Top 10 Crypto AI Convergence Development Trends: Agent-to-Agent Interaction, Content Marketing, Data Markets, and More
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Top 10 Crypto AI Convergence Development Trends: Agent-to-Agent Interaction, Content Marketing, Data Markets, and More
An important application of AI agents is helping users autonomously complete transactions on-chain.
Navigating Web3 tides with focused insightsAuthor: Archetype
Translation: TechFlow
1. Agent-to-Agent Interaction
Blockchains, with their inherent transparency and composability, are an ideal platform for enabling seamless interactions between agents. In this paradigm, agents developed by different entities for various purposes can collaborate to accomplish tasks. There have already been exciting early experiments—such as agents conducting peer-to-peer transfers or jointly issuing tokens. We anticipate that agent-to-agent interaction will expand further: on one hand, creating entirely new application scenarios, such as agent-driven social platforms; on the other, optimizing existing enterprise workflows, including platform verification, micropayments, and cross-platform workflow integration, thereby streamlining today’s complex and cumbersome processes. - Danny, Katie, Aadharsh, Dmitriy
aethernet and clanker jointly launching a token on Warpcast
2. Decentralized Agentic Organizations
Large-scale multi-agent collaboration is another exciting research frontier. How can multi-agent systems cooperate to complete tasks, solve problems, or even manage protocols and systems? In his early 2024 article "The Promise and Challenges of Crypto + AI Applications", Vitalik proposed using AI agents for prediction markets and dispute resolution. He believes that in large-scale applications, multi-agent systems hold significant potential in "truth discovery" and autonomous governance. We look forward to seeing how these capabilities will be further explored and how "collective intelligence" might manifest in new experimental forms.
Additionally, agent-human collaboration is another promising direction. For example, how communities form around agents, or how agents organize humans to take collective action. We hope to see more agent-based experiments aimed at large-scale human coordination. Of course, this would require robust verification mechanisms, especially when tasks are executed off-chain. But such explorations could yield unexpected and fascinating outcomes. - Katie, Dmitriy, Ash
3. Agentic Multimedia Entertainment
The concept of digital virtual personas has existed for years. Hatsune Miku (Hatsune Miku, 2007) sold out concerts in 20,000-seat arenas; Lil Miquela (2016) amassed over two million followers on Instagram. More recent examples include the AI virtual streamer Neuro-sama (2022), who has surpassed 600,000 subscribers on Twitch, and the anonymous K-pop boy group PLAVE (2023), which achieved over 300 million views on YouTube in under two years. With advances in AI technology and blockchain's role in payments, value transfer, and open data platforms, these agents are poised to become increasingly autonomous—and may launch an entirely new mainstream entertainment category in 2025. - Katie, Dmitriy
Clockwise from top left: Hatsune Miku, Luna from Virtuals, Lil Miquela, and PLAVE
4. Generative/Agentic Content Marketing
In some cases, the agent itself is the product; in others, it serves as a powerful supplement. In the attention economy, consistently producing engaging content is key to the success of any idea, product, or company. Generative and agent-driven content provides teams with a powerful tool to ensure a scalable, always-on content creation pipeline. This field has been accelerated by discussions around the distinction between memecoins and agents. Agents are becoming a powerful vehicle for memecoin propagation—even if they aren't fully "agentic" yet.
Another example lies in the gaming industry, which is increasingly pursuing dynamism to sustain user engagement. A classic approach is encouraging user-generated content, but purely generative content—such as in-game items, NPCs, or even fully generated levels—could represent the next phase of this trend. We're curious to see how, in 2025, agent capabilities will further push 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 long-form interview program featuring crypto artists working in music, visual arts, design, and curation. This year’s interviews revealed a clear trend: artists interested in cryptography are often deeply enthusiastic about cutting-edge technologies and want them more deeply integrated into their creative practices—such as AR/VR objects, code-generated art, and livecoding.
The synergy between generative art and blockchain is well-established, making blockchain a natural fit for AI art. Traditional platforms struggle to present these forms effectively. ArtBlocks pioneered ways for digital art to be displayed, stored, monetized, and preserved via blockchain, significantly enhancing the experience for both artists and audiences. Additionally, AI tools now empower ordinary users to easily create their own artworks. We’re excited to see how blockchain will further enhance these tools in 2025. - Katie
KC: Given your frustrations and disaffections with crypto culture, what motivates you to continue participating in Web3? What value does Web3 bring to your creative practice—experimental exploration, economic returns, or something else?
MM: Web3 positively impacts me personally and many other artists in multiple ways. For me, platforms that support releasing generative art are particularly important. For instance, you can upload a JavaScript file so that when someone mints or collects a piece, the code runs in real time and generates unique artwork within your designed system. This real-time generative process is central to my practice. Introducing randomness into the systems I build—both conceptually and technically—has profoundly shaped how I think about art. However, without a platform specifically designed for this art form, or when exhibiting in traditional galleries, it’s often difficult to communicate this process to viewers.
