Original Title: What's the point of Crypto AI Agents?

Original Author: 0xJeff, AI Investor

Original Translation: AididiaoJP, Foresight News

It has been a full year since the wave of AI Agents is set to launch in Q4 2024.

At that time, @virtuals_io was the first to propose the concept of "AI Agents Tokenization," which pairs AI applications/tokens with fairly launched tokens.

In just this short year, the Crypto AI field has undergone tremendous changes: it has propelled the open-source movement of general AI, with a plethora of tools emerging, allowing both developers and novice users to easily start building projects.

Initially, it was just an AI product issuing tokens, with undervalued fair launches led by independent developers or small teams. Now it has evolved into a complete Crypto AI ecosystem, with hundreds of excellent teams building their visions here.

Given the recent hype brought by the x402 narrative, this article will explore the most important question by reviewing the current state of the industry, understanding the changes, and analyzing the progress of key players: where is all of this headed? What is the core value of Crypto AI Agents?

If you, like me, are excited about AI and eager to learn, you have probably noticed the rapid pace of AI development. Every month, new and exciting things emerge. From basic applications that are "not bad to have," like Ghibli-style transformations of everything, to AI-generated videos of production-level quality, and AI Agents that outperform ordinary junior programmers in productivity.

However, in the Crypto field, the situation has not always been the same. At this time last year, when the narrative of AI agents emerged, the hot projects were:

• @truth_terminal became lively, interacting with a16z's @pmarca and receiving investment.
• @aixbt_agent provided insightful analysis and was also a quirky, native player in the Crypto circle on the X platform.
• @virtuals_io, as the "Agent Society," launched the "Agent Token," which frequently surged 10-50 times.
• @dolos_diary became the internet's number one "bully," loved for its sharp humor.
• @luna_virtuals emerged as the first AI idol.

When the narrative started, entertainment was the number one theme. But now, we haven't seen any new forms of entertainment brought by AI Agents for a long time (this may be a good thing, but the charm and appeal of the early AI era have indeed faded).

The focus is now intensely concentrated on the vertical fields that Crypto excels in: financial use cases, namely making money (and not losing money).

In its latest "State of the Crypto Industry" report, a16z proposed a potential market size of $30 trillion for the agent economy, which may be a bit unrealistic, as the entire AI market is expected to be only a few trillion dollars by 2030.

That said, I believe the entire agent economy can indeed be worth trillions of dollars. As generative AI tools and vertical AI help individuals enhance productivity, and as enterprise adoption increases, the market will continue to expand with more efficient AI-driven workflows being introduced and implemented within organizations.

The Crypto field is no exception. However, due to the industry's extreme focus on making money, its workflows will naturally revolve around profit-making. The following categories are particularly prominent:

DeFi: The Most Mature Product Market Fit in the Crypto Field

• Trading (spot, perpetual contracts, conducted on CEX/DEX)
• Money markets (lending, crypto asset collateral)
• Stablecoins (medium of exchange/value-stable units, combinable high-yield DeFi strategies)
• Yield protocols (interest rate markets, points markets, funding rate markets, yield optimizers/treasury products)
• RWA/DePIN (bringing real-world productive assets on-chain, connecting on-chain capital to meet off-chain demand)

This is the largest potential market, with a total locked value exceeding $150 billion and a stablecoin market cap exceeding $300 billion. Increasing regulatory clarity and growing institutional adoption are driving more capital on-chain; meanwhile, the surge in stablecoin adoption is attracting more businesses and startups to use crypto channels.

For these reasons, the demand for automation can serve as both backend infrastructure and tools, while enterprises/startups as the frontend will bring ordinary users on-chain, becoming key to driving the next phase of adoption.

AI agents that can abstract away the complexities of DeFi, simplify execution processes, or improve key aspects of DeFi (such as risk management, asset rebalancing, strategy curation, etc.) are likely to capture a significant portion of the immense value flowing into DeFi protocols.

Key Ecosystem Players:

@almanak, @gizatechxyz, @Cod3xOrg, @TheoriqAI, @ZyfAI_

• DeAI is the most mature product market fit in the Crypto AI field.
• Prediction markets x AI: The fastest-growing niche in the Crypto field.

If you continue to observe the ecosystem, you will find that the DeFi x AI field has not changed much. This is because cracking the workflows related to DeFi is extremely difficult. You cannot just shove AI in and hope for good results; responsible structural design and safeguards must be implemented to prevent serious accidents.

Why am I talking about this now, rather than the vague "AI agents"?

