AI Agents: Evolving Beyond Personality

Intermediate1/8/2025, 8:01:09 AM
This article explores the evolution of AI agents as they shift from personality-driven to utility-focused systems. It examines how Web3 AI agents have developed, highlighting the triumph of practical value over simple personalized interactions. By examining specialized large language models in finance and law, alongside Web3's key strengths—global liquidity, tokenomics, and decentralized infrastructure—it presents a vision of AI agents in 2025 evolving from basic chatbots into sophisticated professional co-pilots.

Forward the Original Title: The Rise of Web3 AI Apps

The AI agent sector has come a long way since it first started with agents focused on personality. At the beginning, we were captivated by agents that could entertain us, make jokes, or simply “vibe” on CT. These agents grabbed attention and built hype, but as the market evolved, one thing became clear: value and utility matter far more than personality.

We’ve seen countless personality-driven agents launch with huge initial hype, only to fade into irrelevance as they failed to deliver anything beyond surface-level engagement. This trend highlights a critical lesson—Web3 prioritizes substance over spectacle, utility over novelty.

This evolution mirrors a similar shift happening in Web2 AI. Specialized LLMs are increasingly being developed to tackle niche use cases, from finance to legal, real estate, and beyond. These models focus on accuracy and reliability, addressing gaps in general-purpose AI.

The challenge with general-purpose AI is that it often provides a “good enough” answer, but that’s not always acceptable. For instance, a popular model might be correct 70% of the time on a specific niche question. That’s fine for casual use but disastrous in high-stakes scenarios—like whether you win a court case or lose millions in a financial decision. This is why specialized LLMs, fine-tuned to achieve 98-99% accuracy, are becoming critical.

And that brings us to a key question: Why Web3? Why not let Web2 dominate the specialized AI landscape?

Web3 offers several advantages that traditional Web2 AI struggles to match:

Global Liquidity

Web3 allows teams to bootstrap funding far more efficiently. With token offerings, an AI project can access global liquidity, bypassing months of VC meetings and negotiations. It democratizes funding, giving developers the resources they need to build faster.

Tokenomics for Value Accrual

Tokens enable teams to reward early adopters, incentivize holders, and sustain their ecosystem. For instance, @virtuals_io allocates 1% of trading fees to cover inference costs, ensuring that their agents remain functional and competitive without relying on external funding.

DeAI Infrastructure

Web3 offers open-source models, decentralized compute (from players like @hyperbolic_labs and @AethirCloud), and access to massive open data pipelines (@cookiedotfun, @withvana). providing a collaborative and cost-effective foundation that’s difficult to replicate in Web2

More importantly, it fosters a community of passionate developers driving innovation together

The Web3 AI Ecosystem

In the Web3 AI agent landscape, we’re starting to see ecosystems improve their capabilities with integrations that unlock entirely new use cases. From Bittensor Subnets to Olas, Pond, and Flock, ecosystems are creating more interoperable, powerful agents. At the same time, easy-to-use tools are emerging to enhance functionality, like @sendaifun’s Solana Agent Kit or @coinbase CDP SDK

Ecosystems like these are building utility-first AI applications:

  • @alchemistAIapp: A no-code builder for AI apps.
  • @myshell_ai: An AI app store focusing on image generation, visual novels, and waifu simulators.

  • @questflow: A Multi-Agent Orchestration Protocol (MAOP) enabling productivity-enhancing use cases. Questflow’s integration with Virtuals created Santa agents that gamify airdrops and manage incentives.

  • @0xCapx: A utility-first AI app store on Telegram.

Individual Agents Specializing in Real-World Use Cases

Beyond ecosystems, individual agents are emerging as experts in specific fields:

@corpauditai: A financial analyst AI agent that reviews reports and identifies market opportunities.

  • $CPA Agent: Built by @RealTjDunham, this agent calculates crypto taxes and generates reports for users.

This shift from “chatbots yapping on CT” to “specialized experts sharing insights” will continue.

The future of AI agents isn’t about random chatbots yapping on CT. It’s about specialized experts in their respective fields, delivering value and insights in an engaging manner. These agents will continue to create mindshare and funnel users into actual products—whether that’s a trading terminal, a tax calculator, or a productivity tool.

