Original author: Archetype
Original translation: TechFlow
1. Agent-to-Agent Interaction
Blockchain, with its natural transparency and composability, is an ideal platform for seamless interaction between agents. In this interaction, agents developed by different institutions for different purposes can collaborate to complete tasks. There are already some exciting attempts, such as transferring money between agents and issuing tokens together . We look forward to further expansion of the interaction between agents: on the one hand, creating new application scenarios, such as new social platforms driven by agents ; on the other hand, optimizing existing enterprise workflows , such as platform authentication, micropayments, cross-platform workflow integration, etc., thereby simplifying todays complex and cumbersome operational processes. - Danny , Katie , Aadharsh , Dmitriy
Aethernet and Clanker jointly issue Tokens on Warpcast
2. Decentralized Agentic Organizations
Large-scale multi-agent collaboration is another exciting research direction. How do multi-agent systems work together to complete tasks, solve problems, and even manage protocols and systems? In the article The Promise and Challenges of Crypto + AI Applications in early 2024, Vitalik proposed the idea of using AI agents to predict markets and make decisions. 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 this multi-agent system can be further explored and how swarm intelligence can show more possibilities in experiments.
In addition, collaboration between intelligent agents and humans is also a direction worth exploring. For example, how can communities interact around intelligent agents, or how can 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 the task is completed off-chain. But this exploration may bring some unexpected and wonderful results. - Katie , Dmitriy , Ash
3. Agent-driven Multimedia Entertainment
The concept of digitized virtual personalities has been around for years. For example, Hatsune Miku (2007) has sold out concerts in 20,000-seat arenas, and Lil Miquela (2016) has over 2 million followers on Instagram. Recent examples include AI virtual streamer Neuro-sama (2022), which has over 600,000 subscribers on Twitch, and anonymous K-pop boy band PLAVE (2023), which has amassed over 300 million views on YouTube in less than two years. As AI technology advances and blockchain is used in payments, value transfer, and open data platforms, these agents are expected to become more autonomous and could usher in a whole new category of mainstream entertainment by 2025. - Katie , Dmitriy
Clockwise from top left: Hatsune Miku, Virtuals Luna, Lil Miquela, and PLAVE
4. Generative/Agentic Content Marketing
In some cases, agents are the product themselves, while in other cases, agents can complement the product. In the attention economy, a consistent output of engaging content is key to the success of any idea, product, or company. Generative/agent-driven content provides teams with a powerful tool to ensure a scalable, 24/7 content creation pipeline. This area has been accelerated by the discussion of the topic The difference between memecoin and agents . Agents are a powerful tool for memecoin to spread, even if they are not yet fully agentized.
As another example, the gaming industry is increasingly looking to dynamism to keep users engaged . A classic approach is to guide users to generate content, but purely generative content (such as in-game items, NPCs, and even fully generated levels) may be the next stage of this trend. We are curious to see how the capabilities of agents 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 with crypto artists in the fields of music, visual arts, design, curation, etc. This years interviews have made me notice a trend: artists interested in crypto are often also passionate about cutting-edge technologies and hope that these technologies can be more deeply integrated into their creative practice, such as AR/VR objects, code-generated art, and livecoding.
Generative Art and blockchain technology have a long history, making blockchain an ideal vehicle 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 audiences. In addition, AI tools make it easy for ordinary people to create their own works of art . We are very much looking forward to how blockchain will further enhance the capabilities of these tools in 2025. - Katie
KC: Given your frustrations and disagreements with crypto culture, what motivates you to still participate in Web3? What value does Web3 bring to your creative practice? Is it experimental exploration, financial rewards, or something else?
MM: For me, Web3 has a positive impact on me and other artists in multiple ways. For me personally, platforms that support publishing generative art are particularly important to my practice. For example, you can upload a JavaScript file, and when someone mints or collects a piece, the code will run in real time and generate a unique art work in the system you designed. This real-time generation process is a core part of my practice. Introducing randomness into the systems I write and build has deeply influenced the way I think about art, both conceptually and technically. However, it is often difficult to communicate this process to the audience if it is not displayed on a platform designed specifically for this art form, or in a traditional gallery.