The initial AI agent ecosystem was basically Virtuals and the agents built within its ecosystem (perhaps with a few others like CreatorBid), along with frameworks like ai16z (now called ElizaOS), which made it easy to build "agents" or X robots that could call various tools, as well as many other frameworks like Arc, Pippin, etc.

These things are cool and interesting, but they do not represent the true definition of AI agents. A true agent should be able to understand its environment, comprehend its role and responsibilities, make proactive decisions, and take actions to achieve specific goals with minimal human intervention.

Looking around, over 95% of the projects do not fit this description. They are either just software, a generative AI product, or still in the process of evolving into autonomous AI agents.

I do not mean to belittle anyone. I want to emphasize that I *am still in a very early stage where most people have not really figured out what works.

Those who have figured out what works are often not classified as "AI agents," but rather seen as an AI project.

Current Ecosystem Status

The recent hype brought by x402 has stimulated capital rotation and interest in Crypto AI, but the new ecological landscape is significantly different from before.

1. Framework Hype Has Diminished

Frameworks were once very important; they helped builders get started quickly and reduced the time spent learning and writing code, designing workflows. Tools like MCP enhanced the ability of agents to call or provide APIs, ERC-8004 will help establish registries and establish Ethereum as a trust and settlement layer, Google's A2A & AP2 are becoming the preferred frameworks for builders, and AI agent/workflow building tools like n8n have also attracted a large number of developers and ordinary users.

Because of this, the hype around "frameworks" themselves has cooled, and many projects have shifted in other directions. For example, @arcdotfun has turned to workflow builders; @openservai, initially positioned as a "cluster," has now also shifted to workflow builders, as well as tools aimed at leveraging agents to create Web3 AI-driven businesses, targeting specific user groups (like prediction market workflows).

Frameworks are still important, but with the proliferation of Web2 AI frameworks and tools, and the adoption of Web3 channels, the hype around Web3 frameworks has indeed diminished.

2. Shift from Fair Launch Pads to ICO Models

The fair launch pad model is beneficial for small retail investors but makes it difficult for teams to scale. It can also easily become a breeding ground for independent developers to engage in short-term builds or pure speculation, rather than building sustainable AI businesses that can last 3-5 years or more.

In this regard, it makes sense for Virtuals to expand through its agent business protocol. As x402 establishes itself as a payment channel for agents, building the infrastructure for agent trust/reputation scoring, and defining how agents work together and pay each other for services is crucial to realizing the agent vision.

However, challenges and core questions remain: "Are there high-quality services that people are willing to pay for?"

If most services are useless, why wouldn't people just use Web2's AI services instead of choosing Web3? If so, what is the point of gathering Web3 agents together?

To build a sustainable AI business that can generate 7-8 figure revenues, you need funding, highly motivated talent, and time to build the vision, while the fair launch pad model struggles to meet these needs.

Instead, we see medium to large AI teams becoming increasingly popular, as they can secure seed funding from angel investors and venture capitalists, and enter the market through community rounds (whether on Kaito Launchpad, Legion, or Echo).

These teams, with the resources they possess (funding, talent, venture backing, etc.), often provide much higher quality products/services, which also typically leads to better performance of their tokens.

3. Ineffective Profit Models and Token Economics

Managing AI products and tokens requires two completely different skill sets, necessitating careful design to combine the two to accelerate product growth and user acquisition (e.g., airdropping tokens to the right users → users convert to paying customers → paying for the product → obtaining more tokens, which bind users to the long-term interests of the project through revenue sharing, buybacks, governance, etc. → the flywheel continues to turn).

Easier said than done. Most small AI agent teams allocate 30-80% of their token economics, leaving no remaining resources to kickstart any growth flywheel.

Most projects adopt a SaaS subscription model or charge based on usage/points, and add options for discounts when using tokens for payment. Many projects will use part of the subscription revenue to buy back tokens or burn tokens used for service payments.

Using subscription revenue to buy back tokens is feasible, but simply mandating token payments (or only offering discounts) makes it difficult to scale.

Cryptocurrency tokens are highly volatile. Using them as a payment medium is not a good idea (they may rise 20% today and drop 30% tomorrow, making budgeting difficult).

4. Darwinian AI: A New Path for Capital Formation and Clear Token Economics

@opentensor (Bittensor) has become the preferred platform for founders to launch ideas, miners to contribute to AI, and investors to invest in the next potentially disruptive DeAI company.

