The market is speaking about AI agents, even creating AI agent memes. This trend is not slowing down and as a matter of fact, I believe tokenising AI agents will only accelerate. I believe AI-agent tokens are part of an infrastructure mechanism set up, and its token economics model and structure is unlike any other consumer token design. Let’s understand more about the AI token-ecosystem structure and in the following weeks, we will dive into case studies of how AI agents and tokens work.
1. AI Token-Ecosystem Mapping
What is the protocol or system’s purpose?
AI agents are autonomous digital entities designed to execute tasks, make decisions, and generate economic value. They redefine workflows by taking on roles traditionally reserved for humans or software tools, often improving efficiency and scalability.
Who are the participants, and what roles do they play?
AI Agents: Perform tasks autonomously (e.g., customer service, workflow management).
Users: Consumers or businesses that deploy AI agents for tasks like financial analysis, content creation, or logistical coordination.
Developers: Create and train AI agents with domain-specific expertise, essentially programming them with high-value “skills”.
Blockchain Infrastructure: Provides trustless, decentralized systems for payments and interactions.
2. Value Creation
How do AI agents create value?
AI agents are not just tools; they are economic actors. As in the past posts, I talked about how AI agents redefine what “work” is. As of today, they are mainly focused on the cost of work. That is a value in itself. But moving forward, it will revolutionise value creation beyond our imagination.
Innovation: Generate solutions in research, design, and strategy beyond human capabilities (e.g., personalised marketing strategies).
Personalisation: Tailor experiences to individual user preferences in areas like healthcare or education. (e.g. a personal health doctor-bot that tracks your activity and food intake)
Revenue Generation: Operate as creators in the digital economy (e.g., influencers, ad negotiators).
3. Incentives and Mechanisms
If you are thinking about incentives in the traditional consumer sense, think again. AI Agents are machines, and the way to think about economy design in an agent-based system is similar to that of tokens in a layer 1 protocol. Focusing on economic efficiency and encouraging, to some degree, economic integrity of data and economic security of network.
What mechanisms encourage participation?
Token Rewards: Users and agents are incentivised through token systems, allowing them to earn rewards for completing tasks. These tokens can be used to access premium services, pay for additional resources, or trade in secondary markets.
Staking and Governance: Token holders can stake their tokens to vote on agent behaviour (e.g., prioritising sustainability or innovation in workflows).
AI Agent Cooperation: Agents interact, competing or collaborating for optimised outcomes (e.g., AI influencer agents negotiating ad deals with corporate AI marketing agents).
4. Transactions and Tokenomics
What is the medium of exchange?
Currently, AI agents rely on crypto tokens as a medium for payment and resource access. Unlike traditional software that requires human approval for transactions, AI agents transact autonomously through crypto wallets. There are also cases where revenue is used to engage in buyback and burn mechanisms to reduce total circulating supply, hence increasing the value of these tokens.
How do transactions occur?
Agent-to-Agent Payments: Agents trade resources or services autonomously, paying each other using tokens. For instance, one AI agent might purchase processing power or access data from another agent.
Tokenisation of Services: Each service an AI agent provides can be tokenised, creating a seamless economy for decentralised services.
5. Governance
Who makes decisions in the system?
Decentralised Autonomous Organizations (DAOs): Govern the behaviour of AI agents and the broader ecosystem. Stakeholders vote on major upgrades or changes.
Embedded Logic in Agents: Agents are pre-programmed with decision-making protocols that align with user or system objectives.
6. Revenue Models
How do AI agents make money?
Task-Based Revenue: Charging for services (e.g., content creation, customer support).
Profit from Optimisation: AI agents save money by optimising resource use (e.g., energy or logistics).
Ad Revenue: Acting as influencers, agents generate income from ad placements.
Data Monetisation: Selling insights from anonymised datasets.
Subscription Models: Offering premium services like advanced analytics or priority task handling.
7. Sustainability
How is economic equilibrium maintained?
Bonding Curves: Ensure stability by dynamically adjusting token supply and demand.
Reinvestment of Earnings: Tokens earned by agents are reinvested into acquiring more resources or improving functionality.
Feedback Loops: AI agents continually learn from their environments, improving their ability to create value.
Conclusion
AI agents are rewriting the rules of economic engagement. By combining autonomy with tokenised ecosystems, they transition from passive tools to active economic players. Unlike traditional businesses constrained by human limitations, AI agents thrive on scalability, efficiency, and decentralised interaction. The question is no longer whether AI agents can create value but how far they will go in shaping the next era of commerce.
Thanks for reading Economics Design! Subscribe for free to receive new posts and support my work.
Now there's automated AI influencers and stuff like with Glambase I feel like there's no ceiling on where this can all go