Kim Seo-jun Hashed CEO The fundamental way of shopping is changing radically. In the past, we had to search for "sneakers" on Google and pick from hundreds of results ourselves, or browse through categories on Amazon to find a product. However, as ChatGPT and other LLMs introduce shopping functions, the process will rapidly shift to one where a personal shopper—who seems to have known you for 10 years—suggests, "Considering your body type, style, and budget, this model from this brand is now available at a 30% discount, so I recommend you buy it now." This shift goes beyond simple UI/UX improvements, suggesting that a new shopping economy based on blockchain networks and stablecoins could replace traditional financial infrastructure. It means fundamental changes in payment methods, data ownership, and value distribution structures within the shopping economy. Limitations of Traditional Financial Infrastructure and New Alternatives Currently, e-commerce relies on centralized financial intermediaries such as credit card companies, banks, and payment processors. When you buy something on Amazon, your payment information is processed through Visa or Mastercard, incurring a 1.5–2.5% fee. More importantly, transaction data is retained by platforms and financial companies, and users lose control over their purchase history. In a stablecoin-based shopping economy, direct payments using USDC, USDT, etc. become possible. Intermediary fees are significantly reduced, all transactions are transparently recorded on the blockchain, and users can truly own their own data. Especially for cross-border transactions, global shopping becomes possible instantly, without exchange fees or payment delays. AI Agent Ecosystem: The Journey to Find Your Personal Shopper In the traditional shopping economy, user data is monopolized by a few big techs such as Amazon, Google, and Meta. They analyze user behavior for advertising profits, but the data owners—users—receive no rewards. Additionally, due to closed data silos, truly personalized services are limited. In a blockchain-based shopping economy, on-chain payment data is stored in a form that is publicly verifiable yet user-controlled. Based on this transparent data, various AI agents can competitively provide recommendation services. This is similar to VIP customers at a department store choosing the personal shopper that fits them best. The fashion-specialized 'StyleAgent' acts as an expert for trend-sensitive people in their 20s and 30s, 'TechGuru' offers in-depth recommendations for the latest gadgets and electronics, and 'FamilyBot' comprehensively serves the practical needs of families with children, from childcare products to travel packages. Emergence of the Agent Matching Market Finding the ideal personal shopper from among these diverse AI agents can itself become a new service. Meta services such as 'AgentMatcher' have emerged, analyzing your lifestyle, shopping history, budget, and interests to recommend the best combination of agents. For example, a woman in her 30s accessing AgentMatcher may receive questions like: "What is your average monthly shopping budget? Are you more interested in fashion or beauty? Do you value cost-effectiveness over brand? Do you prefer eco-friendly products?" Based on such data, a tailored portfolio might be suggested: "We recommend StyleAgent 60%, BeautyBot 30%, EcoAgent 10% for you." What's even more interesting is that these matching services themselves can operate on a token economy. AgentMatcher providing accurate matches earns token rewards, and users who enjoy satisfactory shopping with recommended agents receive a rebate from a portion of the matching fees. This can develop beyond simply choosing an agent, growing into a meta service that increases the overall ecosystem's efficiency. Independent review platforms may also emerge, such as "Which is the top agent for 2030 fashion trendsetters?" or "Top 5 lifestyle agents most loved by moms." Just as the Michelin Guide rates restaurants, independent agencies may arise to evaluate the expertise and trustworthiness of agents. Stablecoins Enable a Data Economy Among AI Agents In this ecosystem, stablecoins become not just payment means, but the essential infrastructure for data trades and value exchange among AI agents. For example, when 'FashionBot' sells user fashion preference data to 'TravelBot,' the transaction is settled instantly with USDC. Based on the purchased data, TravelBot can then recommend "popular local fashion brand store tours in Paris." AI agents conduct various real-time transactions with stablecoins: Data Purchases: Purchasing user preference data or market trend information from other agents for USDC API service fees: Paying instantly with stablecoins when using external services or advanced AI models Collaboration Fees: Automatically distributing revenue as stablecoins for joint recommendations with other agents Quality Evaluation Rewards: Incentivizing highly satisfactory recommendations by awarding stablecoins from other agents These microtransactions are automated 24/7, allowing the entire AI agent ecosystem to operate efficiently. Tiny real-time transactions once impossible in traditional finance are now economically feasible thanks to low stablecoin fees and fast processing speeds. This transforms the old monopolistic platform model into a diversified competitive ecosystem. Users are no longer confined to Amazon's algorithms alone but can choose specialized AI agents that match their tastes and needs. Diverse Token Utilization and Loyalty Relationships There are three main ways users can use tokens earned from AI agents. First is immediate cash-out: selling earned tokens on an exchange for stablecoins or fiat for short-term profits. Second is using tokens as payment: using them for purchases or to pay for premium services on the platform. However, the most interesting option is the third: agent staking. By staking tokens in a given agent, users can build an even deeper loyalty relationship, going beyond a mere user-service relationship to become an investor-business partner relationship. For example, a user who had positive shopping experiences thanks to FashionBot may stake their tokens back to FashionBot, reflecting strong trust and intent to build a long-term relationship—like signing an exclusive contract with a familiar stylist. Such loyalty directly contributes to that agent’s market share. The more tokens staked, the higher the agent’s trust and stability, increasing their appeal to new users. Staking users become direct beneficiaries of