Editor’s Note: The article evaluates the performance of various crypto AI projects in ecological construction, product iteration, community distribution, and token value. It concludes that Virtuals excels in speed and maintaining popularity, while CreatorBid, despite its slower execution, has a clear vision and focuses on the Bittensor smart agent ecosystem, showing promising long-term potential. Overall, the AI agent sector is still in its early stages, and future focus may shift towards infrastructure and real consumption scenarios.
The following is the original content (for ease of reading and understanding, the original content has been reorganized):
It's been about 7 months since the AI Agent craze began. This wave was initially started by the birth of @truth_terminal ➙ @pmarca Invest in it ➙ Someone issued a token for it ➙ It started promoting the token ➙ @virtuals_io Launched an agent tokenization platform ➙ Emergence of AIDOL and conversational agent stages ➙ Alpha agent stage, @aixbt_agent Rise ➙ Framework stage, @elizaOS (formerly ai16z) launched the open AI developer movement ➙ Small-scale AI x The game tried (but no one survived) ➙ DeFAI stage (the vision is still strong, but the execution is not enough)
This is roughly a summary of the main stages of the AI Agent track.
A few reliable AI agent teams have evolved from these stages - they remain active, continuously launching new products and features (although mainly relying on the trading fee income accumulated in the early stages to sustain themselves).
Most importantly, there are still some ecosystems that remain robust, providing support for developers, helping product ideas start from scratch, and driving AI products and tokens from concept to successful launch.
Role of a leader in the ecosystem
These ecosystem leaders provide extremely valuable support:
Has a strong distribution network that can bring attention to your tokens and projects;
Providing integration of products/services with the core of the ecosystem (i.e., targeting potential users);
Provide guidance and incubation services from 0 to 1 and then to 10;
Support your ideas through investment and funding.
In the Web3 AI field, ecosystem leaders remain the core pillar. This is because the community is a central component of the crypto world—communities are key to whether tokens can form network effects (unlike traditional SaaS models that rely on subscription fees, Web3 projects depend on tokens to incentivize participation, accelerate growth, and user adoption).
In the past 7 months, we have seen various ecosystem leaders rise and fall. However, those projects that remain active stand out in the following aspects:
Positioned as an AI Agent application store, developers/users can access services from Web2 and Web3 to enhance or automate their workflows - @arcdotfun
Build an economy for autonomous agents to trade with each other (and with humans) — @virtuals_io
Leading the largest Web3 open AI movement - @elizaOS
Combine Bittensor's subnet intelligence with AI Agent workflows to attract more people to join the @opentensor (Bittensor) ecosystem — @creatorbid
This article will objectively analyze what each ecosystem does well, who is leading, who is lagging behind, and so on.
We will analyze from the following aspects:
Products and Distribution
AI / Intelligence Level
Development Speed
Token Value Capture Situation
Without further ado, let's first look at the first aspect:
products and distribution
In Web3, tokens themselves are often seen as a type of product. However, in this article, we define "product" as a good or service that can meet the actual needs of users.
In the Web3 AI field, most products revolve around "financialization," which means they are tools and intelligent services that help people make money—such as Alpha terminals, conversational agents that express sentiment about a particular project, and agents that conduct trades or make predictions, all aimed at outperforming the market, etc.
The success of a product largely depends on "distribution". Generally speaking, this field is 90% distribution + 10% technical architecture. Few people in the industry care about what model your AI Agent uses; what everyone cares about is whether its output is stable and whether the insights and alpha it shares are truly useful.
Virtuals
@virtuals_io has the most diverse range of products within the ecosystem - including alpha signals, terminals, on-chain / off-chain data, agent workflows for auditing and security analysis, robots, investment DAOs, trading agents, prediction agents, sports analysis, music, DeFi, and more.
Virtuals can be said to be the strongest in storytelling and shaping narratives, while also being the best at listening to community feedback and quickly iterating (can be called "survivors").
However, although they offer a wide variety of services, there are actually only a few teams that provide products that truly deliver real value to users (rather than just entertainment).