Where Will the Value Accrue To?

The biggest beneficiaries will be Agentic L1s and Coordination Layers.

  • Agentic L1s: Platforms like @virtuals_io and @ai16zdao are raising the bar, ensuring their ecosystems prioritize quality. Virtuals remains the top L1 for agents, and soon, @ai16zdao’s launchpad will join the fray. Personality-only agents are dying off, leaving behind agents that are both useful and engaging.
  • Coordination Layers: These layers, like @TheoriqAI, will orchestrate swarms of agents, combining their strengths to deliver seamless, powerful outcomes for users. Imagine tying together agents like aixbt, gekko, and CPA to source alpha, execute trades, and handle taxes—all in one cohesive workflow. Theoriq’s task-based discovery framework is a step toward unlocking this collective intelligence.

Final Thoughts

The narrative of utility-first AI applications is just beginning. Web3 has a unique opportunity to carve out a space where AI agents don’t just entertain but solve real problems, automate complex tasks, and create value for users.

2025 will be the year we see the transition from chatbots to co-pilots, as specialized LLMs and multi-agent orchestration redefine how we think about AI. Web2 and Web3 will converge, but the open, collaborative nature of Web3 will set the stage for some of the most innovative breakthroughs.

It’s no longer about “AI agents with personality.” It’s about agents that deliver utility and create meaningful impact.

Keep your eyes on Agentic L1s, Coordination Layers, and the emerging AI applications. The birth of the Agentic Era is here—and it’s just getting started.

Disclaimer:

  1. This article is reprinted from [0xJeff]. Forward the Original Title: The Rise of Web3 AI Apps. All copyrights belong to the original author [0xJeff]. If there are objections to this reprint, please contact the Gate Learn team, and they will handle it promptly.
  2. Liability Disclaimer: The views and opinions expressed in this article are solely those of the author and do not constitute any investment advice.
  3. Translations of the article into other languages are done by the Gate Learn team. Unless mentioned, copying, distributing, or plagiarizing the translated articles is prohibited.

AI Agents: Evolving Beyond Personality

Intermediate1/8/2025, 8:01:09 AM
This article explores the evolution of AI agents as they shift from personality-driven to utility-focused systems. It examines how Web3 AI agents have developed, highlighting the triumph of practical value over simple personalized interactions. By examining specialized large language models in finance and law, alongside Web3's key strengths—global liquidity, tokenomics, and decentralized infrastructure—it presents a vision of AI agents in 2025 evolving from basic chatbots into sophisticated professional co-pilots.

Forward the Original Title: The Rise of Web3 AI Apps

The AI agent sector has come a long way since it first started with agents focused on personality. At the beginning, we were captivated by agents that could entertain us, make jokes, or simply “vibe” on CT. These agents grabbed attention and built hype, but as the market evolved, one thing became clear: value and utility matter far more than personality.

We’ve seen countless personality-driven agents launch with huge initial hype, only to fade into irrelevance as they failed to deliver anything beyond surface-level engagement. This trend highlights a critical lesson—Web3 prioritizes substance over spectacle, utility over novelty.

This evolution mirrors a similar shift happening in Web2 AI. Specialized LLMs are increasingly being developed to tackle niche use cases, from finance to legal, real estate, and beyond. These models focus on accuracy and reliability, addressing gaps in general-purpose AI.

The challenge with general-purpose AI is that it often provides a “good enough” answer, but that’s not always acceptable. For instance, a popular model might be correct 70% of the time on a specific niche question. That’s fine for casual use but disastrous in high-stakes scenarios—like whether you win a court case or lose millions in a financial decision. This is why specialized LLMs, fine-tuned to achieve 98-99% accuracy, are becoming critical.

And that brings us to a key question: Why Web3? Why not let Web2 dominate the specialized AI landscape?

Web3 offers several advantages that traditional Web2 AI struggles to match:

Global Liquidity

Web3 allows teams to bootstrap funding far more efficiently. With token offerings, an AI project can access global liquidity, bypassing months of VC meetings and negotiations. It democratizes funding, giving developers the resources they need to build faster.

