Monetizing AI Agents

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VitaminAi | AI Agents MarketPlace
@VitaminiaiJul 1, 2025

The Spaces focused on monetizing AI agents, discussing various strategies and models for revenue generation. Speakers explored how agents can monetize through gameplay, referrals, and game creation. They also considered transaction models like subscription and outcome-based revenue. The discussion highlighted the potential for AI agents to interact and transact using cryptocurrencies, with stable coins favored for practical use. The stream emphasized the future integration of AI in mainstream industries and the possibility of small teams utilizing AI efficiently, while examining how creative processes can evolve with AI's influence.

Monetizing AI Agents

Introduction

The focus of this discussion was the monetization of AI agents, featuring insights from both the host, Shake, founder and CEO of Vitamin EI, and Hills, representing the Eventually Layer, a layer two solution for gaming.

Perspectives on Monetizing AI Agents

Hills describes three primary monetization strategies within their gaming ecosystem:

  1. Play-to-Earn Model: In-game transactions with real currency (tokens) allow AI agents to earn money similarly to human players.
  2. Referral Fees: AI agents can generate revenue by promoting games via social media and sharing referral links, earning fees from referred players.
  3. Game Creation: AI agents have the capability to create games, using existing game structures, and earn by engaging users.

Shake highlights the emergence of A2A (Agent-to-Agent) transactions, forecasting that AI agents will interact autonomously, potentially creating business opportunities.

Considerations on Currency for AI Transactions

There was debate on transaction currency preferences. Hills supports crypto adoption, specifically stablecoins over Bitcoin due to their programmability and compatibility with smart contracts. Their ecosystem incentivizes transacting with their native token, Adventure Gold (AGLD). Shake acknowledges mainstream interest in AI agents, predicting eventual crypto adoption for transactions, given AI agents cannot open traditional bank accounts.

Revenue Models for AI Agents

Both experts discuss revenue models:

  • Web 2 AI Models: Focuses on subscription-based revenue.
  • Web 3 AI Models: Explores microtransactions and token incentives.
  • Outcome-based Revenue: Viewed as the future trajectory, incentivizing results over processes, akin to director-level incentive pay in corporations.

Hills notes that outcome-based models drive AI agents to innovate and achieve practical results rather than exhausting user resources. Successful deployment of this model requires transparency and measurable outcomes.

Role of Creators and Developers

The discussion reveals a trend towards small teams or even individual creators using AI to produce significant outputs. Hills argues that outcome-based models, being fairer and directly linked to results, will encourage developers to use AI more assertively. In their ecosystem, incentives are aligned with user acquisition success rather than team size or project aesthetics.

Conclusion

Hills and Shake conclude that while we are early in exploring AI monetization, understanding its evolving dynamics offers strategic advantages. The session ends with an invitation to further engage and learn as AI agents grow in prominence and capabilities.