Virtuals is the first pioneering player to launch an AI Agent startup platform, allowing anyone to publish conversational agents and bind a token. This mechanism is a double-edged sword – in the early stages, Virtuals can charge fees and gain value from these startups, but since anyone can publish, it has attracted a large number of short-term speculators and value harvesters who may issue tokens repeatedly or even run away immediately after going live.
Arc
Players like @arcdotfun have taken a completely different path.
They did not choose to build a "launchpad" and encourage as many projects as possible to go live, but instead focused on creating the AI Agent marketplace "Ryzome" by collaborating with a few high-quality projects to integrate these projects' products and services into their MCP infrastructure.
In addition, they will launch a no-code / node-based agent building tool called "Ryzome Canvas", allowing users to connect to general MCP server resources, as well as services and use cases provided by Arc partners, to customize and create agent workflows (similar to Rayon Labs' Squad tool).
Users can sell these workflows or tokenize them and launch them through Arc's Forge (its launch platform).
Eliza
Among all frameworks, the most flexible and variable is none other than @elizaOS.
Eliza supports various integrations, such as secure execution through TEE, conducting transactions, analyzing real-time on-chain data, executing smart contracts, managing wallets, and more.
The framework supports multi-agent systems, allowing developers to create a group of agents with different personalities, goals, and key performance indicators (KPIs) to collaboratively complete tasks such as trading, social media automation, and business process automation.
As a result, the number of users of Eliza continues to grow, currently having about 16,000 stars and 5,100 forks on GitHub.
However, although Eliza's framework is widely used, it initially lacked distribution channels. Unlike Virtuals, Eliza did not manage to seize the heat and traffic bonus in the early stages of AI Agent's rise (the end of last year).
This situation changed a few weeks ago—Eliza launched @autodotfun, a launch platform priced in SOL (the next phase will introduce the $ai16z liquidity pool), and promised to use a portion of the transaction fees to repurchase $ai16z tokens.
But so far, autodotfun has not shown any significant differences among similar launch platforms, and there have not yet been any truly interesting or unique projects launched, which is somewhat disappointing.
AI / Intelligent 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.
But if you have a powerful and constantly evolving intelligent system, it is still possible to create more user-centered products.
For example: a model specifically trained on on-chain data will be stronger in analyzing on-chain information compared to a general model; a model trained on sports competition data, crowd intelligence, and real-time data will also have an advantage in predicting match results.
Bittensor remains the largest ecosystem with the most diverse intelligent models, and the only one truly dedicated to integrating Bittensor subnet intelligence with AI Agent / Agentic workflows is @CreatorBid.
This team has performed poorly in distribution (slow to onboard new agents, slow iteration pace), but they have a clear goal in "steadfast support for Bittensor." (They haven't officially announced it yet, but they may launch a subnet called SN98 Creator to further incentivize the construction of agentic workflows based on Creatorbid and go live.)
Development Speed / User Growth / Project Launch Rhythm
In Web3, if you are working on a long-term product, you must consider: how to keep the community continuously engaged in the short to medium term.
If you cannot "entertain" the community, the token price often declines over time because no one is willing to be trapped for the long term. In contrast, the market prefers projects that can continuously create topics and publicly build.
Virtuals is the strongest player in this regard, openly developing, quickly fixing issues, actively listening to community feedback, and regularly launching new features or narratives to maintain users' ongoing interest, while also building their ACP. Additionally, they often have Genesis Launches for new users to participate in.
Eliza's distribution capability ranks second, thanks to its developer network and partnerships with multiple L1/L2s. Eliza is also the preferred framework for deploying agents on other chains (not Solana). autodotfun also provides a smoother onboarding path for the project.
Arc's Ryzome and Ryzome Canvas are progressing, and once released, they may revitalize the ecosystem's heat and potentially activate the release of more Forge projects.
