Original Title: How to make fucking $$ in the Agentic Economy 2025
Original Author: Foxi_xyz, Crypto KOL
Original Compiler: zhouzhou, BlockBeats
Editor's Note: This article explores the future development of the agent economy, particularly the evolution of AI agents. From Stage 1's simple chatbots to Stage 2's combination of privacy and DeFi, and then to Stage 3's collaboration between agents, it emphasizes the critical role of infrastructure, frameworks, and technology. While the current market is flooded with simple social media agents, the real opportunity lies in the technologies and platforms that support autonomous economic activities.
The following is the original content (reorganized for readability):
"Agents are not just here to hijack your Twitter feed—they are reshaping the entire digital economy."
In this article, we will delve into how to discover the next AI Agent Alpha by fully understanding the methodology behind it. Remember, once everyone sees it, it is no longer Alpha. The key is to apply this methodology and stay two steps ahead to succeed.
Golden Rule: Consensus
I will dump your coins, and you will dump someone else's. We are all here to make money, so you should never truly "marry" your investment bag. Your task is to find the right timing to dump it. To offload these bad coins onto others, you need to find a project with "consensus." This means you are likely to find your exit liquidity.
How to make money?
Predict an emerging trend (consensus) with firm belief.
Invest in potential stocks, ensuring they have enough legitimacy and innovation.
When consensus forms, sell your bags to others.
The next article will focus on the hardest part: prediction.
1. Three Waves of Agent Evolution
While 2024 is defined by breakthroughs in AI such as OpenAI's o1 and billion-dollar valuations, 2025 will be the year of the financialization of artificial intelligence. However, in this rapidly evolving environment, the key to discovering real value is to look beyond the "AI Agent" hype and understand the fundamental changes taking place.
Understanding our position in the adoption cycle is crucial for discovering opportunities. The agent economy is unfolding in three different stages:
Wave 1: Human to Agent (Current)
We are currently in this stage—imagine the simple chatbot interactions you see on X (aixbt agent), where these agents primarily serve as research assistants and execution layers of human intent. While valuable, they do not require revolutionary infrastructure changes. Most agents now resemble customized ChatGPTs, helping you with simple research. These agents do not possess high autonomy. They cannot independently manage resources, take risks, or pay for other services.
Wave 2: Agent to Human (Emerging)
This is where it gets interesting; AI agents begin to handle daily tasks independently—such as executing trading strategies, optimizing home energy use, or negotiating and paying bills—without your constant intervention. While tools like Stripe's Agent SDK can cover some scenarios, they also herald a larger shift: you will no longer pay fixed monthly or annual fees but will see more granular on-demand billing.
As agents take on more responsibilities, they need to cover computing power, API costs per query, model inference costs, etc.—anytime, anywhere. These small-scale on-demand transactions quickly expose the limitations of existing payment systems, as these systems were not designed to support real-time micropayments. This is where cryptocurrencies can play a role, offering faster settlements, lower fees, and more flexible ways to better meet these new demands.
Some on-chain examples may include:
Automated trading systems
Yield optimization
Portfolio rebalancing
Wave 3: Agent to Agent/Collaboration (Future)
This is where the biggest opportunities lie. We have already seen some early experiments, with projects like Terminal of Truth and Zerebro exploring agent-to-agent business models, but the true potential goes far beyond social media tokens:
Resource markets: Computing agents negotiating with storage agents for optimal data placement
Service optimization: Database agents negotiating query optimization services with computing agents
Financial services: Risk assessment agents trading insurance with assurance agents
This stage requires infrastructure specifically designed for machine-to-machine interaction, as traditional monetary systems, which focus on human verification and control, struggle in an economy dominated by autonomous entities. In contrast, stablecoins provide a crucial framework, with their programmability, cross-border capabilities, rapid transaction settlements, and facilitation of micropayments, making them better suited to meet these new demands than traditional systems.
2. Analyzing the Web3 AI Stack
Delphi Digital has made a fairly clear categorization of the "decentralized AI" stack, highlighting 6 key directions (which I believe have high potential), each with some "sub-industries." You can think of them as narratives or innovation directions. I can guarantee that each sub-industry will have a leader reaching at least a $500 million market cap in the coming months.
6 Key Directions and Notable Examples:
Applications: aixbt agent
Agent Enablers/Coordination: virtuals
Privacy: PhalaNetwork
AI Training/Inferences: ritualnet
Computing: ionet
Data: Arweave
Each key direction has many sub-industries, so I won't cover everything here. However, I expect there will be a "sequence of capital inflow." This sequence will heavily depend on the maturity and development stage of the market, and here is a visual I created:
Stage 1:
We see a large number of agents being released every day, most of which are just wrappers for chatbots/ChatGPT, with very limited practical use, but they are an interesting meme. As we know, whether a product is useful is completely unrelated to its valuation, so many interesting agents with imaginative potential will be hyped.
