# Пов'язані статті щодо Commerce

Центр новин HTX надає останні статті та поглиблений аналіз на тему "Commerce", що охоплює ринкові тренди, оновлення проєктів, технологічні розробки та регуляторну політику в криптоіндустрії.

Xiaohongshu's Second Great Voyage, This Time Sailing Towards AI

Xiaohongshu's Second Voyage: Navigating Towards AI Since ChatGPT's emergence, Xiaohongshu's founder Mao Wenchao has been acutely aware of AI's potential threat, recognizing that the life advice people seek from chatbots overlaps directly with his platform's core business. Founded in 2013 as a PDF shopping guide for Chinese tourists, Xiaohongshu evolved into a massive community where millions share authentic, personal experiences—from product reviews to travel tips. This vast repository of "I've tried this" human judgment became its most valuable asset. However, the rise of AI, which delivers instant answers, challenges the very need for users to sift through numerous personal notes. Fearing its treasure trove of lived experience could become mere training data for others, Xiaohongshu is proactively adapting. In 2026, it established a dedicated AI division (Dots), launched RED Skill to turn user experiences into usable AI tools, and acquired the AI search product "Diandian." Its investments now extend to AI firms like MiniMax and hardware startups, moving upstream to address needs before they even become search queries. The platform's commercialization strategy is also evolving. With a newly acquired payment license and tools like the AIPS model to track consumer decision journeys, Xiaohongshu aims to seamlessly integrate recommendations with transactions, embedding commerce within AI-generated answers. Yet, a critical tension remains. While building smarter machines to organize and leverage its human experiences, Xiaohongshu must prevent AI from drowning out the authentic, flawed, and trustworthy "I've tried this" voices that built its community. Its core challenge is to harness AI's power without letting the map—the machine's perfect, synthesized answer—replace the territory of genuine human experience. This balance between technological advancement and preserving human trust defines its current journey and its future.

marsbit06/16 01:14

Xiaohongshu's Second Great Voyage, This Time Sailing Towards AI

marsbit06/16 01:14

The Battle for the AI Payment Race: Traditional Card Networks Face Off Against Coinbase

With the rise of AI agents conducting transactions, a battle for the underlying payment infrastructure is underway. Two distinct and incompatible approaches have emerged for enabling autonomous AI payments. The first approach is championed by traditional card networks Visa and Mastercard. They leverage their existing tokenized card credential systems, extending them to allow verified AI agents to make purchases within user-defined limits. Services like Mastercard's Agent Pay and Visa's Intelligent Commerce integrate with major AI platforms (e.g., OpenAI, Anthropic) and keep transactions within the established, decades-old card payment model. This system offers advantages for consumer retail, including robust fraud protection, chargeback mechanisms, and extensive merchant networks. The second approach, led by Coinbase, utilizes stablecoins on open internet protocols. Its x402 protocol reactivates the HTTP 402 status code for machine-to-machine micropayments, using USDC for settlement directly on-chain. This method eliminates the need for accounts or card fees, making it highly efficient for high-frequency, low-value, cross-border transactions between AI agents—such as paying for API calls, data streams, or computational resources—where traditional card fees and settlement times are impractical. While card networks excel in consumer-facing scenarios requiring dispute resolution, stablecoin protocols are tailored for machine economies. A key challenge for both is agent identity verification and transaction authorization. Notably, Visa and Mastercard are hedging their bets by also investing in stablecoins. Visa has rapidly grown its stablecoin settlement volume and is collaborating with Coinbase to bridge its network with the x402 protocol. Mastercard plans to acquire stablecoin platform BVNK. Their strategy is to become the fee-collecting gateway for all payment flows, regardless of the channel. Current applications reflect this division: consumer AI shopping tools (e.g., ChatGPT's checkout, Amazon's "Shop for Me") predominantly use card networks, while machine-focused services (e.g., Amazon Bedrock's core payments) adopt stablecoins via the x402 protocol. In the short term, a coexistence model is expected, with cards dominating retail and stablecoins powering machine transactions. The long-term outcome depends on whether AI-driven commerce evolves to resemble traditional retail or becomes a vast network of machine micropayments. By investing in both tracks, the incumbent card networks are positioning themselves to capture transaction fees regardless of which future prevails.

marsbit06/08 09:57

The Battle for the AI Payment Race: Traditional Card Networks Face Off Against Coinbase

marsbit06/08 09:57

It Took Me a Year to See the Bitter Truth About Agent Payments

After a year building infrastructure for the Agent economy, engaging with major players like Stripe, Visa, and Coinbase, the author shares a sobering analysis of the current state of Agent payments. The core finding is a stark lack of genuine, immediate demand across most envisioned use cases. The article breaks down four key market segments: 1. **Agent-to-Merchant (Consumer Shopping):** For most product categories (e.g., clothing, electronics), conversational AI shopping is a step backwards from visual e-commerce interfaces. While agents excel at understanding needs, they can't replace side-by-side product comparison. Real merchant interest is defensive "Agent Engine Optimization," not driven by current customer demand. Potential exists for high-frequency, low-decision purchases (like food delivery) or navigating complex store UIs, but these require massive B2C distribution channels dominated by giants like Amazon. 2. **Agent-to-API (Developer Services):** Developers already have subscriptions and billing relationships for APIs (compute, data). Prepaid balances solve micro-payment issues for low transaction volumes. A deeper structural problem is that major SaaS vendors' business models rely on enterprise contracts, resisting granular pay-per-call pricing. While protocols like MPP and x402 serve the long tail of niche services, this market is small and developers are historically low-willingness-to-pay. 3. **Agent-to-Agent:** This remains largely theoretical with minimal transaction volume. While it represents a long-term bet on a fundamentally new transaction infrastructure (sub-second, micro-penny to million-dollar, multi-party settlements), it does not constitute a present market. 4. **Agent-to-Finance:** This is the only category with existing, paying demand. Integrating AI into financial workflows (trading, portfolio management) is a natural evolution and enables new capabilities like autonomous rebalancing. However, competition favors established, regulated institutions. The "real problem" is not moving money between agents, but the broader challenge of **coordination**—orchestrating work between agents and humans, verifying outcomes, and settling results. Payment is just one component of settlement, which is itself part of coordination. Companies that solve the coordination layer will subsume payment, not the other way around. While well-funded incumbents build defensively for a long-term future, startups must find where the market is today—which, for the author's team, lies outside these four categories in an area of real, growing, and underserved activity.

marsbit06/06 10:19

It Took Me a Year to See the Bitter Truth About Agent Payments

marsbit06/06 10:19

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