Inventory of the top 10 integration development directions of Crypto AI: intelligent inter-vehicle interaction, content marketing and data market, etc.

Author: Archetype

Compilation: DeepTechFlow

1. Agent-to-Agent Interaction

Blockchain, due to its inherent transparency and composability, has become an ideal platform for seamless interaction between intelligent entities. In this interaction, intelligent entities developed by different institutions for different purposes can collaborate to complete tasks. Exciting attempts have been made, such as mutual transfers between intelligent entities and joint issuance of tokens. We look forward to further expanding the interaction between intelligent entities: on the one hand, creating new application scenarios, such as intelligent agent-driven social platforms; on the other hand, optimizing existing enterprise workflows, such as platform authentication, micro payments, cross-platform workflow integration, etc., thereby simplifying complex and cumbersome operation processes today. - Danny, Katie, Aadharsh, Dmitriy

aethernet and clanker jointly issue Token on Warpcast

2. Decentralized Agentic Organizations

Large-scale multi-agent collaboration is another exciting research direction. How do multi-agent systems coordinate to complete tasks, solve problems, and even manage protocols and systems? In his article 'Promises and Challenges of Cryptocurrency + AI Applications' in early 2024, Vitalik proposed the idea of using AI agents for market prediction and arbitration. He believes that in large-scale applications, multi-agent systems have great potential in 'truth' discovery and autonomous governance. We look forward to seeing how the capabilities of such multi-agent systems are further explored and how 'collective intelligence' can demonstrate more possibilities in experiments.

In addition, collaboration between intelligent agents and humans is also a direction worth exploring. For example, how communities interact around intelligent agents, or how intelligent agents organize humans to complete collective actions. We hope to see more intelligent agent experiments aimed at large-scale human collaboration. Of course, this requires some kind of verification mechanism, especially when tasks are completed off-chain. But such exploration may bring some unexpected and wonderful results. - Katie, Dmitriy, Ash

3. Agentic Multimedia Entertainment

The concept of digital virtual personalities has existed for many years. For example, Hatsune Miku (2007) held a sold-out concert in a 20,000-seat venue, and Lil Miquela (2016) has over 2 million fans on Instagram. Recent examples include AI virtual streamer Neuro-sama (2022), whose Twitch subscriptions have exceeded 600,000, and the anonymous Kpop boy band PLAVE (2023), whose YouTube views have surpassed 300 million in less than two years. With the advancement of AI technology and the application of blockchain in payment, value transfer, and open data platforms, these intelligent entities are expected to become more autonomous and may open up a new mainstream entertainment category in 2025. - Katie, Dmitriy

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

4. Generative/Agentic Content Marketing

In some cases, the intelligent agent itself is the product, while in other cases, the intelligent agent can complement the product. In the attention economy, continuously generating compelling content is the key to the success of any idea, product, or company. Generative/agent-driven content provides teams with a powerful tool to ensure a scalable, round-the-clock content creation channel. This field has accelerated development due to the discussion of the difference between memecoins and intelligent agents. Intelligent agents are powerful tools for memecoins to achieve dissemination, even if they have not yet fully achieved 'intelligentization'.

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

5. Next-Gen Art Tools/Platforms

In 2024, we launched the IN CONVERSATION WITH series, a talk show that interviews encrypted artists in the fields of music, visual arts, design, curation, etc. This year's interviews have led me to notice a trend: artists interested in encryption technology are often passionate about cutting-edge technology and hope that these technologies can be more deeply integrated into their creative practices, such as AR/VR objects, code-generated art, and livecoding.

The combination of Generative Art and blockchain technology has a long history, making blockchain an ideal carrier for AI art. In traditional platforms, it is very difficult to display and present these art forms. ArtBlocks provides a preliminary exploration of how digital art can be displayed, stored, monetized, and preserved through blockchain, greatly improving the experience of artists and viewers. In addition, AI tools also enable 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: What motivates you to continue participating in Web3 despite feeling frustrated and having disagreements with the crypto culture? What value does Web3 bring to your creative practice? Is it experimental exploration, economic returns, or other aspects?

