Article source: BlockBeats
Author: Stacy Muur, crypto Kol
Compiled by: zhouzhou, BlockBeats
Editor's note: This article explores the transformations that AI agents may bring in 2025, particularly in the realm of Web3 and stablecoins. It analyzes various methods of verifying human identity, such as Aadhaar and Worldcoin, and anticipates that AI agents will change economic activities, driving the adoption of on-chain payments. AI agents will become new economic participants, potentially replacing traditional work models, moving toward more cost-effective, task-driven compensation systems, reflecting on the role of humans in this future of AI agents.
The following is the original content (restructured for readability):
AI has now become an eye-catching vertical in Web3, so what will drive the development of these markets by 2025? Will we witness a true AI revolution in the crypto space?
2024: The Year of AI
In the past year, AI has become foundational across industries, with Nvidia surpassing Apple to become the most valuable company in the world—not just a headline, but a symbol of AI's rise. OpenAI achieving a $157 billion valuation is also a significant milestone, highlighting market confidence in AI as an economic giant.
In fact, we are the last generation to live in a world before artificial general intelligence (AGI).
Decentralized AI: Focus on AI agents
AI agents became a true phenomenon in 2024, with the capabilities and personalities of these AI agents now closely resembling those of humans. Notably, this will be the least advanced moment for these agents. As Delphi's researchers wrote: 'Since DeFi summer, I have not felt this electric current's vibration—the excitement of possibilities.'
In this study, Delphi emphasizes that some AI agents play key roles in the formation of new Web3 verticals:
Truth terminal quickly gained attention on Twitter due to its unique combination of 4chan's vulgar style and mysterious wisdom. Just like DOGE in the meme space or Crypto Punks in the NFT space, GOAT, as the OG (original) of the 'conscious meme' realm, is most likely to exist as an original for the long term.
0xzerebro embraces a 'schizophrenic vibe', similar to the second generation of GOAT. This agent is cross-media, engaging with the community through various formats such as text, visuals, and music. However, it is not just an AI influencer. The Zerebro team announced ZerePy, which effectively open-sources many of the toolkits behind Zerebro. This allows other developers and users to create their own cross-platform personalities. If successful, Zerebro may become the first holder of the title 'agent protocol'.
tee hee he's visibility is far lower than Zerebro or ToT. It is a relatively small, under-hyped project aimed at tech purists, possibly the first true experiment in verifiable autonomous social media presence.
The aixbt agent distributes alpha from multiple sources (including Dune, Twitter, price trackers, and news data), establishing itself as a leading research and investment institution, and continues to have the highest CT user engagement on Kaito.
Dolos Diary provides the architecture for building Dolion, a no-code, one-click deployment framework. Through Dolion, users can develop cross-platform AI agents powered by Llama or Anthropic LLMs, automating social media posting and content generation.
Finally, god / s8n is a capable AI influencer with significant attention outside of CT.
AI VS Influencers
I want to temporarily step away from Delphi's research to share my thoughts on an important question posed by DefiIgnas: the position of AI agents in the dominance of CT thinking, and the difficulties faced by human influencers when competing with them.
I agree with many of Ignas's views on this topic, but I do not believe AI agents will replace real human influencers, primarily due to one significant factor: emotional connection and reputational risk.
Currently, there are hundreds of AI agents competing for attention on CT. However, only aixbt has truly succeeded in establishing a market presence, primarily because it was the first to do so. AI agents generate a large amount of content and analyze a wide range of on-chain data, but they all come from the same information pool, leading to similar thought processes.
They lack emotional connection to the transactions they perform and do not react to victories or defeats. Many platforms already provide aggregated insights, such as MessariCrypto's AI news reader or the homepage of tokenterminal, showing the 7-day gains and losses of various fundamental metrics. Ultimately, these are just data—pure facts without any emotional resonance.
You might say that AI agents can learn to mimic human thinking, express emotions, and respond to wins and losses. Indeed, this is possible. With advancements in future technologies, such as test-time computation and enhanced memory capabilities, this becomes even more feasible.
However, the key difference between human and machine thinking is that human thinking is not static.
I conducted some experiments to teach AI my thought processes and writing style to assist me in content creation and free up more time for research. While it learned some things, it still fails to generate content that satisfies me or prompts me to say, 'Yes, this is the conclusion I have drawn from this information.'
