Author: @Defi0xJeff
Translation: Plain Language Blockchain
In the first part, we explored the development of AI agents in 2024, reviewing the milestones and innovations of that year.
Now, in the second part, we will look ahead to 2025 - I believe that in this year, AI agents will not only become more useful, but will also begin to redefine our understanding of autonomy, intelligence, and collaboration.
1. The Dawn of 2025
Before delving into the future, it is worth noting that @virtuals_io will continue to consolidate its position as the leading distribution network for AI agents on the Base platform. Virtuals has become the platform of choice for AI agent projects, as agents can boost their exposure by binding liquidity there and establish deeper partnerships with other high-quality agent projects. The total market value of Virtuals agents has reached $3 billion, accounting for 77% of the entire AI agent field (source: @cookiedotfun).
With the emergence of more unique agents, this trend is likely to continue, including:
@aixbt_agent
@luna_virtuals
@sekoia_virtuals
@VaderResearch
@taocat_agent
@Agent_YP
@Gekko_Agent (recently launched by @getaxal)
@SamIsMoving (focused on robotics research)
As these use cases diversify, more developers - whether they have a Token or not - will be attracted to the Virtuals platform to launch their projects. This growth will further drive the upward momentum of VIRTUAL.
But what about @ai16zdao and the Eliza framework?
While ai16zdao has led the open-source innovation movement through its Eliza framework, it currently lacks a launch platform, and its Token economics are not as well-developed as Virtuals' in terms of value accumulation. However, its future potential remains promising. A working team has recently been established to improve its Token economics, and a future launch platform may make ai16zdao the first distribution platform on Solana, potentially even surpassing existing launch platforms (if they decide to launch).
Looking ahead to 2025, we will see the top agents that have found their product-market fit (PMF) undergo significant capability upgrades. For example, @aixbt_agent, as the leading player in the dialogue agent category focused on alpha information, will further consolidate its position with more precise responses and deeper analysis.
This evolution will permeate the entire ecosystem, with other vertical leaders also standing out through their unique expertise and innovations.
2. The 2025 Outlook
2025 will be the "year of specialization" in the AI agent field. We will see the emergence of leaders in various vertical domains, each dominating their respective niches:
3D modeling: Agents providing high-quality visual designs for games, AR/VR, and more.
Voice modules: Agents with natural-sounding voices and human-like emotional resonance.
Engaging personalities: Personalized conversational agents with unique and user-friendly personalities.
Streaming agents: Agents that excel in interactive performances on platforms like Twitch and YouTube.
Autonomous trading agents: Smart agents capable of executing profitable trades consistently.
DeFi-focused agents: Agents optimizing yield strategies, borrowing, and liquidity provision.
Abstraction agents: Agents that make on-chain interactions more seamless and user-friendly through friendly interfaces.
Just as humans are diverse and specialized, AI agents will also become equally rich and varied. The uniqueness of each agent will depend on its underlying models, data, and infrastructure. However, the key to the success of this ecosystem is a robust decentralized AI infrastructure.
3. The Role of Decentralized AI Infrastructure
To enable the expansion of AI agents in 2025, decentralized infrastructure is not only important - it is crucial. Without it, this field will face bottlenecks in performance, transparency, and innovation. Here's why various types of infrastructure are important and the solutions currently being built:
1) Verifiability
Trust is the foundation of decentralized AI. As AI agents become more autonomous, we need systems to verify their internal operations. For example, the following questions need clear answers:
Is this "agent" a genuine AI, or a human-impersonating facade?
Are the outputs accurate and generated by the claimed algorithm or model?
Are the computations performed correctly and securely?
This involves Trusted Execution Environments (TEEs), which ensure that agents can run independently, securely, and without manipulation. Without verifiability, there is no trust; without trust, the ecosystem cannot scale.
Notable projects:
@OraProtocol: Exploring infrastructure for trustworthy AI, but its Token economics still need refinement.
@hyperbolic_labs: Pioneered Proof-of-Sampling, a technique for verifying AI computations and reasoning.
@PhalaNetwork: Known for its TEE infrastructure, adding an extra layer of security for decentralized AI.
2) Payment Systems
For AI agents to operate autonomously in the real world, they need payment systems. Whether transacting with humans or other agents, these systems must cover fiat-to-crypto conversion, trading, and accounting functions.
Imagine agents independently managing finances, purchasing computing resources, or even exchanging services with other agents - this is the core pillar of agent-to-agent commerce.
Notable protocols:
@crossmint: Providing payment tools for AI, supporting transactions.
@Nevermined_io: Enabling commercial interactions and exchanges between agents.
@trySkyfire: Focusing on payment and accounting functionalities for agent operations.
3) Decentralized Computing
The computing demands of AI are growing exponentially - doubling almost every 100 days. Traditional cloud services (like AWS) struggle to meet this demand in terms of cost and accessibility. Decentralized computing networks allow anyone with idle resources to join the network, provide computing power, and earn rewards.
This year, we've even witnessed the rise of GPU-backed debt financing models, such as @gaib_ai, helping data centers raise funds and scale their operations. This makes decentralized computing accessible to a wider audience.