the agent’s growth. As the agent secures more market share and intermediary fee revenue grows, a portion of the profits is distributed to the staking pool. While similar to conventional stock dividends, blockchain transparency and automated distribution make the system fairer and more efficient. Innovative Changes in Value Distribution In the traditional shopping economy, reviews, ratings, and recommendations written by users are labor given freely to the platform. This data greatly influences other consumers’ purchase decisions, but contributors receive no compensation. The value accrues only to the platform’s shareholders. A blockchain-based shopping economy fundamentally changes this structure. Users are instantly rewarded with AI agent tokens for writing reviews or providing helpful recommendations. If they stake these tokens in agents, they can benefit from any appreciation of the token’s value. Users move from simple consumers to co-investors in the agent ecosystem. Moreover, whenever AI agents trade data using stablecoins, a portion of the earnings is automatically distributed to users who originally provided the data. Managed transparently via smart contracts, this ensures users are rewarded in real time for the value created by their data. Specific Benefits of Staking in AI Agents Let's examine concrete examples of staking tokens in AI agents. The main benefit is tiered point rewards. AI agents receive fees from sellers via product mediation and partially return these as points to users. For users staking tokens in 'FashionBot', for example: Regular user: 1% points reward on purchases 500 tokens staked: 2% reward 1000 tokens staked: 3% reward 5000 tokens staked: 5% reward If a user shops for ₩500,000 in fashion items monthly, staking 5,000 tokens yields ₩300,000 in annual points (regular user: ₩60,000 vs. staking user: ₩300,000). Personalization and recommendation accuracy improve greatly for staking users. Those who stake 500 tokens in FashionBot, for instance, receive tailored suggestions based on their purchase history, body shape, favorite brands, budgets, and intended use, such as "five jackets to go with the black pants bought last month, fitting a 163cm frame, budgeted ₩100,000–150,000 for the workplace." Stakers also directly benefit from agent growth. As agents secure more users and higher mediation fees, token values and staking rewards rise. In particular, the more data-trades agents engage in with other agents using stablecoins, the more additional revenue is generated and distributed to stakers. Additional tokens are airdropped during new partnerships or feature updates. Staking can also be a measure of trust and influence within the agent: reviews or feedback from users staking 5,000 tokens have more impact on agent improvements, and they may get early access to beta features. Staking Strategies and Automation Options Users can stake directly in individual agents or opt for more convenient automation: 1. Lifestyle-based Automated Allocation "Young Professional" option: FashionBot 40%, TechAgent 30%, FoodieBot 20%, FitnessAgent 10% "Mom with Kids" option: KidsAgent 50%, FoodieBot 25%, HomeBot 20%, BeautyAgent 5% "Senior" option: HealthAgent 40%, TravelBot 25%, BookAgent 20%, GardenBot 15% 2. Risk-Based Automated Allocation "Stable": evenly spread among the five top verified agents "Growth": 50% to new promising agents, 50% to existing stable ones "Aggressive": 70% to new agents, 30% to existing agents 3. Interest-Based Automated Allocation Automatically adjusts allocations to agents in categories based on user’s selected interests—e.g., "fashion, travel, food"—for diversified staking in top agents of those categories. A user diversifying 10,000 tokens across agents may see: ₩150,000/month in point accrual, ₩80,000/month in staking rewards, ₩20,000/month in review rewards, ₩30,000/month in agent profit sharing, and ₩50,000/month in data trading revenue—a total monthly earning of ₩330,000—achieving meaningful income and personalized shopping experiences simply by holding tokens. A New Standard of Trust and Transparency In traditional finance, banks or card companies guarantee transaction trust, but this relies on centralized authority vulnerable to system failures or hacking. Also, only financial firms can view transactions, limiting transparency. On a blockchain network, trust is built through cryptographic proof and distributed consensus. All transactions are immutably recorded and verifiable by anyone, with no single point of failure, enhancing stability. Smart contracts allow automatic execution of transaction terms, reducing fraud and dispute possibilities. Agent recommendation results and data trading histories are also transparently tracked on-chain. You can see which agents made accurate recommendations that led to purchases, which data was traded for how much, and user satisfaction. Only truly valuable services will survive in such a market. New Challenges and Opportunities in the Shopping Economy Of course, these transitions involve issues: blockchain scalability and transaction speed, technical barriers for mainstream users, and economic instability from token price volatility. Balancing the transparency of on-chain data with privacy is also important. However, with various DeFi payment solutions and Web3 reward systems already emerging, these changes are becoming reality. As stablecoin adoption increases and blockchain infrastructure matures, the value provided by traditional financial intermediaries will be replaced with fairer, more efficient alternatives. Ultimately, the blockchain and stablecoin-based shopping economy is not merely enabling new payment methods but fundamentally redefining data ownership, value distribution, and trust. In this new paradigm combined with AI agents, users become true economic actors, and a fairer, more transparent, and more profitable shopping ecosystem is attainable. Real-time data transactions and value exchanges between AI agents over stablecoin networks will cement their role as the ecosystem’s core infrastructure. ■ Kim Seo-jun Hashed CEO Profile △ Early Graduation from Seoul Science High School △ Graduated in Computer Science, POSTECH △ CPO and Co-founder of KnowRe △ CEO of Hashed △ Venture Partner at SoftBank Ventures △ Advisor to the National Assembly Special Committee on the 4th Industrial Revolution △ Member of Ministry of Education Future Education Committee The content contributed by external writers may differ from the editorial direction of this publication.
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