Original Article Title: AI Agents: Ecosystem Leaders
Original Article Author: @Defi0xJeff
Original Article Translation: zhouzhou, BlockBeats

Editor's Note: The article evaluates the performance of multiple crypto AI projects in ecosystem development, product iteration, community distribution, and token value, believing that Virtuals excels in speed and momentum maintenance, CreatorBid, while slow in execution, has a clear vision, focuses on the Bittensor smart agent ecosystem, and has promising long-term potential. The overall AI agent track is still in its early stages, with future focus possibly shifting to infrastructure and real consumer scenarios.

Below is the original content (lightly edited for readability):

It has been about 7 months since the AI Agent craze began. This wave originally started with the birth of @truth_terminal ➙ @pmarca investing in it ➙ someone creating a token for it ➙ it starting to promote the token ➙ @virtuals_io launching a tokenized agent platform ➙ the emergence of AIDOL and conversational agent stages ➙ the alpha agent stage, where @aixbt_agent rose to prominence ➙ the framework stage, with @elizaOS (formerly ai16z) initiating an open AI developer movement ➙ small-scale AI x gaming attempts (but no survivors) ➙ the DeFAI stage (with a strong vision but lacking execution).

This roughly sums up the main stages of the AI Agent track.

From these stages, a few reliable AI agent teams have emerged — they remain active, continuously launching new products and features (although primarily sustained by early accumulated transaction fee revenue).

Most importantly, there are still some ecosystems standing strong, providing support to developers, helping product ideas take off from scratch, and driving AI products and tokens from conceptualization to successful launch.

Role of Ecosystem Leaders

These ecosystem leaders provide extremely valuable support:

· They have a strong distribution network that can bring attention to your token and project;

· They provide product/service integration with the ecosystem's core (i.e., aimed at potential users);

· They offer guidance and incubation services from 0 to 1 and then to 10.

· Support your idea through investment and funding.

In the Web3 AI field, ecosystem leadership remains a core pillar. Because the community is a central part of the crypto world—community is key to whether a token can achieve network effects (unlike the traditional SaaS model relying on subscription fees, Web3 projects depend on tokens to incentivize participation, accelerate growth, and user adoption).

Over the past 7 months, we have seen multiple ecosystem leaders rise and fall. But those projects that are still active stand out in the following areas:

· Positioning as an AI Agent app store, where developers/users can access services from Web2 and Web3 to enhance or automate their workflows—@arcdotfun

· Building an economy where autonomous agents interact with each other (and with humans)—@virtuals_io

· Leading the largest-scale Web3 open AI movement—@elizaOS

· Combining Bittensor's subnet intelligence with AI Agent workflows to attract more participants to the @opentensor (Bittensor) ecosystem—@creatorbid

This article will objectively analyze each ecosystem in terms of what they are doing well, who is leading the pack, who is falling behind, and so on.

We will analyze the following aspects:

· Product & Distribution

· AI / Intelligence

· Development Speed

· Token Value Capture

Without further ado, let's start with the first aspect:

Product & Distribution

In Web3, the token itself is often seen as a product. But in this article, we define "product" as a commodity or service that satisfies actual user needs.

In the Web3 AI field, most products revolve around "financialization," meaning they are tools and intelligent services that help people make money—such as the Alpha Terminal, conversational agents that can express emotions about a project, agents that execute trades or predictions aiming to beat the market, and so on.

The success of a product largely depends on "distribution." Generally speaking, this field is 90% distribution + 10% technical architecture. Few in the space care about what models your AI Agent is using; what matters more is whether its outputs are consistent, the insights it shares, and whether the alpha is truly useful.

Virtuals

@virtuals_io has the most diverse set of products in its ecosystem—including alpha signals, terminals, on-chain/off-chain data, agent workflows for auditing and security analysis, robots, investment DAOs, transaction agents, prediction agents, sports analytics, music, DeFi, and more.

Virtuals can be said to excel the most in storytelling and shaping narratives, while also being the best at listening to community feedback and iterating quickly (earning them the title of "survivors").

However, despite offering a wide range of services, only a few teams are actually providing products that deliver tangible value to users (rather than just entertainment).

Virtuals was the first player to pioneer an AI Agent launchpad, enabling anyone to launch a conversational agent and bind it to a token. This mechanism is a double-edged sword—Virtuals can initially charge fees for these launches and extract value, but because anyone can launch, it has attracted a large number of short-term speculators and value extractors who may repeatedly mint tokens, or even rug pull right after launch.

(However, Virtuals is developing the ACP, and hopefully, we will soon see some flagship agent products and services.)

Arc

Players like @arcdotfun have taken a completely different path.

They did not choose to build a "launch platform" and encourage as many projects to go live as possible, but instead focused on creating an AI Agent marketplace called "Ryzome." Through partnerships with a few high-quality projects, they integrate these projects' products and services into their MCP infrastructure.

In addition, they will launch a "Ryzome Canvas," a no-code/node-based Agent building tool where users can access common MCP server resources, as well as services and use cases provided by Arc partners, to custom create agent workflows (similar to Rayon Labs' Squad tool).
Users can sell these workflows, tokenize them, and launch them on Arc's Forge (their launch platform).

(In short, Arc follows the "polish the product first, then discuss distribution" route. Meanwhile, Ryzome will soon enter open testing.)

Eliza

Among all the frameworks, the most flexible and versatile is none other than @elizaOS.

Eliza supports various integrations, such as achieving secure execution through TEE, conducting transactions, analyzing real-time on-chain data, executing smart contracts, managing wallets, and more.

This framework supports a multi-agent system, allowing developers to create a group of agents with different personalities, goals, and key performance indicators (KPIs) to collaboratively complete tasks (such as transactions, social media automation, business process automation).

