in the face of technological tailwinds, and how to construct solutions with competitive advantages, whether in scale, quality, or better incentive (and filtering) mechanisms, to create higher-value data products. Particularly as the majority of demand still comes from Web2 AI, it is an important research area to consider how to integrate smart contract execution mechanisms with traditional service level agreements (SLAs) and tools.7. Decentralized Computing
If data is a fundamental element in the development and deployment of AI, then computing power is another key component. The traditional large-scale data center model, with its unique advantages in site, energy, and hardware, has largely dominated the development trajectory of deep learning and AI in recent years. However, physical constraints and the evolution of open-source technologies are challenging this landscape.- The first stage (v1) of decentralized AI computing is essentially a replica of Web2 GPU cloud services, without real advantages on the supply side (hardware or data centers) and limited organic demand.
- In the second stage (v2), some outstanding teams have built complete technology stacks on the foundation of heterogeneous high-performance computing (HPC) supply, demonstrating unique capabilities in scheduling, routing, and pricing, while also developing proprietary features to attract demand and address profit margin pressures, particularly on the inference side. Teams have also begun to differentiate in use cases and market strategies, with some focusing on integrating compiler frameworks to achieve efficient inference routing across hardware, and others creating distributed model training frameworks on the computing networks they have built.
8. Computation Accounting Standards
In the incentive mechanism of the decentralized high-performance computing network, one of the major challenges facing the coordination of heterogeneous computing resources is the lack of a unified computation accounting standard. AI models have added multiple unique complexities to the output space of high-performance computing, including model variants, quantization schemes, and adjustable levels of randomness through model temperature and sampling hyperparameters. In addition, AI hardware can also produce different output results due to differences in GPU architecture and CUDA version. These factors ultimately require 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, there have been multiple cases in the Web2 and Web3 domains in 2024 where model and computing markets have failed to accurately account for their computing quality and quantity. This has forced users to run their own comparative model benchmarks and execute proof-of-work by throttling the rate of the computing market 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 more verifiable than traditional AI. For the average user, the ability to fairly compare various aspects of the defined model or compute cluster output is crucial, as it will aid in auditing and evaluating system performance.
9. Probabilistic Privacy Primitives
In "The Prospects and Challenges of Crypto and AI Applications", Vitalik pointed out a unique challenge in connecting cryptocurrencies and AI: "In cryptography, open source is the only way to achieve true security, but in AI, the openness of models (and even their training data) greatly increases the risk of adversarial machine learning attacks."
While privacy is not a new field in blockchain research, the rapid development of AI is accelerating the research and application of privacy-preserving cryptographic primitives. Significant progress has 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 generic application scenarios such as encrypted data computation and private shared state. Meanwhile, centralized AI giants like Nvidia and Apple are also using proprietary TEEs for federated learning and private AI inference, ensuring privacy while maintaining hardware, firmware, and model consistency across systems.
Based on these developments, the industry is closely following the progress of privacy-preserving techniques in probabilistic state transitions, and how these technologies can accelerate the practical deployment of decentralized AI applications on heterogeneous systems. This includes aspects ranging from decentralized private inference to encrypted data storage/access pipelines, and fully sovereign execution environments.
10. Proxy Intentions and Next-Gen User Trading Interfaces
Over the past 12-16 months, there has been a lack of clear definition around concepts such as intent, proxy behavior, proxy intent, solutions, and proxy solutions, and how these differ from the traditional "robot" development in recent years. AI agents autonomously conducting on-chain transactions is one of the closest application scenarios to being realized.
In the next 12 months, the industry is expected to see more complex language systems combined with different data types and neural network architectures, advancing the overall design space. This raises several key questions:
- Will agents use existing on-chain trading 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 start using natural language for trading?
- Will the classic "wallet as browser" concept ultimately be realized?
The answers to these questions will profoundly shape the future development of cryptocurrency trading. As AI technology advances, agent systems may become more intelligent and autonomous, better able to understand and execute user intent.
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