Crypto x AI: 10 areas to watch in 2025

Original text: Archetype

Compiled by: Yuliya, PANews

Today, with the rapid development of artificial intelligence and blockchain technology, the intersection of these two fields is giving rise to exciting innovation possibilities. This article provides an in-depth analysis of the ten important areas worthy of attention in 2025, from the interaction of intelligent agents to decentralized computing, from changes in the data market to breakthroughs in privacy technology.

1. Interaction between agents

The inherent transparency and composability characteristics of blockchain make it an ideal base layer for interactions between agents. Intelligent agents developed by different entities and serving different purposes can interact seamlessly on the blockchain. There have been some eye-catching experimental applications, such as fund transfers between agents and joint issuance of tokens.

The development potential of future agent interaction is mainly reflected in two aspects: first, creating new application fields, such as new social scenarios driven by agent interaction; second, optimizing existing enterprise-level workflows, including platform certification and verification, small payment, cross-platform workflow integration and other traditionally cumbersome links.

aethernet and clanker implement joint currency issuance on Warpcast platform

2. Decentralized intelligent agent organization

Large-scale multi-agent coordination is another exciting area of ​​research. This involves how multi-agent systems work together to complete tasks, solve problems, and govern systems and protocols. In the article "Prospects and Challenges of Cryptocurrency and AI Applications" published in early 2024, Vitalik mentioned the possibility of using AI agents in the fields of prediction markets and arbitration. He believes that from a macro perspective, multi-agent systems show significant potential for "truth" discovery and autonomous governance systems.

The industry is continuing to explore and experiment with the capabilities boundaries of multi-agent systems and various forms of "swarm intelligence." As an extension of inter-agent coordination, 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 carry out collective actions.

The researchers are particularly interested in agent experiments where the objective function involves large-scale human coordination. Such applications need to be equipped with corresponding verification mechanisms, especially when human work is completed off-chain. This human-machine collaboration may lead to some unique and interesting emergent behaviors.

3. Intelligent agent multimedia entertainment

The concept of digital personality has been around for decades.

• As early as 2007, Hatsune Miku was able to hold a sold-out concert in a venue with 20,000 people;
• Lil Miquela, a virtual internet celebrity born in 2016, has more than 2 million fans on Instagram.
• Neuro-sama, an AI virtual anchor launched in 2022, has accumulated more than 600,000 subscribers on the Twitch platform;
• PLAVE, a virtual Korean group founded in 2023, has received more than 300 million views on YouTube in less than two years.

With advances in AI infrastructure and the integrated application of blockchain in payments, value transfer, and open data platforms, these intelligent agents are expected to achieve a higher degree of autonomy by 2025 and may usher in a new mainstream entertainment category.

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

4. Generative/Intelligent Agency Content Marketing

Unlike the case where the intelligent agent itself is a product as mentioned above, the intelligent agent can also be used as a complementary tool to the product. In today’s attention economy, consistently outputting engaging content is critical to the success of any idea, product, or company. Generative/intelligent proxy content is becoming a powerful tool for teams to ensure 24/7 uninterrupted content production.

Development in this area has been accelerated by discussions about the boundaries between meme coins and intelligent agents. Even if "intelligence" has not yet been fully realized, intelligent agents have become a powerful means for Meme coins to spread.

The gaming field provides another typical example. Modern games increasingly need to remain dynamic to maintain user engagement. Traditionally, cultivating user-generated content (UGC) is a classic way to create dynamics in games. Purely generative content (including in-game items, NPC characters, fully generated levels, etc.) may represent the next stage in this evolution. Looking forward 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 interviews artists active in the cryptocurrency field and its fringes in fields such as music, visual art, design and curation. These interviews revealed an important observation: artists interested in cryptocurrencies also tend to focus on broader cutting-edge technologies, and tend to integrate these technologies deeply into the aesthetics or core of their artistic practice, such as AR/VR objects, code-based art and the art of real-time programming.

There have always been synergies between generative art and blockchain technology, making its potential as AI art infrastructure even more obvious. It is extremely difficult to properly display these new art media on traditional display platforms. The ArtBlocks platform demonstrates the future of digital art display, storage, monetization and preservation using blockchain technology, significantly improving the overall experience for artists and audiences.

In addition to display capabilities, AI tools expand the ability of the general public to create art. This democratization trend is reshaping the landscape of artistic creation. Looking forward to 2025, how blockchain technology will expand or empower these tools will be a very attractive development direction.

Excerpted from "Conversation: Maya Man"

6. Data market

It has been 20 years since Clive Humby put forward the statement that "data is the new oil". Major companies have been taking strong measures to hoard and monetize user data. Users have realized that their data is the cornerstone of these multi-billion dollar companies, but they have little control over how the data is used and no share in the profits it creates. With the rapid development of powerful AI models, this contradiction has become more prominent.

The opportunities facing the data market are twofold: one is to solve the problem of user data being exploited, and the other is to solve the problem of data supply shortage, because bigger and better models are consuming the easily accessible "oil field" of public Internet data. , and new data sources are needed.

