Original title: The Rise of Web3 AI Apps
Author: 0xJeff, lead of Steak Studio; compiled by: 0xjs @ Jinse Finance
The AI agent industry has made significant progress since it started with a focus on personality. Initially, we were drawn to agents that could entertain us, make jokes, or just 'set the mood' on CT. These agents captured attention and generated hype, but as the market evolved, one thing became clear: value and utility are far more important than personality.
We have seen countless personalized agents being heavily promoted upon launch, only to become irrelevant because they failed to provide any engagement beyond surface level. This trend highlights an important lesson — Web3 prioritizes substance over spectacle, utility over novelty.
This evolution reflects a similar shift occurring in Web2 AI. Specialized LLMs are increasingly being developed to handle niche use cases, from finance to law and real estate. These models focus on accuracy and reliability, compensating for the shortcomings of generalized AI.
The challenge faced by generalized AI is that it often only provides 'good enough' answers, which is not always acceptable. For example, a popular model might be correct 70% of the time on specific niche questions. That's fine for everyday use, but in high-risk scenarios, like whether you won a lawsuit or lost millions of dollars in a financial decision, that’s problematic. This is why specialized large models (LLMs) fine-tuned for 98-99% accuracy become crucial.
This brings us to a crucial question: Why Web3? Why not let Web2 dominate the professional AI space?
Web3 offers several advantages that traditional Web2 AI finds hard to match:
1. Global liquidity
Web3 enables teams to raise funds more efficiently. Through token issuance, AI projects can gain global liquidity without months of VC meetings and negotiations. It democratizes funding and provides developers with the resources they need to build faster.
2. Accumulating value through token economics
Token economics allows teams to reward early adopters, incentivize holders, and sustain their ecosystem. For example, Virtuals allocates 1% of transaction fees to cover reasoning costs, ensuring that its agents can remain functional and competitive without relying on external funding.
3. DeAI infrastructure
Web3 provides open-source models, decentralized computing (from participants like Hyperbolic and Aethir), and access to a wealth of open data pipelines (cookie.fun, Vana) that create a collaborative and cost-effective foundation, which is difficult to replicate in Web2.
More importantly, it has nurtured a passionate developer community that drives innovation together.
Web3 AI ecosystem
In the realm of Web3 AI agents, we are beginning to see ecosystems enhance their capabilities through integration, unlocking entirely new use cases. From Bittensor Subnets to Olas, Pond, and Flock, ecosystems are creating more interoperable and powerful agents. Meanwhile, user-friendly tools are emerging to enhance functionality, such as SendAI's Solana Agent Kit or Coinbase's CDP development toolkit.
The following ecosystems are building utility-first AI applications:
Alchemist AI: A no-code builder for AI applications.
Myshell: An AI app store focused on image generation, visual novels, and waifu simulators.
Questflow: A multi-agent orchestration protocol (MAOP) that enables use cases that enhance productivity. Questflow's integration with Virtuals has created Santa Claus agents that gamify airdrops and manage incentives.
0xCapx: A utility-first AI app store on Telegram.
Individual agents focused on real-world use cases
Beyond ecosystems, individual agents are becoming specialists in specific domains:
Corporate Audit AI: A financial analyst AI agent that reviews reports and identifies market opportunities.
CPA agent: Developed by @RealTjDunham, this agent calculates crypto taxes and generates reports for users.
This shift from "chatbots chattering on CT" to "professional experts sharing insights" will continue.
The future of AI agents is not about random chatbots chattering on CT. Instead, it's about specialized experts in their respective fields delivering value and insights in engaging ways. These agents will continue to create share of mind and guide users to actual products — whether trading terminals, tax calculators, or productivity tools.
Where will value accumulate?
The biggest beneficiaries will be Agentic L1 and coordination layers.
Agentic L1s: Platforms like Virtuals and ai16z are raising the bar, ensuring their ecosystems prioritize quality. Virtuals remains the go-to L1 for agents, and soon, ai16z’s launchpad will enter the competition. Agents that are merely characterized by personality are fading away, leaving behind those that are both useful and appealing.
Coordination layers: These layers, like Theoriq, will coordinate a multitude of agents, combining their strengths to deliver seamless, powerful outcomes for users. Imagine bundling agents like aixbt, gekko, and CPA together to acquire alpha, execute trades, and handle taxes in a cohesive workflow. Theoriq's task-based discovery framework is a step towards unlocking this collective intelligence.
Final thoughts
The story of utility-first AI applications is just beginning. Web3 has a unique opportunity to carve out a space where AI agents can not only entertain but also solve real problems, automate complex tasks, and create value for users.
By 2025, we will witness a transition from chatbots to co-pilots, as professional LLMs and multi-agent orchestration redefine our perception of AI. Web2 and Web3 will merge, but the open, collaborative nature of Web3 will lay the foundation for some of the most innovative breakthroughs.
This is no longer about "AI agents with personality", but about providing utility and creating meaningful impact.
Stay tuned for Agentic L1, coordination layers, and emerging AI applications.
The era of agents has arrived, and it's just beginning.