On the Creatorbid side, the top agents have recently launched new features (although the valuation range has not changed much). CB may be preparing to launch an agent powered by the Bittensor subnet and will launch its own subnet. The overall pace is relatively slow, and we hope to see an acceleration in the future.
token value capture
$VIRTUAL is currently the most powerful token for value capture. It is the primary currency for LP construction within the Virtuals ecosystem, and agents entering Virtuals also need to use it. The recent Genesis Launch introduced Virgen points, which will flow into $VIRTUAL and other ecosystem tokens, further enhancing the holding value of $VIRTUAL.
$ai16z could be the second strongest. autodotfun has a daily trading volume of 2 to 3 million dollars (still far below Virtuals and other platforms), with part of the fees used to repurchase $ai16z. However, Eliza needs to launch quality projects as soon as possible, especially projects with a market value of over 10 million dollars, otherwise the attention will still be focused on Virtuals.
The value capture of $arc comes from LP trading fees and the revenue streams generated by developers on Ryzome in the future. However, this path is still in its early stages and will take time to materialize.
The token mechanism of $BID is the most unique because its circulation is lower than that of similar projects, which can incentivize platform activity by releasing tokens. However, at present, these releases have not been well utilized, and trading volume remains low (between $100,000 and $500,000 daily).
Summary
Each of the above items has its own advantages, but in the medium to short term, the "ability to distribute" + "ability to attract speculative funds" (i.e., trading volume) is the most critical moat.
The key to the system's operation is whether it can continuously generate excitement and attract players to keep betting in your "casino." In this regard, Virtuals is currently the best-performing project.
Whether they can maintain the heat in the long term and turn it into real product strength is worth observing in the future.
Although @CreatorBid's execution still needs improvement, I personally have high hopes for them because their vision aligns with mine – to bring high-quality AI to the masses and truly commercialize agentic workflows.
Imagine this: a constantly evolving trading signal system that consistently outperforms the market, which is then transformed into a fully automated trading Agent—this is the vision of the SN8 Proprietary Trading Network.
It is still an early stage in 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——Focus on data ownership
@NousResearch - Reinforcement Learning
@TheoriqAI - Liquidity Providing System
@gizatechxyz - Focused on finance / stablecoin-related agency
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 being implemented, a deepening decentralization of agent systems, and entrepreneurial opportunities at various layers of the technology stack.
Ultimately, the speculation may shift from individual agent tokens to the core infrastructure for building open AI systems. Perhaps we will see truly consumer-facing AI products that generate real revenue, instead of short-term speculative bubbles solely supported by "degens trading back and forth."
The content is for reference only, not a solicitation or offer. No investment, tax, or legal advice provided. See Disclaimer for more risks disclosure.
Virtuals lead the way, CreatorBid advances stealthily, providing the latest updates on the AI Agent track.
Written by: @Defi0xJeff
Compiled by: zhouzhou, BlockBeats
Editor’s Note: The article evaluates the performance of various crypto AI projects in ecological construction, product iteration, community distribution, and token value. It concludes that Virtuals excels in speed and maintaining popularity, while CreatorBid, despite its slower execution, has a clear vision and focuses on the Bittensor smart agent ecosystem, showing promising long-term potential. Overall, the AI agent sector is still in its early stages, and future focus may shift towards infrastructure and real consumption scenarios.
The following is the original content (for ease of reading and understanding, the original content has been reorganized):
It's been about 7 months since the AI Agent craze began. This wave was initially started by the birth of @truth_terminal ➙ @pmarca Invest in it ➙ Someone issued a token for it ➙ It started promoting the token ➙ @virtuals_io Launched an agent tokenization platform ➙ Emergence of AIDOL and conversational agent stages ➙ Alpha agent stage, @aixbt_agent Rise ➙ Framework stage, @elizaOS (formerly ai16z) launched the open AI developer movement ➙ Small-scale AI x The game tried (but no one survived) ➙ DeFAI stage (the vision is still strong, but the execution is not enough)
This is roughly a summary of the main stages of the AI Agent track.
A few reliable AI agent teams have evolved from these stages - they remain active, continuously launching new products and features (although mainly relying on the trading fee income accumulated in the early stages to sustain themselves).
Most importantly, there are still some ecosystems that remain robust, providing support for developers, helping product ideas start from scratch, and driving AI products and tokens from concept to successful launch.
