A summary of the top 10 integration development directions of Crypto AI: inter-agent interaction, content marketing, and data markets, etc

Original Title: "Crypto x AI: 10 Categories We're Watching in 2025"

Author: Archetype

Translation by: 深潮TechFlow

1. Agent-to-Agent Interaction

Blockchain, with its inherent transparency and composability, has become an ideal platform for seamless interaction between agents. In this interaction, agents developed by different entities for different purposes can collaborate to complete tasks. There have already been some exciting attempts, such as transfers between agents and joint token issuance. We look forward to further expanding agent-to-agent interactions: on one hand, creating entirely new application scenarios, such as agent-driven new social platforms; on the other hand, optimizing existing business workflows, such as platform authentication, micropayments, cross-platform workflow integration, etc., thereby simplifying today's complex and cumbersome operational processes. - Danny, Katie, Aadharsh, Dmitriy

aethernet and clanker jointly issued Token on Warpcast

2. Decentralized Agentic Organizations

Large-scale multi-agent collaboration is another exciting research direction. How can multi-agent systems work together to complete tasks, solve problems, and even manage protocols and systems? In an early 2024 article titled “The Promise and Challenges of Crypto + AI Applications”, Vitalik proposed the idea of using AI agents for prediction markets and adjudication. He believes that in large-scale applications, multi-agent systems have tremendous potential in "truth" discovery and autonomous governance. We look forward to seeing how the capabilities of such multi-agent systems can be further explored and how "collective intelligence" can demonstrate more possibilities in experiments.

Moreover, collaboration between agents and humans is also a direction worth exploring. For instance, how communities interact around agents, or how agents organize humans to accomplish collective actions. We hope to see more agent experiments aimed at large-scale human collaboration. Of course, this requires some form of verification mechanism, especially when tasks are completed off-chain. However, this exploration may yield some unexpectedly wonderful results. - Katie, Dmitriy, Ash

3. Agentic Multimedia Entertainment

The concept of digital virtual personas has existed for many years. For example, Hatsune Miku (Hatsune Miku, 2007) held sold-out concerts in venues with 20,000 seats; Lil Miquela (2016) has over 2 million followers on Instagram. Recent examples include the AI virtual streamer Neuro-sama (2022), which has surpassed 600,000 subscribers on Twitch; and the anonymous Kpop boy group PLAVE (2023), which has garnered over 300 million views on YouTube in less than two years. With advancements in AI technology and the application of blockchain in payments, value transfer, and open data platforms, these agents are expected to become more autonomous and may usher in a brand new mainstream entertainment category by 2025. - Katie, Dmitriy

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

4. Generative/Agentic Content Marketing

In some cases, agents themselves are the products, while in others, agents can complement the products. In the attention economy, continuously producing engaging content is key to the success of any idea, product, or company. Generative/agentic content provides teams with a powerful tool to ensure a scalable, round-the-clock content creation channel. This field has accelerated development due to discussions on "the difference between memecoins and agents". Agents are a powerful tool for the dissemination of memecoins, even if they have not fully achieved "agentification."

Another example is that the gaming industry is increasingly pursuing dynamism to maintain user engagement. A classic approach is to guide users to generate content, while purely generative content (such as in-game items, NPCs, or even fully generated levels) may become the next phase of this trend. We are curious to see how the capabilities of agents will further expand the boundaries of content distribution and user interaction by 2025. - Katie

5. Next-Gen Art Tools/Platforms

In 2024, we launched the IN CONVERSATION WITH series, an interview program that dialogues with crypto artists in music, visual arts, design, curation, and more. This year's interviews made me notice a trend: artists interested in crypto technology are often also passionate about cutting-edge technologies and hope these technologies can be more deeply integrated into their creative practices, such as AR/VR objects, code-generated art, and live coding.

The combination of generative art and blockchain technology has a long history, making blockchain an ideal medium for AI art. In traditional platforms, showcasing and presenting these art forms is very challenging. ArtBlocks provides a preliminary exploration of how digital art can be displayed, stored, monetized, and preserved through blockchain, greatly improving the experience for artists and audiences. Additionally, AI tools allow ordinary people to easily create their own artworks. We are very much looking forward to how blockchain will further enhance the capabilities of these tools in 2025. - Katie

KC : Since you feel frustrated and disagree with certain aspects of crypto culture, what motivates you to continue participating in Web3? What value does Web3 bring to your creative practice? Is it experimental exploration, economic returns, or something else?

MM: For me, Web3 has a positive impact on myself and other artists in multiple ways. Personally, platforms that support the release of generative art are particularly important to my creation. For example, you can upload a JavaScript file, and when someone mints or collects a piece, the code runs in real-time, generating unique artworks within the system you designed. This real-time generation process is a core part of my creative practice. Introducing randomness into the systems I write and build profoundly influences how I think about art, both conceptually and technically. However, it is often difficult to convey this process to the audience unless it is displayed on a platform specifically designed for this art form or in traditional galleries.

In galleries, an algorithm might be displayed running in real-time through projection or screens, or works selected from multiple outputs generated by the algorithm might be exhibited in some physical form. But for those audiences who are not familiar with code as an artistic medium, it can be challenging to understand the significance of randomness in this creative process, which is a crucial part of all artists' practices that use generative software. When the final presentation of a work is merely an image posted on Instagram or a printed physical piece, I sometimes find it difficult to emphasize the core concept of "code as a medium of creation" to the audience.