Because of this, Eliza's user base continues to grow, currently with around 16,000 stars and 5,100 forks on GitHub.

However, although Eliza's framework is widely used, it initially lacked a distribution channel. Unlike Virtuals, Eliza did not capitalize on the hype and traffic dividends in the early AI Agent takeoff phase (end of last year).

This situation changed a few weeks ago—Eliza introduced @autodotfun, a SOL-priced launchpad (the next phase will introduce the $ai16z liquidity pool) and pledged to use a portion of transaction fees to buy back $ai16z tokens.

However, so far, autodotfun has not shown a significant difference in similar launch platforms, nor have any truly interesting or unique projects gone live, which is somewhat disappointing.

(Eliza's greatest strength and weakness actually lie with @shawmakesmagic: without Shaw's countless hours of intense commitment, this framework would not exist at all; however, he also often experiences "power outages" and makes questionable decisions, leading to market FUD, a situation that has occurred multiple times.)

AI / Intelligence Capability

As mentioned earlier, most of the time, the market is more focused on the "product" and "distribution" rather than the underlying architecture or the AI model itself.

However, if you have a powerful and ever-evolving intelligent system, it is still possible to develop a more user-centric product.

For example: a model specifically trained on on-chain data would be stronger in analyzing on-chain information than a general model; a model trained on sports match data, crowdsourced intelligence, real-time data would also have the advantage in predicting match results.

Bittensor remains the ecosystem with the most diverse intelligent models to date, and the only one truly committed to integrating Bittensor subnet intelligence with AI Agent/Agentic workflows is @CreatorBid.

This team has shown poor performance in distribution (slow onboarding of new agents, slow iteration pace), but they have a clear focus on "steadfastly supporting Bittensor." (They have not yet made an official announcement, but they may be launching a subnet called SN98 Creator to further incentivize building agentic workflows based on Creatorbid and taking them online.)

Development Speed / User Growth / Project Launch Pace

In Web3, if you are building a long-term product, you must consider: how to keep the community engaged in the short to medium term.

If you fail to "entertain" the community, the token price often declines over time because no one wants to be stuck for the long term. In contrast, the market prefers projects that can sustainably generate topics, openly build, and create.

Virtuals are the strongest player in this regard, with open development, quick issue resolution, active community feedback incorporation, regular feature or narrative releases to maintain user interest, and also in building their ACP. In addition, they often have Genesis Launches for new user participation.

Eliza ranks second in distribution capability, thanks to its developer network and collaborations with multiple L1/L2 networks. Eliza is also the preferred framework for deploying agents on other chains (non-Solana). autodotfun also provides an easier path for projects to go live.

Arc's Ryzome and Ryzome Canvas are currently in progress. Once released, they may drive ecosystem activity upwards, activating more Forge project launches.

On the Creatorbid front, top agents have recently introduced new features (although the valuation range has not changed much). CB may be preparing to launch an agent driven by the Bittensor subnet and take its subnet online. The overall pace is slow, and hopefully, it will accelerate in the future.

Token Value Capture

$VIRTUAL currently has the strongest token value capture. It is the primary currency built on LPs in the Virtuals ecosystem, and entering Virtuals as an agent also requires its use. The recent Genesis Launch introduced Virgen points, which flow towards $VIRTUAL and other ecosystem tokens, further enhancing the holding value of $VIRTUAL.

$ai16z could be the second strongest player. autodotfun sees a daily trading volume of $2-3 million (still far below Virtuals and other platforms), with some fees used for $ai16z buybacks. However, Eliza needs to onboard high-quality projects as soon as possible, especially those with a market cap exceeding tens of millions of dollars, or else attention will continue to focus on Virtuals.

$arc's value capture comes from LP transaction fees, as well as future revenue generated by developers on Ryzome. However, this path is still in its early stages and requires time to materialize.

$BID has the most unique tokenomics, as its circulating supply is lower than similar projects, allowing for platform engagement incentives through token releases. However, currently, these releases have not been fully utilized, and the trading volume remains low (between $100,000 and $500,000 per day).

Summary:

Each of the above projects has its own strengths, but in the medium term, "distribution capability" + "ability to attract speculative capital" (i.e., trading volume) are the most critical moats.

The key to the system's operation lies in whether you can sustainably generate hype and attract players to continuously place bets in your "casino." In this regard, Virtuals is currently the best-performing project.

Whether they can maintain long-term hype and translate it into real product strength is worth observing in the future.

Although @CreatorBid's execution still needs improvement, personally, I am most optimistic about them because their vision aligns with mine—to bring high-quality AI to the masses, truly commercializing agentic workflows.

Imagine: an ever-evolving trading signal system consistently outperforming the market, which is then transformed into a fully automated trading Agent—this is the vision of the SN8 Proprietary Trading Network.

We are still in the early stages of the market, and it is unclear who will ultimately emerge victorious. More complex use cases are being handled by large teams outside the ecosystem, such as:

· @vana—focused on data ownership

· @NousResearch—reinforcement learning

· @TheoriqAI—liquidity provision system

· @gizatechxyz—focused on financial/stablecoin-related agents

In the future, how the leaders of the AI Agent ecosystem position themselves will determine whether they can seize the growth opportunities of the next cycle. We may also see more DeAI infrastructure implementation, increased decentralization of agent systems, and entrepreneurial opportunities across various layers of the tech stack.

Ultimately, speculative fervor may shift from individual agent tokens to the core infrastructure powering open AI systems. Perhaps we will witness AI products that are truly consumer-facing and generate real income, rather than being supported by short-term speculative bubbles driven by 'degen' trading.

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