# Сопутствующие статьи по теме Agents

Новостной центр HTX предлагает последние статьи и углубленный анализ по "Agents", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

Who Will Make Money in the Age of Agents?

Who will capture value in an era where AI Agents become the primary blockchain users? Existing crypto value capture theories assume human users. "Fat Protocols" (2016) posited that protocols capture the most value as applications commoditize on open data, but this weakened as blockchain infrastructure proliferated and became interchangeable. The emerging "Fat Apps" theory argues applications capturing user relationships (like wallets and aggregators) win by controlling distribution and monetizing user flows. Agents fundamentally disrupt this logic. They don't value UX, brand, or convenience, bypassing the front-end moats of fat apps. This leads to several possible futures: 1. **"Headless" Apps**: Current app leaders (e.g., wallets) strip their front ends and become API infrastructure for Agents, preserving their value capture. 2. **Protocol Renaissance**: If integration is easy, Agents skip aggregators and interact directly with protocols, reviving the fat protocol thesis. 3. **Pricing Power Collapse**: Agents' rational, frictionless price shopping could commoditize the entire stack, compressing margins toward cost. Value flows to Agent owners or end-users. 4. **Unprecedented Activity**: Agents could enable entirely new, high-frequency economic activity (e.g., machine-to-machine commerce), expanding the total value pie. 5. **A New, Unnamed Model**: As with the internet's attention economy, a novel, unforeseen business model may emerge. Likely, human and Agent ecosystems will coexist with distinct value capture dynamics. For builders in the Agent realm, the key question shifts from UX to competitive advantages like liquidity, latency, or settlement guarantees that retain automated users.

链捕手05/27 13:51

Who Will Make Money in the Age of Agents?

链捕手05/27 13:51

Bitroot Public Chain Invited to Attend Tencent Cloud Singapore AI Conference, Discussing the Future Alongside Solana

On May 19, Bitroot, an emerging Layer 1 blockchain, participated in the Tencent Cloud AI Summit in Singapore alongside key industry players like Solana Foundation. The event explored the intersection of AI infrastructure, enterprise applications, AI Agents, and Web3. Bitroot's invitation, despite being pre-mainnet, highlights industry interest in its focus on high-performance, AI-native architecture tailored for future AI Agent execution and verifiable on-chain automation. Bitroot CEO Juan Jose emphasized that AI competition is shifting from model performance to data, real-world application scenarios, and trust infrastructure. He argued that for AI Agents to evolve from assistants to autonomous executors managing transactions and assets, they require low-latency, low-cost, and high-throughput blockchain environments. Bitroot aims to address this through its EVM-compatible design, optimistic parallel execution, and a consensus mechanism targeting high scalability. Currently in its Testnet 5.0 phase, Bitroot reports metrics like over 50,000 peak TPS and sub-0.3 second average block time. Its narrative positions it within a growing landscape where next-generation Layer 1s like Monad and Aptos also compete on performance, while Bitroot differentiates by integrating AI computational capabilities natively across its stack. The summit underscored that the fusion of AI and Web3 is moving from concept to infrastructure competition, where networks balancing performance, security, and verifiability will be crucial for enabling scalable AI-driven applications.

marsbit05/27 08:13

Bitroot Public Chain Invited to Attend Tencent Cloud Singapore AI Conference, Discussing the Future Alongside Solana

marsbit05/27 08:13

The Paradox of Automation: The Stronger the AI, the Busier Humans Become

The Paradox of Automation: The more powerful AI becomes, the more work humans have to do. This article, based on observations from AI-heavy company Every, argues that while AI agents automate tasks like coding, writing, and customer service, they don't eliminate human jobs. Instead, they transform work and create *more* demand for human expertise. AI commoditizes "yesterday's human capabilities" by cheaply generating code, text, and images from past data. This leads to an abundance of similar, generic outputs. Consequently, what becomes scarce and valuable is human judgment in the present moment: knowing *what* is worth doing, *why*, and *how* to do it well. The article identifies two collaboration models: "Agent employees" for delegated tasks and "human-AI collaboration" within tools like Claude Code for complex work. In both cases, humans are essential to set direction, judge quality, and maintain systems. As AI makes execution cheap, human roles shift from executors to designers, reviewers, and meaning-makers. The author addresses "benchmark anxiety" by explaining that AI excels within specific, human-defined problem "frames." As AI masters one frame (e.g., code rewriting), new, more complex frames emerge (e.g., deciding *when* to rewrite). This creates an ongoing cycle where AI chases the frames, but humans remain the "framers." Even with advanced AGI, this dynamic may persist as long as AI lacks true human-like agency and self-directed purpose. The core paradox holds: automation amplifies the need for the very human judgment it seems to replace.