Tokenomics for Value Accrual

Tokens enable teams to reward early adopters, incentivize holders, and sustain their ecosystem. For instance, @virtuals_io allocates 1% of trading fees to cover inference costs, ensuring that their agents remain functional and competitive without relying on external funding.

DeAI Infrastructure

Web3 offers open-source models, decentralized compute (from players like @hyperbolic_labs and @AethirCloud), and access to massive open data pipelines (@cookiedotfun, @withvana). providing a collaborative and cost-effective foundation that’s difficult to replicate in Web2

More importantly, it fosters a community of passionate developers driving innovation together

The Web3 AI Ecosystem

In the Web3 AI agent landscape, we’re starting to see ecosystems improve their capabilities with integrations that unlock entirely new use cases. From Bittensor Subnets to Olas, Pond, and Flock, ecosystems are creating more interoperable, powerful agents. At the same time, easy-to-use tools are emerging to enhance functionality, like @sendaifun’s Solana Agent Kit or @coinbase CDP SDK

Ecosystems like these are building utility-first AI applications:

  • @alchemistAIapp: A no-code builder for AI apps.
  • @myshell_ai: An AI app store focusing on image generation, visual novels, and waifu simulators.

  • @questflow: A Multi-Agent Orchestration Protocol (MAOP) enabling productivity-enhancing use cases. Questflow’s integration with Virtuals created Santa agents that gamify airdrops and manage incentives.

  • @0xCapx: A utility-first AI app store on Telegram.

Individual Agents Specializing in Real-World Use Cases

Beyond ecosystems, individual agents are emerging as experts in specific fields:

@corpauditai: A financial analyst AI agent that reviews reports and identifies market opportunities.

  • $CPA Agent: Built by @RealTjDunham, this agent calculates crypto taxes and generates reports for users.

This shift from “chatbots yapping on CT” to “specialized experts sharing insights” will continue.

The future of AI agents isn’t about random chatbots yapping on CT. It’s about specialized experts in their respective fields, delivering value and insights in an engaging manner. These agents will continue to create mindshare and funnel users into actual products—whether that’s a trading terminal, a tax calculator, or a productivity tool.

Where Will the Value Accrue To?

The biggest beneficiaries will be Agentic L1s and Coordination Layers.

  • Agentic L1s: Platforms like @virtuals_io and @ai16zdao are raising the bar, ensuring their ecosystems prioritize quality. Virtuals remains the top L1 for agents, and soon, @ai16zdao’s launchpad will join the fray. Personality-only agents are dying off, leaving behind agents that are both useful and engaging.
  • Coordination Layers: These layers, like @TheoriqAI, will orchestrate swarms of agents, combining their strengths to deliver seamless, powerful outcomes for users. Imagine tying together agents like aixbt, gekko, and CPA to source alpha, execute trades, and handle taxes—all in one cohesive workflow. Theoriq’s task-based discovery framework is a step toward unlocking this collective intelligence.

Final Thoughts

The narrative of utility-first AI applications is just beginning. Web3 has a unique opportunity to carve out a space where AI agents don’t just entertain but solve real problems, automate complex tasks, and create value for users.

2025 will be the year we see the transition from chatbots to co-pilots, as specialized LLMs and multi-agent orchestration redefine how we think about AI. Web2 and Web3 will converge, but the open, collaborative nature of Web3 will set the stage for some of the most innovative breakthroughs.

It’s no longer about “AI agents with personality.” It’s about agents that deliver utility and create meaningful impact.

Keep your eyes on Agentic L1s, Coordination Layers, and the emerging AI applications. The birth of the Agentic Era is here—and it’s just getting started.

Disclaimer:

  1. This article is reprinted from [0xJeff]. Forward the Original Title: The Rise of Web3 AI Apps. All copyrights belong to the original author [0xJeff]. If there are objections to this reprint, please contact the Gate Learn team, and they will handle it promptly.
  2. Liability Disclaimer: The views and opinions expressed in this article are solely those of the author and do not constitute any investment advice.
  3. Translations of the article into other languages are done by the Gate Learn team. Unless mentioned, copying, distributing, or plagiarizing the translated articles is prohibited.
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