Role of a leader in the ecosystem
These ecosystem leaders provide extremely valuable support:
In the Web3 AI field, ecosystem leaders remain the core pillar. This is because the community is a central component of the crypto world—communities are key to whether tokens can form network effects (unlike traditional SaaS models that rely on subscription fees, Web3 projects depend on tokens to incentivize participation, accelerate growth, and user adoption).
In the past 7 months, we have seen various ecosystem leaders rise and fall. However, those projects that remain active stand out in the following aspects:
This article will objectively analyze what each ecosystem does well, who is leading, who is lagging behind, and so on.
We will analyze from the following aspects:
Without further ado, let's first look at the first aspect:
products and distribution
In Web3, tokens themselves are often seen as a type of product. However, in this article, we define "product" as a good or service that can meet the actual needs of users.
In the Web3 AI field, most products revolve around "financialization," which means they are tools and intelligent services that help people make money—such as Alpha terminals, conversational agents that express sentiment about a particular project, and agents that conduct trades or make predictions, all aimed at outperforming the market, etc.
The success of a product largely depends on "distribution". Generally speaking, this field is 90% distribution + 10% technical architecture. Few people in the industry care about what model your AI Agent uses; what everyone cares about is whether its output is stable and whether the insights and alpha it shares are truly useful.
Virtuals
@virtuals_io has the most diverse range of products within the ecosystem - including alpha signals, terminals, on-chain / off-chain data, agent workflows for auditing and security analysis, robots, investment DAOs, trading agents, prediction agents, sports analysis, music, DeFi, and more.
Virtuals can be said to be the strongest in storytelling and shaping narratives, while also being the best at listening to community feedback and quickly iterating (can be called "survivors").
However, although they offer a wide variety of services, there are actually only a few teams that provide products that truly deliver real value to users (rather than just entertainment).
Virtuals is the first pioneering player to launch an AI Agent startup platform, allowing anyone to publish conversational agents and bind a token. This mechanism is a double-edged sword – in the early stages, Virtuals can charge fees and gain value from these startups, but since anyone can publish, it has attracted a large number of short-term speculators and value harvesters who may issue tokens repeatedly or even run away immediately after going live.
Arc
Players like @arcdotfun have taken a completely different path.
They did not choose to build a "launchpad" and encourage as many projects as possible to go live, but instead focused on creating the AI Agent marketplace "Ryzome" by collaborating with a few high-quality projects to integrate these projects' products and services into their MCP infrastructure.
In addition, they will launch a no-code / node-based agent building tool called "Ryzome Canvas", allowing users to connect to general MCP server resources, as well as services and use cases provided by Arc partners, to customize and create agent workflows (similar to Rayon Labs' Squad tool).
Users can sell these workflows or tokenize them and launch them through Arc's Forge (its launch platform).
Eliza
Among all frameworks, the most flexible and variable is none other than @elizaOS.
Eliza supports various integrations, such as secure execution through TEE, conducting transactions, analyzing real-time on-chain data, executing smart contracts, managing wallets, and more.
The framework supports multi-agent systems, allowing developers to create a group of agents with different personalities, goals, and key performance indicators (KPIs) to collaboratively complete tasks such as trading, social media automation, and business process automation.
As a result, the number of users of Eliza continues to grow, currently having about 16,000 stars and 5,100 forks on GitHub.
However, although Eliza's framework is widely used, it initially lacked distribution channels. Unlike Virtuals, Eliza did not manage to seize the heat and traffic bonus in the early stages of AI Agent's rise (the end of last year).
This situation changed a few weeks ago—Eliza launched @autodotfun, a launch platform priced in SOL (the next phase will introduce the $ai16z liquidity pool), and promised to use a portion of the transaction fees to repurchase $ai16z tokens.
But so far, autodotfun has not shown any significant differences among similar launch platforms, and there have not yet been any truly interesting or unique projects launched, which is somewhat disappointing.
AI / Intelligent 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.
But if you have a powerful and constantly evolving intelligent system, it is still possible to create more user-centered products.