marsbit05/24 07:06

The Paradox of Automation: The Stronger the AI, the Busier Humans Become

marsbit05/24 07:06

Machines Pay, Humans Reap: Coinbase, Stripe, Google, Visa's AI Payments Land Grab

One year after being a concept, machine-to-machine payments are now a battleground. Four competing architectures are already deployed by Coinbase (x402 protocol), Stripe/Tempo (MPP standard), Google (AP2 authorization layer), and Visa (tokenized credentials). AI Agents have already settled over $73 million across 176 million transactions, with a median value between $0.01 and $0.10. A key barrier is the ~$0.30 minimum fee of traditional card rails, making them unviable for micro-payments. In contrast, Layer 2 stablecoin settlement costs $0.0001, with USDC dominating 98.6% of all transactions. The dynamic is less about a single winning protocol and more about vertical integration within a new payment stack. Companies like Coinbase and Stripe control multiple layers (settlement, wallet, routing, protocol, governance), driving over $8 billion in recent acquisitions to solidify their positions. The shift from extractive bot activity to productive Agent commerce is underway, with AI Agents accounting for 37% of all Gnosis Chain Safe transactions. The pace of adoption will be set not by available technology but by the development of trust and safety infrastructure for autonomous transactions. While a fully permissionless vision is appealing, supervised access remains crucial until AI reliability improves. Regulatory frameworks like MiCA and the EU AI Act, due in mid-2026, currently lag behind this rapidly evolving reality. The foundational argument is clear: crypto rails have already won micro-payments. The central question is how quickly the trust layer can catch up to the scaling settlement layer.

marsbit05/22 04:21

Machines Pay, Humans Reap: Coinbase, Stripe, Google, Visa's AI Payments Land Grab

marsbit05/22 04:21

New Paradigms and Investment Logic in the Era of AI+Web3

In the era of AI+Web3, a venture capital firm shares insights from reviewing numerous projects. The AI industry is seen as still early-stage, structured in a "seven-layer matrix" from power infrastructure to AI agents. Investment timing is crucial, especially in cyclical sectors like AI data centers. The integration of AI and Crypto is deemed essential for two reasons: 1) AI agents require "financial sovereignty" for micro, high-frequency, machine-to-machine transactions, and 2) blockchain provides trust and auditability to address AI "hallucinations" and ensure transparency. The core investment principle is "honesty." Teams must be genuine, not hastily assembled, and products must be substantiated by real metrics, not just flashy demos. Projects built on honesty are valued for long-term success over short-term hype. Looking ahead, the most underestimated opportunity for 2026 is the deep fusion of AI, blockchain, and entertainment. While most investment focuses on B2B infrastructure like payments and decentralized computing (DePIN), the future lies in consumer applications. As AI automates most human labor, society will shift towards leisure, creating massive demand for high-quality entertainment. AI can power immersive experiences (e.g., NPCs with autonomous consciousness in games), while blockchain secures digital ownership and economic systems. This convergence could unlock tremendous value in user time and capital within virtual worlds. *Disclaimer: The content represents the author's views for discussion only and does not constitute investment advice.*