@flock_io utilizes federated learning to set standards for privacy protection and domain-specific AI, attracting Web2 companies and governments as clients, as well as trainers (miners) who wish to contribute to AI. Similar to Bittensor, Flock helps companies complete cool and meaningful AI work with external high-quality talent.

@BitRobotNetwork, inspired by Bittensor, is taking a similar approach to guide a robot-centric subnet ecosystem.

At the same time, real-world benchmarks/assessments with real money gaming are emerging (which has also become a form of high-quality entertainment):

• @the_nof1's Alpha Arena allows six cutting-edge AI models to compete in perpetual contract trading with real money ($10,000 each).
• @FractionAI_xyz leverages competition among AI agents to improve/continuously fine-tune agents for better outputs, signals, profits, and risk management.
• @openservai creates OpenArena, where AI models compete in prediction market trading.

Darwinian AI is addressing the issue of capital formation and is the engine driving Crypto AI innovation.

• The top-ranked Bittensor subnet Chutes is already the number one inference service provider on OpenRouter, which is the most popular unified API gateway among general AI developers worldwide.
• The top computing subnets (3-4) collectively achieve annual recurring revenues of $20-30 million.
• Prediction-related subnets are beginning to generate annual recurring revenues ranging from hundreds of thousands to millions of dollars by monetizing alpha signals and/or utilizing signals for better trading/predictions.

Darwinian competitive AI = capital formation (without venture capital) + innovation accelerator (attracting AI/ML engineers to contribute) = this will be the core driving force behind the AI agent narrative in 2026.

Note: "Darwinian AI" specifically refers to a decentralized ecosystem that drives AI model development, evaluation, and returns based on competition and market economics. Its core idea is "survival of the fittest," similar to Darwin's theory of natural selection, allowing the best and most useful AI models to win and be rewarded in open competition.

So, what is exciting for small teams or AI agents right now?

To be honest, there are some that I find useful, but none that I am willing to pay for at the moment.

• Research: Grok covers the X platform, while ChatGPT covers general domains.
• In-depth analysis: I mainly look at newsletters and Messari reports.
• Quick market outlook: using @elfa_ai's TG chatbot.
• Prediction market trading ideas: using @AskBillyBets, @Polysights, and @aion5100's @futuredotfun. (I am looking forward to @sire_agent's aVault, but it has not been made public yet).
• DeFi: Mostly self-operated, sometimes using @almanak and @gizatechxyz, but these are not strictly "AI agents" nor fair launches.
• Trading: using @DefiLlama for exchanges on EVM, or using @JupiterExchange for exchanges on Solana. I do not engage in perpetual contracts (using @Cod3xOrg for analysis and execution when necessary).

The Crypto field is accustomed to allowing users to use everything for free, so users prefer free tools. Token gating or fee gating is not very effective, but seamlessly embedding fees into products is feasible. This is why outcome-based pricing models are *very effective. People are unwilling to pay $40 monthly, but they are willing to pay $40 in gas fees for a successful trade.

If you can deliver optimal results (high returns, best trading prices), as long as the results are good enough, no one will mind that you have built-in fees.

After trying so many Crypto AI applications or agents, what I have learned is that the best products right now are those that can make money, and the best verticals to achieve this goal are launchpads (and the soon-to-explode prediction markets), which operate "casinos" on-chain and accumulate fees from trading.

Future Outlook

• Real use cases that can achieve mainstream adoption (i.e., those that ordinary AI developers or users outside the circle would use) will emerge next year, likely stemming from the DeAI/Darwinian AI ecosystem.
• 2026 will be the year of Crypto AI, with a surge of DeFi use cases, DeAI infrastructure, and prediction use cases.
• Most small agent teams will gradually disappear or be acquired/merged, or shift to building within the Darwinian AI ecosystem.
• Crypto AI and AI agents as a niche will merge, marking a clearer product direction and vision for Crypto AI.
• Launchpads will remain the core of the Crypto Twitter circle, generating trading volume and fees, but significant innovations that truly drive industry progress will occur where resources (capital, talent, distribution channels, and user adoption) are most concentrated.

What is the significance of Crypto AI Agents?

For fairly launched "AI agents," their significance lies in designing a trading experience that wears the guise of "investment technology," even though most of them are merely LLM wrappers with a layer of token shell.

In most cases, it provides small retail investors with the best way to early invest in such "AI agent" speculative assets and make money.

The narrative of Crypto AI agents signifies laying the foundation for the future agent economy, where blockchain will serve as the core infrastructure/channel to make all of this possible.

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