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

In a gallery, an algorithm may be displayed running in real time through projection or screen, or a selection of outputs generated by the algorithm may be exhibited in some way transformed into physical form. However, for audiences unfamiliar with code as an artistic medium, it is difficult for them to understand the significance of randomness in the creative process, which is an important part of all artists' practice using software in a generative way. When the final presentation of the work is only an image posted on Instagram or a printed physical work, I sometimes find it difficult to emphasize to the audience the core concept of 'code as a creative medium' in the work.

The emergence of NFTs has excited 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 the viewpoint that "Data is the new oil", enterprises have taken measures to hoard and monetize user data. However, users are gradually realizing that their data is the cornerstone on which these tech giants rely for survival, yet they have almost no control over how the data is used, nor do they benefit from it. With the rapid development of powerful AI models, this contradiction has become even more acute. On the one hand, we need to address the problem of misuse of user data; on the other hand, as larger-scale, higher-quality models deplete the "resource" of public internet data, new sources of data are becoming increasingly important.

In order to return the control of data to users, decentralized infrastructure provides a broad design space. This requires innovative solutions in multiple areas such as data storage, privacy protection, data quality assessment, value ownership, and monetization mechanisms. At the same time, in response to the shortage of data supply, we need to consider how to use technological advantages to build competitive solutions, such as creating higher-value data products through better incentive mechanisms and filtering methods. Especially in the current dominance of Web2 AI, it is worth exploring how to combine smart contracts with traditional service level agreements (SLAs). - Danny

7.去中心化计算 (Decentralized Compute)

In the development and deployment of AI, computing power is as important as data. In the past few years, large data centers have dominated the development of deep learning and AI by relying on exclusive access to sites, energy, and hardware. However, as physical resources become limited and open source technologies develop, this pattern is gradually being broken.

The decentralized AI computing in v1 is similar to the GPU cloud of Web2, but there is no obvious advantage in hardware supply and demand. In v2, we see some teams starting to build a more complete technical stack, including high-performance computing orchestration, routing, and pricing systems, while developing proprietary features to attract demand and improve inference efficiency. Some teams focus on optimizing cross-hardware inference routing through compiler frameworks, while others develop distributed model training frameworks on their computing network.

In addition, a new market called AI-Fi is forming, which transforms computing power and GPUs into income-generating assets through innovative economic mechanisms, or provides new ways of 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 the concept and actual demand can be bridged. - Danny

8. Compute Accounting Standards

In decentralized high-performance computing (HPC) networks, how to coordinate heterogeneous computing resources is an important challenge, and the lack of unified accounting standards makes this problem even more complex. The output results of AI models are diverse, such as model variants, quantization, randomness adjusted through temperature and sampling hyperparameters, and so on. In addition, different GPU architectures and CUDA versions can also lead to differences in hardware output results. These factors make it an urgent problem to accurately measure the capacity of models and computing markets in heterogeneous distributed systems.

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

The encryption field has always emphasized 'verifiability', so we hope that by 2025, the combination of encryption and AI can make the system performance more transparent. Ordinary users should be able to easily compare the key output characteristics of models or computing clusters, thereby auditing and evaluating the actual performance of the system. - Aadharsh

9. Probabilistic Privacy Primitives

Vitalik mentioned a unique contradiction in the article 'The Promise and Challenge of Cryptography + 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 in blockchain, with the rapid development of AI, privacy-related cryptographic technologies are accelerating their application. Significant progress has been made in privacy-enhancing technologies this year, 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 universal computation on encrypted data. At the same time, tech giants such as Nvidia and Apple are also 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 promote the practical application of decentralized AI on heterogeneous systems, such as decentralized private inference, storage and access pipelines for encrypted data, 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 the chain. However, in the past 12-16 months, the definitions of terms such as 'agent intent', 'agent behavior', and 'solver' have always been ambiguous, and the difference from the development of traditional 'robots' is not clear enough.

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