In a gallery, you might show an algorithm running in real time via a projection or screen, or a selection of multiple outputs generated by an algorithm that are somehow transformed into physical form for exhibition. But for viewers who are not familiar with code as an artistic medium, it can be hard to understand the significance of this randomness in the creative process, which is an important part of the practice of all artists who use software in a generative way. I sometimes find it difficult to emphasize this core idea of code as a creative medium when the final presentation of the work is just a picture posted on Instagram or a physical print.
I am excited about the emergence of NFT because it not only provides a platform for displaying 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 way of creation.
Excerpted from IN CONVERSATION WITH: Maya Man
6. Data Markets
Ever since Clive Humby proposed the idea that data is the new oil, companies have taken steps to hoard and monetize user data. However, users have gradually realized that their data is the cornerstone of the survival of these giant companies, but they have little control over how their data is used and have not been able to benefit from it. With the rapid development of powerful AI models, this contradiction has become more acute. On the one hand, we need to solve the problem of user data being abused; on the other hand, as larger and higher-quality models exhaust the resource of public Internet data, new sources of data have become particularly important.
In order to return the control of data to users, decentralized infrastructure provides a broad design space. This requires innovative solutions in many areas such as data storage, privacy protection, data quality assessment, value attribution and monetization mechanisms. At the same time, in response to the shortage of data supply, we need to think about 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 context where Web2 AI still dominates, how to combine smart contracts with traditional service agreements (SLAs) is a direction worthy of in-depth exploration. - Danny
7. Decentralized Compute
In the development and deployment of AI, computing power is also a key factor in addition to 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, with the limitations of physical resources and the development of open source technology, this pattern is gradually being broken.
The v1 stage of decentralized AI computing is similar to the GPU cloud of Web2, but it has no obvious advantages in hardware supply and demand. In the v2 stage, we see some teams starting to build a more complete technology stack, including orchestration, routing, and pricing systems for high-performance computing, while developing proprietary features to attract demand and improve reasoning efficiency. Some teams focus on optimizing reasoning routing across hardware through compiler frameworks, while others develop distributed model training frameworks on their computing networks.
In addition, an emerging market called AI-Fi is emerging, which uses innovative economic mechanisms to transform computing power and GPUs into income assets, or use on-chain liquidity to provide new ways to finance hardware for data centers. However, whether decentralized computing can truly realize its potential still depends on whether the gap between ideas and actual needs 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 current lack of a unified accounting standard makes this problem more complicated. The output results of AI models are diverse, such as model variants, quantization, randomness adjusted by temperature and sampling hyperparameters, etc. In addition, different GPU architectures and CUDA versions will also lead to differences in hardware output results. These factors make it an urgent problem to accurately count 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 model performance and the quality and quantity of compute resources were miscalculated in Web2 and Web3 compute markets. This has forced users to verify the actual performance of AI systems by running their own benchmarks or limiting the rate at which compute markets are used.
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 key output characteristics of models or computing clusters to audit and evaluate the actual performance of the system. - Aadharsh
9. Probabilistic Privacy Primitives
Vitalik mentioned a unique contradiction in his 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 their risk of adversarial machine learning attacks.
Although privacy protection is not a new research direction for blockchain, with the rapid development of AI, privacy-related cryptographic technologies are being applied at an accelerated pace. Significant progress has been made this year in privacy-enhancing technologies, such as zero-knowledge proof (ZK), fully homomorphic encryption (FHE), trusted execution environment (TEE), and multi-party computing (MPC). These technologies are used in scenarios such as private shared states for general computing on encrypted data. At the same time, technology giants such as Nvidia and Apple are also using proprietary TEE technology to achieve federated learning and private AI reasoning while keeping hardware, firmware, and models consistent.
In the future, we will focus on how to preserve privacy in random state transitions and how these techniques can facilitate the practical application of decentralized AI on heterogeneous systems, such as decentralized private reasoning, encrypted data storage and access pipelines, and the construction of fully autonomous execution environments. - Aadharsh
Apples Apple Intelligence stack and Nvidias H100 GPU
10. Agentic Intents and Next-Gen User Trading Interfaces
An important application of AI agents is to help users complete transactions autonomously 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 vague, and the distinction from traditional robot development is not clear enough.
In the coming year, we expect to see more complex language systems combined with multiple data types and neural network architectures to advance this field. Will agents continue to use existing on-chain systems to complete transactions, or will they develop completely new tools and methods? Will large language models (LLMs) remain 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 browser theory become a reality? These are all questions worth exploring. - Danny , Katie , Aadharsh , Dmitriy