The emergence of NFTs excites me because it not only provides a platform for showcasing generative art but also helps popularize the concept of "code as an artistic medium," allowing more people to understand the uniqueness and value of this creative approach.

Excerpt from IN CONVERSATION WITH: Maya Man

6. Data Markets

Since Clive Humby proposed that "data is the new oil," companies have taken measures to hoard and monetize user data. However, users are gradually realizing that their data is the cornerstone of these giant companies' survival, yet they have almost no control over how their data is used and have not benefited from it. With the rapid development of powerful AI models, this contradiction has become increasingly acute. On one hand, we need to address the issue of user data being misused; on the other hand, as larger and higher-quality models exhaust the "resource" of public internet data, new data sources become particularly important.

To return control of data to users, decentralized infrastructure offers vast design space. This requires innovative solutions in various areas, including data storage, privacy protection, data quality assessment, value attribution, and monetization mechanisms. At the same time, in response to the data supply shortage, we need to think about how to leverage technological advantages to build competitive solutions, such as creating higher-value data products through better incentive mechanisms and filtering methods. Especially in the current context where Web2 AI still dominates, how to combine smart contracts with traditional service level agreements (SLA) is a direction worth exploring. - Danny

7. Decentralized Compute

In the development and deployment of AI, computational power is a key element alongside data. In recent years, large data centers have dominated the development of deep learning and AI due to their exclusive access to sites, energy, and hardware. However, this pattern is gradually being broken down due to physical resource limitations and the development of open-source technologies.

The decentralized AI compute v1 phase is similar to Web2's GPU cloud, but there is no significant advantage in hardware supply and demand. In the v2 phase, we see some teams beginning to build more complete technology stacks, including orchestration, routing, and pricing systems for high-performance computing, while developing proprietary features to attract demand and enhance inference efficiency. Some teams focus on optimizing cross-hardware inference routing through compiler frameworks, while others are developing distributed model training frameworks on their compute networks.

Additionally, an emerging market known as AI-Fi is forming, which converts computational power and GPUs into revenue-generating assets through innovative economic mechanisms or provides new avenues for hardware financing for data centers using on-chain liquidity. However, whether decentralized computing can truly realize its potential still depends on whether the gap between concepts and actual needs can be bridged. - Danny

8. Compute Accounting Standards

In decentralized high-performance computing (HPC) networks, coordinating heterogeneous computing resources is a significant challenge, and the lack of unified accounting standards complicates this issue. The outputs of AI models are diverse, such as model variants, quantization, and randomness adjusted through temperature and sampling hyperparameters. Additionally, different GPU architectures and CUDA versions can lead to discrepancies in hardware outputs. These factors make it an urgent problem to accurately account for the capacity of models and computing markets in heterogeneous distributed systems.

Due to the lack of these standards, this year we have seen multiple instances in the Web2 and Web3 computing markets where the quality and quantity of model performance and computing resources have been miscalculated. This forces users to verify the actual performance of AI systems by running their own benchmarks or limiting the usage rate of the computing market.

The crypto space has always emphasized "verifiability," so we hope that by 2025, the combination of crypto and AI will make system performance more transparent. Ordinary users should be able to easily compare the key output characteristics of models or compute clusters to audit and assess the actual performance of the systems. - Aadharsh

9. Probabilistic Privacy Primitives

Vitalik mentioned a unique contradiction in the article “The Promise and Challenges of Crypto + AI Applications”: "In cryptography, open source is the only way to achieve security, but in AI, public models (even training data) greatly increase the risk of adversarial machine learning attacks."

Although privacy protection is not a new research direction for blockchain, the rapid development of AI is accelerating the application of privacy-related cryptographic technologies. Significant progress has been made this year in privacy-enhancing technologies, such as zero-knowledge proofs (ZK), fully homomorphic encryption (FHE), trusted execution environments (TEE), and multi-party computation (MPC). These technologies are used in scenarios such as private shared states for general computation on encrypted data. At the same time, tech giants like Nvidia and Apple are leveraging proprietary TEE technology to achieve federated learning and private AI inference while maintaining consistency across hardware, firmware, and models.

In the future, we will focus on how to protect privacy in random state transitions and how these technologies can facilitate the practical application of decentralized AI in heterogeneous systems, such as decentralized private inference, encrypted data storage and access pipelines, and the construction of fully autonomous execution environments. - Aadharsh

Apple's Apple Intelligence stack and Nvidia's H100 GPU

10. Agentic Intents and Next-Gen User Trading Interfaces

An important application of AI agents is to help users autonomously complete transactions on-chain. However, over the past 12-16 months, the definitions of terms such as "agentic intent," "agent behavior," and "solvers" have remained vague, and the distinctions from traditional "robot" development have not been clear enough.

In the coming year, we look forward to seeing more complex language systems combined with various data types and neural network architectures to drive development in this field. Will agents continue to use existing on-chain systems to complete transactions, or will they develop entirely new tools and methods? Will large language models (LLMs) still serve as the core of these systems, or will they be replaced by other technologies? At the user interface level, will users interact with the system to complete transactions using natural language? Will the classic "wallet as browser" theory become a reality? These are all questions worth exploring. - Danny, Katie, Aadharsh, Dmitriy