In a gallery setting, you might project an algorithm running in real time or display selected outputs transformed into physical form. But for audiences unfamiliar with code as an artistic medium, it's hard to convey the significance of randomness—a core element in every artist’s practice who uses software generatively. When the final output is just an image posted on Instagram or a printed physical piece, I sometimes struggle to emphasize the fundamental idea of “code as a creative medium.”
NFTs energized me because they not only provided a platform for showcasing generative art but also helped popularize the concept of “code as an artistic medium,” helping more people understand the uniqueness and value of this creative approach.
Excerpt from IN CONVERSATION WITH: Maya Man
6. Data Markets
Since Clive Humby coined the phrase “data is the new oil,” companies have aggressively stockpiled and monetized user data. Yet users are increasingly aware that their data forms the foundation of these tech giants’ business models, while they retain little control over its use and receive no direct benefit. As powerful AI models advance rapidly, this tension intensifies. On one hand, we must address the misuse of user data; on the other, as large-scale, high-quality models exhaust publicly available internet data—their primary “resource”—new data sources become critically important.
To return control of data to users, decentralized infrastructure offers vast design possibilities. This requires innovative solutions across data storage, privacy protection, data quality assessment, attribution of value, and monetization mechanisms. At the same time, to address data supply shortages, we must leverage technological advantages to build competitive solutions—such as better incentive structures and filtering methods—to produce higher-value data products. Especially in today’s landscape where Web2 AI still dominates, integrating smart contracts with traditional service-level agreements (SLAs) is a direction worth exploring. - Danny
7. Decentralized Compute
Beyond data, compute power is equally crucial in AI development and deployment. Over the past few years, large data centers have dominated deep learning and AI progress by monopolizing access to physical sites, energy, and hardware. However, this dominance is gradually eroding due to physical constraints and the rise of open-source technologies.
The first version (v1) of decentralized AI compute resembles Web2 GPU clouds but lacks clear advantages in hardware supply or demand. In v2, we see teams building more sophisticated technical stacks—including orchestration, routing, and pricing systems for high-performance computing—while developing proprietary features to attract demand and improve inference efficiency. Some focus on optimizing inference routing across hardware via compiler frameworks, while others build distributed model training frameworks atop their compute networks.
Additionally, an emerging market called AI-Fi is forming, using novel economic mechanisms to turn compute power and GPUs into income-generating assets, or leveraging on-chain liquidity to finance hardware for data centers. However, whether decentralized compute can fulfill its potential ultimately depends on closing the gap between vision and real-world demand. - Danny
8. Compute Accounting Standards
In decentralized high-performance computing (HPC) networks, coordinating heterogeneous compute resources is a major challenge—one exacerbated by the lack of unified accounting standards. AI model outputs vary widely: model variants, quantization, randomness tuned via temperature and sampling hyperparameters, etc. Moreover, differences in GPU architectures and CUDA versions lead to divergent hardware outputs. These factors make accurate measurement of capacity and performance in heterogeneous distributed systems a pressing issue.
Due to this lack of standards, throughout this year we've repeatedly seen misreporting of model performance and compute resource quality and quantity in both Web2 and Web3 compute markets. This forces users to run their own benchmarks or limit usage rates to verify actual AI system performance.
Cryptography has always emphasized "verifiability," so we hope that by 2025, the convergence of crypto and AI will make system performance more transparent. Ordinary users should be able to easily compare key output characteristics of models or compute clusters, enabling effective auditing and evaluation of real-world performance. - Aadharsh
9. Probabilistic Privacy Primitives
Vitalik highlighted a unique paradox in his article "The Promise and Challenges of Crypto + AI Applications": "In cryptography, open source is the only path to security, but in AI, revealing models—or even training data—greatly increases vulnerability to adversarial machine learning attacks."
While privacy is not a new research area in blockchain, privacy-enhancing cryptographic techniques are accelerating due to rapid AI advancements. Significant progress has been made this year in technologies like zero-knowledge proofs (ZK), fully homomorphic encryption (FHE), trusted execution environments (TEE), and multi-party computation (MPC). These are being applied to scenarios such as general-purpose computation on encrypted data and private shared states. Meanwhile, tech giants like Nvidia and Apple are leveraging proprietary TEEs to enable federated learning and private AI inference, all while maintaining consistency across hardware, firmware, and models.
Looking ahead, we will focus on how to preserve privacy during stochastic state transitions, and how these technologies can enable practical decentralized AI applications on heterogeneous systems—such as decentralized private inference, secure pipelines for encrypted data storage and access, 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
A key application of AI agents is helping users autonomously execute transactions on-chain. However, over the past 12–16 months, terms like "agentic intent," "agent behavior," and "solvers" have remained poorly defined, with unclear distinctions from traditional "bot" development.
In the coming year, we expect more sophisticated language systems combining diverse data types and neural network architectures to drive progress in this space. Will agents continue using existing on-chain systems for transactions, or will they develop entirely new tools and methods? Will large language models (LLMs) remain central, or will they be replaced by other technologies? At the user interface level, will users interact via natural language to complete trades? Will the classic notion of "wallet as browser" finally materialize? These are all compelling questions worth exploring. - Danny, Katie, Aadharsh, Dmitriy
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