AI agents will become new economic participants, potentially replacing traditional work methods, moving towards a more cost-effective, task-driven compensation system, reflecting on the role of humans in this future of AI agents.
Author: Stacy Muur, Crypto Kol
Compiled by: zhouzhou, BlockBeats
Editor’s note: This article explores the transformations that AI agents may bring in 2025, especially in the fields of Web3 and stablecoins. It analyzes various methods of verifying human identity, such as Aadhaar and Worldcoin, and suggests that AI agents will change economic activities, driving the popularity of on-chain payments. AI agents will become new economic participants, potentially replacing traditional work methods, moving towards a more cost-effective, task-driven compensation system, reflecting on the role of humans in this future of AI agents.
The following is the original content (for ease of reading, the original content has been restructured):
AI has now become a captivating vertical in Web3, so what will drive the development of these markets by 2025? Will we see a true AI revolution in the crypto space?
2024: The big year for AI
Over the past year, AI has become foundational across industries, with Nvidia surpassing Apple to become the world's most valuable company. This is not just a headline; it signifies the rise of AI. OpenAI achieving a valuation of $157 billion is also a significant milestone that highlights the market's 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: Focusing on AI agents
AI agents became a true phenomenon in 2024, with these AI agents' capabilities and personalities now remarkably similar to 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 current of excitement—the thrill of possibility.'
In this study, Delphi emphasizes the key roles played by some AI agents in the formation of new Web3 verticals:
Truth terminal quickly gained attention on Twitter due to its unique combination of 4chan's crude style and mysterious wisdom. Like DOGE in the meme space or Crypto Punks in the NFT space, GOAT, as the OG (original) in the 'conscious meme' space, is most likely to exist long-term as an original.
0xzerebro embraces the 'schizophrenic vibe,' akin to the second generation of GOAT. This agent is cross-media, interacting 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, an initiative that actually open-sourced 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 is far less known than Zerebro or ToT. It is a relatively small, underhyped project aimed at tech purists and may be the first true experiment in verifiable autonomous social media presence.
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 driven by Llama or Anthropic LLMs, automating social media posting and content generation.
Finally, god / s8n is a capable AI influencer with significant attention beyond CT.
AI VS Influencers
I would like to temporarily step away from Delphi's research to share my thoughts on an important question raised by DefiIgnas: the status of AI agents in CT thought leadership and the challenges faced by human influencers in competing with them.
I agree with many of Ignas's viewpoints on this topic, but I don't think AI agents will replace real human influencers for one main reason: emotional connection and reputational risk.
Currently, there are hundreds of AI agents on CT competing for attention. 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 volume of content and analyze a wide array of on-chain data, but they all originate from the same information pool, leading to similar thought processes.
They lack the emotional connection to the transactions they perform and do not react to wins or losses. Many platforms already offer aggregated insights, such as the AI news reader from MessariCrypto or the homepage of tokenterminal, which show the 7-day increase and decrease 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 react to wins and losses. Indeed, this is possible. With future technological advancements, such as test-time computation and enhanced memory capabilities, this becomes more feasible.
However, the key difference between human and machine thinking is that human thinking is not static.
I conducted some experiments trying to teach AI my thought processes and writing style to assist with content creation and free up more time for research. While it learned some things, it still couldn't generate content that satisfied me or made me say, 'Yes, this is the conclusion I draw from this information.'
In the coming years, we will certainly see the rise of AI influencer agents, each designed for specific tasks. However, as these agents proliferate, the demand for genuine 'human' thinking will increase.
Ultimately, social media revolves around emotion 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 published in her name.
Democratizing AI: Platform level
Given that the market is larger and value capture more specific, everyone wants to be a platform. This shift is now guiding developers' attention, as evidenced by virtuals io's successful transformation into an AI agent launch platform. Meanwhile, ai16zdao has launched ELIZA—an open-source framework for easily building agents. It includes preconfigured character files, memory modules for long-term interactions, and seamless integration with social platforms.
AI16z and Virtuals are both hinting at multi-agent capabilities, which are expected to become an important theme in 2025.
ELIZA is launching 'SwarmTech', a coordination mechanism for collaboration between agents. Meanwhile, Virtuals has launched 'GAME', a platform and engine that allows AI agents to act and interact within virtual worlds and environments.
These frameworks will enable agents with different capabilities to collaborate in cooperative or hierarchical organizations to accomplish 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 'tweeting' on X), making meme coin publishing as easy as tweeting.
SimulacrumIO is doing the same 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 in collaboration with human players to create rich storytelling experiences.
MemeticaAI is an AI influencer launch platform on Solana that offers highly tuned LLMs (large language models) and allows easy selection and editing of knowledge bases and attributes while empowering agents with active learning capabilities.
TopHat One is a no-code AI agent launch platform that allows you to create personalized AI agents in 3 minutes, offering fairly distributed tokens. Free to create, no hierarchy, supports optional token launches, fully autonomous.
Authentication is coming soon
With the explosive growth of agents, authentication will inevitably become a hot topic in 2025.
There seem to be three main paths to verifying human identity:
State-based biometrics: India's Aadhaar is the most relevant example, serving as a key component of India's modern digital infrastructure.
Private encrypted biometrics: Currently, Worldcoin is the leading candidate in this category.
Private mixed solutions: This involves combining government-issued IDs or big tech companies' single sign-on (SSO) 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 transform economic activities and demand updates to financial infrastructure.
The card payment systems of the 1960s will not meet the demands for cost, speed, precision, and expressiveness. Economic activities between agents will soon surpass those of other economic participants. On-chain payments will become key to facilitating these transactions and are expected to reach a turning point in 2025.
Final thoughts
As usual, at the end of the research summary, I would like to share my personal thoughts with everyone. If you believe the future of AI is bright, promising human happiness and a perfect work-life balance, I strongly encourage you to chat with OpenAI's ChatGPT. Let it generate some business ideas utilizing AI that will become relevant in the next 5 to 10 years.
A few months ago, prior to the AI craze on CT, I conducted this experiment. Let me share some of the 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 their employees' performance relative to global and industry averages.
Even Delphi's report presents an interesting vision: 'Rather than having 'salary' employees, we are more likely to move towards a more refined, task-based compensation system (i.e., renting three agents, each working 30 minutes to solve 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?