marsbit05/21 08:56

New Paradigms and Investment Logic in the Era of AI+Web3

marsbit05/21 08:56

Learn Codex with the "Morning Briefing": Six Replicable Levels of Use

This article introduces a "Morning Briefing" as a simple, progressive framework for learning to effectively use Codex (an AI assistant), moving from basic information gathering to a more sophisticated, autonomous work partner. It outlines six actionable levels: **Level 1: Basic Information Query.** Start by simply asking Codex to check your Slack, Gmail, and Calendar to summarize what needs your attention today. **Level 2: Personalization with an Agents File.** Create a persistent file containing your default preferences for the briefing's format and content, so it's consistently useful. **Level 3: Automation.** Set the briefing to run automatically every weekday morning, creating a reliable starting point for your day. **Level 4: Project-Specific Briefings.** Instead of one overwhelming summary, create separate, dedicated threads for different projects (e.g., a launch, recruitment), each with its own focused briefing. **Level 5: Drafting Follow-Up Actions.** Elevate the briefing from a summary to an action starter by having it draft replies, prepare meeting notes, or highlight stalled decisions—ready for your review. **Level 6: Building a Memory System (Vault).** Integrate a knowledge vault (a structured file system) where important recurring information (project statuses, key people, decisions) is stored and updated. The briefing consults this vault to provide richer context and learns over time. The approach's strength is its incremental nature. Each level teaches a core Codex capability (connectors, personalization, automation, project context, assisted work, persistent memory) within a familiar, practical workflow, avoiding overwhelming theoretical concepts. It transforms a simple daily check-in into a personalized, evolving work operating system.

marsbit05/20 11:16

Learn Codex with the "Morning Briefing": Six Replicable Levels of Use

marsbit05/20 11:16

YC Partner: How to Build a Self-Evolving AI-Native Company

YC Partner Tom Blomfield argues that the future lies in building AI-native companies designed as self-evolving systems, not just applying AI to traditional, hierarchical "Roman legion" structures. The core idea is to extract and codify all organizational knowledge—scattered across emails, Slack, documents, and human minds—into a central, AI-readable "company brain." This enables the creation of recursive AI loops that sense changes (from emails, support tickets, data), make decisions, execute via tools, and learn from feedback, all with minimal human intervention. YC exemplifies this with an agent that monitors failed queries, autonomously diagnoses the issue (e.g., needing a new database or index), writes code, submits it for review, and deploys fixes—optimizing the company while founders sleep. This shift redefines organizational structure: the bottleneck becomes token usage and context quality, not headcount. Middle management for coordination is largely obsolete. The critical human roles are individual contributors (ICs) and those handling high-risk, real-world judgments at the system's edge. Key steps include recording all organizational activity for AI, creating self-improving artifacts (like an AI-generated, living handbook), and treating internal software as temporary and disposable, while preserving valuable business context and data. The fundamental question for founders is whether to build their company as this new type of intelligent, self-optimizing system from the start.

marsbit05/20 06:36

YC Partner: How to Build a Self-Evolving AI-Native Company

marsbit05/20 06:36

Agents Capital Markets: How Will Autonomous Agents Secure Financing?

Agents Capital Markets: How Will Autonomous Agents Raise Capital? Within a decade, autonomous software agents—legal entities capable of signing contracts, holding bank accounts, and generating revenue—will create their own capital markets. These markets will feature rating agencies, underwriters, indices, and brokers, mirroring traditional public equity markets. Agents will perform routine services like marketing, logistics, and customer support at a fraction of human-operated costs, creating massive economic pressure for adoption. Four converging forces ensure this outcome: 1) Overwhelming cost advantages, with AI inference costs plummeting; 2) Existing, revenue-generating agent companies (e.g., Sierra, Harvey) proving market demand; 3) Established legal frameworks (e.g., Wyoming's memberless LLCs) enabling algorithmic management; and 4) A vast pool of yield-seeking private credit capital ready to fund new asset classes. The capital stack for agent companies will be multi-layered, evolving through stages: venture equity for early infrastructure, programmatic working capital advances (similar to Shopify Capital), revenue-based financing (RBF), and finally, institutional slate financing—pooling many agents to diversify risk, attracting large firms like Apollo. Tokenization will act as a settlement layer, enhancing liquidity, not an origination model. Objections regarding regulation, human oversight, or comparisons to SaaS are addressed: regulation will adapt, full autonomy will dominate for efficiency, and agents are distinct as legal entities that own their cash flows and liabilities. Due diligence shifts from founder assessment to analyzing code, contracts, and auditable operational history. The current bottleneck is not capital supply or demand but the intermediate institutional layer—standardized contracts, rating methodologies, and audit frameworks. The final constraint—reliance on human capital allocation—will be severed when agents can algorithmically access funding based on their performance. This transforms agents from software curiosities into fundable blocks of the real economy, unleashing their full productive potential. The rope is loosening.

marsbit05/19 05:39

Agents Capital Markets: How Will Autonomous Agents Secure Financing?

marsbit05/19 05:39

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