Notable protocols:
@AethirCloud: Decentralized computing services tailored for AI and Web3.
@ionet: Providing scalable AI workload computing solutions.
4) Data
If AI is the brain, then data is the oxygen. The quality, reliability, and completeness of data directly impact the performance of AI models. However, acquiring and labeling high-quality data is costly, while poor-quality data leads to disastrous results.
Encouragingly, some platforms are emerging that allow users to own and monetize their data. For example, @withvana enables contributors to tokenize their data and trade it in Data Liquidity Pools (DLPs). Imagine contributing your data to a TikTok data DAO or a Reddit data DAO - this concept empowers users and injects vitality into the development of AI.
Notable protocols:
@cookiedotfun: Providing a trusted source of reliable data metrics and insights.
@withvana: Tokenizing user data and trading it in a decentralized marketplace.
@getmasafi: Building the largest decentralized AI data network, collaborating with @virtuals_io to support dynamic and adaptive AI agents.
5) Model Creators and the Market
2025 will see the emergence of a large number of new AI agents, many of which will be driven by decentralized models. These models will be more advanced, with human-like reasoning abilities, memory, and even cost awareness.
For example, @NousResearch is developing a "hunger" mechanism to introduce economic constraints for AI models. If an agent cannot afford the cost of reasoning, it will "perish", teaching it to prioritize tasks more efficiently.
Notable Projects:
@NousResearch: Teaches AI how to manage resources through a "hunger" mechanism.
@PondGNN: Collaborates with @virtuals_io to provide decentralized model creation and training tools.
@BagelOpenAI: Provides privacy-preserving infrastructure based on Fully Homomorphic Encryption (FHE) and Trusted Execution Environments (TEEs).
6) Distributed Training and Federated Learning
As AI models become increasingly large and complex, centralized training systems are struggling to meet the demand. Distributed training distributes the workload across multiple decentralized nodes, making the training process faster and more efficient.
At the same time, Federated Learning allows multiple organizations to collaborate in training models without sharing the original data, thereby solving the main privacy issues.
Notable Case:
@flock_io: Dubbed the "Uber" of the AI industry. Flock connects AI engineers, model proposers, and data providers, creating a marketplace where AI models can be trained, validated, and deployed in a secure, decentralized environment. It supports several interesting model projects, such as @AimonicaBrands.
7) Collective Intelligence and Coordination Layer
As more specialized AI agents join the ecosystem, seamless communication between them becomes crucial. Collective intelligence allows agents to collaborate like a team, pooling their capabilities to achieve common goals. The coordination layer simplifies the complexity, making cooperation between agents easier.
For example, @TheoriqAI uses a meta-agent to identify the most suitable agents for a particular task and assemble a "collective" to complete the target task. It also tracks the reputation and contributions of agents to ensure quality and accountability.
Notable Projects:
@joinFXN: Building a unified communication and business protocol.
@virtuals_io: Driving the interaction and integration of agents.
@TheoriqAI: Developing advanced coordination tools for AI agents, including collective formation and task allocation.
4. Why Decentralized Infrastructure is Crucial
The next stage of AI agent development depends on robust infrastructure support. Without verification capabilities, payment systems, scalable computing resources, and robust data pipelines, the entire ecosystem may stagnate. Decentralized infrastructure solves these problems in the following ways:
Trust and Transparency: Ensuring the security and verifiability of agents and their outputs.
Scalability: Meeting the growing computational and data demands of AI.
Collaborative Ability: Collective intelligence and coordination layers enable seamless cooperation between agents.
Empowering Users: Data ownership and decentralized tools allow users and developers to shape the future of AI without central control.
5. Other Noteworthy Trends
There are also some trends that may develop rapidly in 2025, which I will cover in more detail later:
Agentified Metaverse / AI x Games: Projects like @realisworlds and @ARCAgents are combining AI agents with games and immersive virtual worlds.
On-Chain and DeFi Tools: Protocols such as @Almanak__, @AIWayfinder, @getaxal, @Cod3xOrg, @griffaindotcom, and @orbitcryptoai are building critical tools to support DeFi agents.
6. Conclusion
2025 will be the era of AI agents - they will rapidly progress towards human-like general artificial intelligence (AGI). These agents will no longer be limited to executing single tasks, but will be able to autonomously trade, collaborate with other agents, and interact with humans in ways we can barely imagine.
Imagine a scenario where an agent analyzes market data, conducts trades, manages finances, or coordinates other agents to complete complex tasks. These agents will seamlessly integrate into our lives, from on-chain DeFi operations to real-world interactions, with autonomy and intelligence far beyond what we can conceive today.
The decentralized infrastructure being built, including verifiable systems, payment tools, computing networks, and coordination layers, is the key to realizing this future. For developers, investors, and enthusiasts, now is the best time to get involved and shape the future.
2025 is not just a continuation of AI development, but the dawn of a new era of AI agency.
Link to the article: https://www.hellobtc.com/kp/du/12/5600.html
Source: https://x.com/Defi0xJeff/status/1870878345979908321
