Author: Stacy Muur, Crypto Kol
Compiled by: Zhouzhou, BlockBeats
Editor's Note: This article explores the transformative changes that AI agents may bring in 2025, particularly in the applications within Web3 and stablecoin sectors. It analyzes various methods for verifying human identity, such as Aadhaar and Worldcoin, and predicts that AI agents will change economic activities, driving the adoption of on-chain payments. AI agents will become new economic participants, potentially replacing traditional ways of working, moving towards a more cost-effective, task-driven compensation system, and reflecting on the role of humans in this AI agent future.
The following is the original content (edited for readability):
AI has now become an eye-catching vertical in Web3, so what will drive the growth of these markets by 2025? Will we see 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 world's most valuable company. This is not just a headline; it marks the rise of AI. OpenAI achieving a valuation of $157 billion is also a significant milestone, highlighting market confidence in AI as an economic powerhouse.
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 real phenomenon in 2024, with the capabilities and personalities of these AI agents now becoming very similar to humans. Notably, this will be the least advanced moment for these agents. As Delphi researchers wrote: "Since the DeFi summer, I have not felt the electric current of excitement that comes from possibilities."
In this research, Delphi highlights some AI agents' crucial roles in shaping new verticals within Web3:
Truth terminal rapidly gained attention on Twitter due to its unique blend of 4chan's vulgar style and mysterious wisdom. Just like DOGE in the meme realm or Crypto Punks in the NFT space, GOAT, as the OG (original) of the 'conscious meme' domain, is most likely to endure as an original.
0xzerebro, embracing a 'schizophrenic vibe', similar 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, which effectively open-sources many of the toolkits behind Zerebro. This allows other developers and users to create their own cross-platform personas. If successful, Zerebro may become the first holder of the title 'agent protocol'.
The notoriety of tee hee he is far lower than that of Zerebro or ToT. It is a relatively small, underhyped project aimed at tech purists and may be the first genuine 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 firm, and continually holds the highest CT user engagement on Kaito.
Dolos Diary provides the architecture for building Dolion, a no-code, one-click deployment framework. With Dolion, users can develop cross-platform AI agents powered by Llama or Anthropic LLMs, automating social media publishing and content generation.
Finally, god/s8n is an AI influencer with considerable capabilities and massive attention outside of CT.
AI VS Influencers
I would like to temporarily step away from Delphi's research to share my views on an important question posed by DefiIgnas: the status of AI agents in CT's dominant thinking and the difficulties faced by human influencers in competing with them.
I agree with many of Ignas's points on this topic, but I do not believe AI agents will replace real human influencers, for one main reason: emotional connection and reputation risk.
Currently, there are hundreds of AI agents on CT vying for attention. However, only aixbt has successfully established 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 an emotional connection to the transactions they make 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, 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 thought, express emotions, and respond to wins and losses. Indeed, this is possible. With future technological advancements, such as testing time calculations 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 trying 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 couldn't generate content that satisfied me or made me say, 'Yes, this is the conclusion I derived 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 genuine 'human' thinking will also rise.
Ultimately, social media revolves around emotions and entertainment. Those who truly stand out and become real influencers offer unique value that goes beyond mere '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 that the market is larger and value capture is more specific, everyone wants to become a platform. Now, this shift is guiding developers' attention, as shown by virtuals io's successful transformation into an AI agent launch platform. Meanwhile, ai16zdao has launched ELIZA—a framework for easily building agents. It includes pre-configured character files, memory modules for long-term interaction, and seamless integration with social platforms.
ai16z and Virtuals both hint at multi-agent capabilities, which are expected to become an important theme by 2025.
ELIZA is launching 'SwarmTech', a coordination mechanism for collaboration between agents. Meanwhile, Virtuals has launched 'GAME', a platform and engine that enables 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 to complete more complex tasks, similar to how the human economy operates today.
Other protocols worth noting include:
CLANKER directly integrates pump.fun features into 'casting' on Farcaster (equivalent to 'tweeting' on X), making publishing meme coins as easy as tweeting.
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 featuring thousands of coordinating AI agents that generate content and maintain cross-platform consistency, collaborating with human players to create rich storytelling experiences.
MemeticaAI is an AI influencer launch platform on Solana, offering highly tuned LLMs (large language models) and allowing 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 within 3 minutes, offering fairly distributed tokens. Create for free, no hierarchy, optional token release supported, completely autonomous.
Authentication is coming soon
With the explosive growth of agents, authentication will inevitably become a hot topic in 2025.
It seems there are three main paths to verify human identity:
State-based biometrics: India's Aadhaar is the most relevant example, serving 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 the use of government-issued IDs or single sign-ons (SSO) from big tech companies combined 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 regulatory changes in the U.S. and the wave 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 necessitate 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 outpace those of other economic participants. On-chain payments are expected to become key in facilitating these transactions, anticipating 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 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 utilizing AI that will become relevant in the next 5 to 10 years.
Months ago, before the AI boom on CT, I conducted this experiment. Let me share some of the ideas it suggested:
Memory modification tool: An AI tool that analyzes human traumatic experiences, actively modifies them, and regularly presents modified memories to individuals to replace old memories.
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 'salaried' employees, we are more likely to move towards a more granular, task-based compensation system (i.e., renting three agents, each working 30 minutes to solve a specific task)."
In the future of AI agents—ultimately more cost-effective than today's models and more aligned with commercial needs—what role will we humans play?