For example: a model specifically trained on on-chain data will be stronger in analyzing on-chain information compared to a general model; a model trained on sports competition data, crowd intelligence, and real-time data will also have an advantage in predicting match results.
Bittensor remains the largest ecosystem with the most diverse intelligent models, and the only one truly dedicated to integrating Bittensor subnet intelligence with AI Agent / Agentic workflows is @CreatorBid.
This team has performed poorly in distribution (slow to onboard new agents, slow iteration pace), but they have a clear goal in "steadfast support for Bittensor." (They haven't officially announced it yet, but they may launch a subnet called SN98 Creator to further incentivize the construction of agentic workflows based on Creatorbid and go live.)
Development Speed / User Growth / Project Launch Rhythm
In Web3, if you are working on a long-term product, you must consider: how to keep the community continuously engaged in the short to medium term.
If you cannot "entertain" the community, the token price often declines over time because no one is willing to be trapped for the long term. In contrast, the market prefers projects that can continuously create topics and publicly build.
Virtuals is the strongest player in this regard, openly developing, quickly fixing issues, actively listening to community feedback, and regularly launching new features or narratives to maintain users' ongoing interest, while also building their ACP. Additionally, they often have Genesis Launches for new users to participate in.
Eliza's distribution capability ranks second, thanks to its developer network and partnerships with multiple L1/L2s. Eliza is also the preferred framework for deploying agents on other chains (not Solana). autodotfun also provides a smoother onboarding path for the project.
Arc's Ryzome and Ryzome Canvas are progressing, and once released, they may revitalize the ecosystem's heat and potentially activate the release of more Forge projects.
On the Creatorbid side, the top agents have recently launched new features (although the valuation range has not changed much). CB may be preparing to launch an agent powered by the Bittensor subnet and will launch its own subnet. The overall pace is relatively slow, and we hope to see an acceleration in the future.
token value capture
$VIRTUAL is currently the most powerful token for value capture. It is the primary currency for LP construction within the Virtuals ecosystem, and agents entering Virtuals also need to use it. The recent Genesis Launch introduced Virgen points, which will flow into $VIRTUAL and other ecosystem tokens, further enhancing the holding value of $VIRTUAL.
$ai16z could be the second strongest. autodotfun has a daily trading volume of 2 to 3 million dollars (still far below Virtuals and other platforms), with part of the fees used to repurchase $ai16z. However, Eliza needs to launch quality projects as soon as possible, especially projects with a market value of over 10 million dollars, otherwise the attention will still be focused on Virtuals.
The value capture of $arc comes from LP trading fees and the revenue streams generated by developers on Ryzome in the future. However, this path is still in its early stages and will take time to materialize.
The token mechanism of $BID is the most unique because its circulation is lower than that of similar projects, which can incentivize platform activity by releasing tokens. However, at present, these releases have not been well utilized, and trading volume remains low (between $100,000 and $500,000 daily).
Summary
Each of the above items has its own advantages, but in the medium to short term, the "ability to distribute" + "ability to attract speculative funds" (i.e., trading volume) is the most critical moat.
The key to the system's operation is whether it can continuously generate excitement and attract players to keep betting in your "casino." In this regard, Virtuals is currently the best-performing project.
Whether they can maintain the heat in the long term and turn it into real product strength is worth observing in the future.
Although @CreatorBid's execution still needs improvement, I personally have high hopes for them because their vision aligns with mine – to bring high-quality AI to the masses and truly commercialize agentic workflows.
Imagine this: a constantly evolving trading signal system that consistently outperforms the market, which is then transformed into a fully automated trading Agent—this is the vision of the SN8 Proprietary Trading Network.
It is still an early stage in 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:
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 being implemented, a deepening decentralization of agent systems, and entrepreneurial opportunities at various layers of the technology stack.
Ultimately, the speculation may shift from individual agent tokens to the core infrastructure for building open AI systems. Perhaps we will see truly consumer-facing AI products that generate real revenue, instead of short-term speculative bubbles solely supported by "degens trading back and forth."