Agent Enablers (launch platforms) become the first-level infrastructure for agents at this stage. For example, as agents generate income through audience interaction, these funds will be used to buy back and burn tokens in the liquidity pool paired with $Virtual. This creates a direct link between the success of agents and the value of the platform, aligning the incentive mechanisms within the ecosystem.
Stage 2:
If a narrative is left with only chatbots on Twitter spamming junk, it is no longer appealing. Therefore, we begin to see people incorporating the concept of "privacy" (e.g., aipool tee and sporedotfun).
The good news is that most people do not understand technical terms like TEE, FHE, and ZKP. This suddenly makes agent applications look very innovative, even if these agents may not actually implement TEE. However, all of this is to enhance the value of agent applications and give them more "practicality." Agents will soon venture into the DeFi/wallet space.
We will see agents capable of token swapping, cross-chain bridging, optimizing trading routes, and minimizing transaction fees for you, and these agents will be integrated into wallet interfaces.
The key is that these agents now need to compete on "technology" rather than "culture," which is different from Stage 1. Therefore, you will see tokens like $BUZZ or $ACOLYT being hyped because they are backed by a legitimate AI team or developer (even though they may still be far from Web2 AI experts).
You will soon see: the "AI agent wealth effect" will attract a large number of top Web2 AI developers into the Web3 space to make money, leading to many powerful AI projects. If I were you, I might check LinkedIn more frequently rather than Dexscreener.
Stage 3:
These agents will eventually mature and derive core value from AI inference, data, and distributed computing.
We will have TEE-based infrastructure for secure key management, dedicated data availability (DA) layers for storing and retrieving the context of large language models (LLM), on-chain oracles providing trusted data streams, zkVM frameworks for verifiable execution, and chain abstraction solutions.
This also means that we will access large-scale, trustless computing resources while ensuring that interactions, data flows, and outputs remain verifiable and secure. At this point, advanced infrastructures like ritualnet, ionet, and StoryProtocol will transition from mere speculation to essential drivers of the next generation of AI innovation.
Part Three: So What Should I Invest in Now?
Let’s return to the overview we saw earlier:
I believe we are currently transitioning from Stage 1 to Stage 2, and I am more interested in whether agents can "actively" provide us with real value, rather than just posting on Twitter or analyzing token price charts (which I can do with GPT). By "actively," I assume agents can manage resources for me and make autonomous decisions. They should be able to independently complete transaction settlements.
Some more complex applications I would personally be willing to invest in include:
Automated trading systems
Portfolio rebalancing
Virtual reality
AI-based smart contracts (prediction market arbitration)
These directions may still be too broad for most people, including myself. According to our golden rule of "consensus," I have observed that most consensus is concentrated in the following areas:
1. shawmakesmagic
He is the ultimate AI cult leader in this AI supercycle, much like MustStopMurad in the memecoin supercycle. Do not overlook any projects that ai16z and shaw/core ai16z members are focusing on and promoting. The projects he pays attention to and promotes are buying opportunities.
Note: "ai16z partners" are not always core members; I can also call myself a partner.
2. Solana Hackathon Winners
This chain is driving some projects that help you filter potential projects. You need to stay at the forefront of the market and select some high-quality projects. I have already done this work for you; you can check my posts. I am not an insider, so I can only judge these projects based on personal experience. You should also do some research yourself.
However, to actually profit from this narrative, you need tools to help you; otherwise, you will not be able to enter the market at the optimal time.
For example: AgentiPy was initially on my list of high-quality projects, with a token. Once it launched the token, it skyrocketed to 40M, and I personally couldn't keep up in time. You need a tool to monitor the project's Twitter and enter promptly when the CA (contract address) is detected. More tutorials are coming soon.
3. AI Frameworks
Do you know why there are now over 500 framework layers, and all decentralized applications (dapps) have transformed into application chains (appchains)? It’s simply because of "higher valuations." Infrastructure essentially provides you with a higher valuation space. However, not every framework is the same.
You either enter the market leaders or research the technology and GitHub to find a "good technological infrastructure." If you can't manage these, the best option is still to find a good entry point in $ai16z or $virtual. Do not throw your SOL/ETH into random AI scam projects.
4. DeFi x AI
As mentioned above, I am optimistic about the upcoming DEFAI (the combination of DeFi and AI) narrative. This aligns with my hypothesis for Stage 2 (agent to human). A good starting point is to learn through the following DeFi x AI projects and their subfields:
Finally, understanding these dynamics is crucial for identifying real opportunities in the agent economy.
Although the current market seems dominated by simple social media agents, the real value lies in the infrastructures and frameworks that will drive the next generation of autonomous economic activities. I will continue to hold my $ai16z and $Virtual tokens.
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