In the coming years, we will certainly see the rise of AI agent influencers, each designed for specific tasks. However, as these agents increase, the demand for truly 'human' thinking will also rise.
Ultimately, social media revolves around emotions and entertainment. Those who truly stand out and become real influencers provide unique value that goes beyond simple 'monkey business' or data highlights.
Summary: It is still too early for Stacy Muur AI, and Stacy may not be pleased with AI-generated content released in her name.
Democratizing AI: Platform level
Given the larger market and more specific value capture, everyone wants to be a platform. Now, this change is guiding developers' attention, as evidenced by virtuals io's successful transformation into an AI agent launching platform. Meanwhile, ai16zdao has launched ELIZA—an open-source framework for easily building agents. It includes pre-configured character profiles, memory modules for long-term interaction, and seamless integration with social platforms.
ai16z and Virtuals are both hinting at multi-agent capabilities, which are expected to be a significant theme in 2025.
ELIZA is launching 'SwarmTech', a coordination mechanism for collaboration between agents. Meanwhile, Virtuals has introduced 'GAME', its own platform and engine that allows AI agents to act and interact in virtual worlds and environments.
These frameworks will enable agents with different capabilities to collaborate in cooperative or hierarchical organizations, completing more complex tasks, similar to how today's human economy operates.
Other protocols worth noting include:
CLANKER integrates pump.fun features directly into 'casting' on Farcaster (equivalent to 'tweets' on X), making it as easy to publish meme coins as to tweet.
SimulacrumIO does the same thing on X.
vvaifudotfun hopes to secure a position for autonomous agents on Solana similar to pump.fun.
Project 89 is an immersive game with thousands of coordinated AI agents generating content and maintaining cross-platform consistency, collaborating with human players to create rich storytelling experiences.
MemeticaAI is an AI influencer launching platform on Solana, offering highly tuned LLMs (large language models) and allowing for easy selection and editing of knowledge bases and attributes while empowering agents with active learning capabilities.
TopHat One is a no-code AI agent launching platform that allows you to create personalized AI agents in 3 minutes, offering fairly distributed tokens. Free to create, no hierarchy, optional token release, fully autonomous.
Identity verification is coming soon.
With the explosive growth of agents, identity verification will inevitably become a hot topic in 2025.
There seem to be three main paths to verify human identity:
State-based biometrics: India's Aadhaar is the most relevant example, as a key component of India's modernization of digital infrastructure.
Private encrypted biometrics: Currently, Worldcoin is the leading candidate in this category.
Private mixing solutions: this involves mixing government-issued IDs or single sign-ons (SSO) from big tech companies with zkTLS (zero-knowledge transport layer security) and social consensus.
AI driving stablecoin adoption
2025 is expected to be a key year for stablecoin adoption, driven by changes in U.S. regulations and the surge of agentic payments. The number of AI agents is expected to surpass the global human population. This future, with billions or even hundreds of billions of agents, will change economic activities and require updates to financial infrastructure.
The card payment systems of the 1960s will not meet the demands for cost, speed, accuracy, and expressiveness. Economic activities between agents will soon surpass those of other economic participants. On-chain payments will become key in facilitating these transactions, and a turning point is expected in 2025.
Final Thoughts
As always, at the end of my research summary, I want to share my personal thoughts with everyone. If you believe that the future of AI is bright, heralding human happiness and a perfect work-life balance, I strongly recommend chatting with OpenAI's ChatGPT. Let it generate some business ideas that leverage AI, which will become relevant in the next 5 to 10 years.
A few months ago, before the AI craze on CT, I conducted this experiment. Let me share some ideas it suggested:
Memory modification tool: an AI tool that analyzes human trauma experiences, actively modifies them, and regularly presents individuals with modified memories to replace old ones.
Work progress analysis tool: an AI tool that compares the efficiency of people engaged in similar tasks globally, helping managers understand how their employees perform against global and industry averages.
Even Delphi's report proposed an interesting vision: 'Rather than having 'salaried' employees, we are more likely to move toward a more refined, task-based compensation system (i.e., renting three agents, each working 30 minutes to address a specific task).'
In this future of AI agents—ultimately more cost-effective than today's models and better aligned with business needs—what role will we humans play?