Data power returns to users

The question of how to leverage decentralized infrastructure to return data power to users is a vast design space that requires innovative solutions in multiple areas. Some of the most pressing issues include:

• Where data is stored and how privacy is protected during storage, transmission and computation;
• How to objectively assess, screen and measure data quality;
• What mechanisms are used for attribution and monetization (especially tracing value back to the source after inference);
• and what orchestration or data retrieval systems are used across the diverse model ecosystem.

supply restrictions

When it comes to solving supply constraints, the key is not to simply copy Scale AI’s model with tokens, but to understand where we can build advantage when the technology is favorable, and how to build solutions that have a competitive advantage, whether in Scale, quality, or better incentive (and screening) mechanisms to create higher value data products. Especially when most of the demand still comes from Web2 AI, thinking about how to combine smart contract execution mechanisms with traditional service level agreements (SLAs) and tooling is an important area of ​​research.

7. Decentralized computing

If data is an essential element of AI development and deployment, then computing power is another key component. The traditional large-scale data center model has largely dominated the development trajectory of deep learning and AI in the past few years due to its unique space, energy and hardware advantages. However, physical limitations and the development of open source technologies are challenging this landscape.

• The first stage (v1) of decentralized AI computing is essentially a replica of the Web2 GPU cloud service, with no real advantages on the supply side (hardware or data centers) and limited organic demand.
• In the second phase (v2) , some outstanding teams built a complete technology stack based on heterogeneous high-performance computing (HPC) provision, demonstrating unique capabilities in scheduling, routing and pricing, while developing proprietary capabilities to attract demand and respond to margin compression, particularly on the reasoning side. Various teams have also begun to diverge in usage scenarios and market strategies. Some focus on integrating compiler frameworks to achieve efficient inference routing across hardware, while others create distributed model training frameworks on the computing networks they build.

The industry is even beginning to see the rise of the AI-Fi market, with the emergence of innovative economic primitives that convert computing power and GPUs into income-generating assets, or use on-chain liquidity to provide data centers with alternative sources of funding for purchasing hardware.

The main question here is to what extent decentralized AI will be developed and deployed on decentralized computing infrastructure, or whether, like in storage, the gap between ideal and actual needs will always exist, making this idea Difficulty realizing its full potential.

8. Calculation and accounting standards

In terms of the incentive mechanism of decentralized high-performance computing networks, a major challenge in coordinating heterogeneous computing resources is the lack of unified computing accounting standards. AI models add several unique complexities to the output space of high-performance computing, including model variants, quantization schemes, and levels of randomness that are tunable through model temperature and sampling hyperparameters. In addition, AI hardware will also produce different output results due to differences in GPU architecture and CUDA versions. These factors ultimately lead to the need for standards to standardize how models and computing markets measure computing power in heterogeneous distributed systems.

Partly due to the lack of these standards, 2024 saw multiple cases in the Web2 and Web3 space where the model and computing market failed to accurately account for the quality and quantity of their computing. This results in users having to audit the true performance of these AI layers by running their own comparative model benchmarks and performing proof-of-work by limiting the rate at which they compute the market.

Looking forward to 2025, the intersection of encryption technology and AI is expected to achieve a breakthrough in verifiability, which will be easier to verify than traditional AI. It is critical for the average user to be able to make fair comparisons of all aspects that define the output of a model or computing cluster, which will aid in auditing and evaluating system performance.

9. Probabilistic privacy primitives

In "Prospects and Challenges of Cryptocurrency and AI Applications," Vitalik points out a unique challenge when connecting cryptocurrencies and AI: ' "In cryptography, open source is the only way to achieve true security, but in AI, The openness of a model (and even its training data) greatly increases its risk of adversarial machine learning attacks.”

Although privacy is not a new area of ​​blockchain research, the rapid development of AI is accelerating the research and application of cryptographic primitives that support privacy. 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 for encryption Common application scenarios such as private shared state of data calculations. At the same time, centralized AI giants like Nvidia and Apple are also using proprietary TEEs for federated learning and private AI inference, ensuring privacy while keeping hardware, firmware, and models consistent across systems.

Based on these developments, the industry is paying close attention to the progress of privacy maintenance technologies in random state transitions, and how these technologies can accelerate the actual implementation of decentralized AI applications on heterogeneous systems. This includes everything from decentralized private inference, to storage/access pipelines for encrypted data, and fully sovereign execution environments.

Apple’s AI technology stack and Nvidia’s H100 graphics processor

10. Agent intent and the next generation of user transaction interfaces

Over the past 12-16 months, there has been ambiguity around the definitions of concepts such as intent, agent behavior, agent intention, solution, agent solution, and how these concepts differ from traditional "robot" development in recent years. Lack of clear definition. AI agents' autonomous on-chain transactions are one of the application scenarios closest to implementation.

Over the next 12 months, the industry expects 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 the agent use existing on-chain trading systems, or develop its own tools and methods?
• Will large language models continue to be the backend of these agent trading systems, or will entirely new systems emerge?
• At the interface level, will users start using natural language to conduct transactions?
• Will the classic "wallet as browser" concept finally come true?

The answers to these questions will profoundly impact the future direction of cryptocurrency trading. As AI technology advances, agent systems may become more intelligent and autonomous, able to better understand and execute user intentions.