# Сопутствующие статьи по теме Automation

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

Will Middle Management Be Replaced by AI? What Will the Future Company Structure Look Like

The article explores whether AI will eliminate middle management and reshape future corporate structures. It traces the historical evolution of organizations—from Roman military units to modern corporations—showing how hierarchical systems emerged to manage information flow under the constraint of limited "span of control." Middle management, matrix structures, and bureaucratic systems were all solutions to coordination challenges in information-scarce environments. AI, however, challenges this foundational premise. By enabling real-time modeling, understanding, and distribution of information, AI could replace human-centric coordination mechanisms. Examples like the AI firm "Moon Dark Side" illustrate radical experiments: no departments, titles, or traditional KPIs, with co-founders directly managing large teams and AI agents handling tasks from data processing to code generation. Block (founded by Jack Dorsey) is presented as a case study in building an "intelligent company." This model relies on two core components: a "company world model" (a real-time understanding of internal operations via digital traces) and a "customer world model" (built from real behavioral data, especially financial transactions). An intelligence layer uses these models to dynamically combine capabilities (e.g., payments, lending) to serve customers proactively, without pre-defined product roadmaps. In this structure, traditional roles shift. Middle managers are replaced by a system that handles coordination, while humans focus on individual contributions (ICs), direct responsibility (DRIs), or player-coach roles. The organization becomes flatter, faster, and more adaptive. The article concludes that AI is not just a tool for efficiency but a transformative force that could redefine organizational design, moving companies from human-led hierarchies to system-driven intelligence.

marsbit04/01 08:11

Will Middle Management Be Replaced by AI? What Will the Future Company Structure Look Like

marsbit04/01 08:11

AI Agents Are About to Take Market Share from Visa

Artificial intelligence agents are poised to disrupt Visa's business model by bypassing the traditional credit card interchange fee structure. Unlike humans, AI agents are purely rational: they don't accumulate rewards, seek fraud protection, or desire premium cards. Their sole objective is to complete transactions at the lowest cost, fastest speed, and with minimal fees. This shift threatens the 2-3% interchange fees that underpin Visa’s $500 billion valuation, as these fees essentially tax human irrationality—something agents lack. Recent developments, such as the launch of Tempo (a high-volume stablecoin settlement blockchain), the Machine Payment Protocol (enabling autonomous micro-payments), and Visa’s own command-line payment tool for AI, indicate a rapid move toward agent-driven commerce. While current transaction volumes remain small, infrastructure is being built to support machine-to-machine payments that avoid card networks. Major players like Stripe, Mastercard, and Circle are investing heavily in this space. Visa network’s distribution advantage relies on human behavior—consumer trust and merchant acceptance—a cycle that doesn’t apply to agents. They optimize for efficiency, not brand loyalty. Although widespread consumer adoption is still emerging, the infrastructure for agent-commerce is advancing quickly, starting with micro-payments for AI services. The fundamental challenge is that interchange fees are a tax on human psychology, and agents are purely rational actors.

marsbit03/31 11:14

AI Agents Are About to Take Market Share from Visa

marsbit03/31 11:14

2026年AI热潮下，普通人如何赢？

In the 2026 AI boom, winning means using AI to measurably improve your life—through increased income, time, flexibility, skills, and security. The author outlines three paths to avoid being trapped by AI and instead leverage it to build a better future. 1. **Career Architect**: Optimize your current role by using AI to enhance productivity and value within your organization. This involves building domain-specific AI workflows, continuously learning AI tools, and gaining basic technical skills. It’s the lowest-risk, highest-probability path for most professionals. 2. **Income Architect**: Create independent income streams outside traditional employment. This path suits those seeking financial autonomy without full-scale entrepreneurship. Examples include productized AI solutions (e.g., automated content or design services) and content/audience monetization (e.g., building a niche following on social platforms for paid opportunities). 3. **Venture Architect**: Build scalable AI-native businesses, such as tools, agencies, or consulting firms. This high-risk, high-reward path requires experience, resources, and resilience. It often evolves from success in the Income or Career paths. The key takeaway: Speed and execution matter most. Instead of waiting for perfect information, start acting—experiment, iterate, and adapt. The biggest risk in the AI era is inaction.

marsbit03/31 08:46

marsbit03/31 08:46

60 Curated Skills, Workflows & Open-Source Projects: The Ultimate Claude Advanced List

This comprehensive guide curates 60 essential AI tools, frameworks, and resources to help developers, creators, and builders move from using AI to constructing robust AI systems. The list is organized into nine key categories: AI Programming Agents & IDEs (e.g., Claude Code, Cursor), Agent Frameworks (e.g., OpenClaw, LangGraph), MCP Services & Tool Integrations (e.g., Tavily, Context7), Claude Skills (e.g., PDF Processing, Frontend Design), Local AI & Model Runners (e.g., Ollama, Open WebUI), Workflow & Automation (e.g., n8n, Langflow), Search, Data & RAG (e.g., GPT Researcher, Chroma), API & Infrastructure (e.g., FastAPI, Portkey Gateway), and Learning Resources (e.g., Awesome Agents, PromptingGuide). The author emphasizes a structured approach, recommending specific starting toolkits based on the user's role—developer, creator, product builder, or learner—to avoid overload and build practical, production-ready workflows.

marsbit03/30 11:10

60 Curated Skills, Workflows & Open-Source Projects: The Ultimate Claude Advanced List

marsbit03/30 11:10

Dialogue with MIT Economist: Don't Panic About 'AI Doomsday Theory', Verification Capability is a Scarce Resource

In a discussion with MIT economist Christian Catalini, the core argument is that the true scarcity in the AI economy is not intelligence but verification—the human capacity to check, judge, and confirm the correctness of AI outputs. Catalini explains that while automation costs are falling exponentially, verification remains constrained by human biological limits, at least for now. Entry-level jobs are most vulnerable, as AI can easily replicate tasks that rely on measurable, existing knowledge. However, even top experts are inadvertently training their own replacements by generating data that AI learns from—a phenomenon termed the "coder’s curse." Three roles will remain critical in the AI-driven economy: - **Directors**: Those who set intentions and steer AI agents toward goals, dealing with "unknown unknowns." - **Meaning Makers**: Individuals who create cultural, social, or narrative value based on human consensus and status games. - **Liability Underwriters**: Top-tier experts (e.g., lawyers, doctors) who assume responsibility for edge cases and final validation. Catalini advises against panic and encourages experimentation with AI tools to automate current roles and discover new opportunities. He emphasizes that uniquely human traits—like judgment in unmeasurable contexts—will retain value, and crypto-based verification infrastructure may play a key role in ensuring authenticity. The transition will be disruptive, but leveraging AI can amplify human potential exponentially.

marsbit03/28 08:06

Dialogue with MIT Economist: Don't Panic About 'AI Doomsday Theory', Verification Capability is a Scarce Resource

marsbit03/28 08:06

Technological Fantasy or "Career Killer"? Deep Reflection Under the Wave of AI Layoffs

Amid the rapid adoption of AI tools like OpenClaw, a wave of layoffs is sweeping across industries from North America to Asia. Major companies, including Meta and Crypto.com, are cutting jobs—up to 20% or more in some cases—not due to economic pressure but as part of a strategic shift toward AI-driven productivity. In Q1 2026 alone, over 20% of tech layoffs were attributed to AI and automation. Roles most affected include programmers, customer service reps, data entry clerks, and marketing specialists—jobs with high language or data-processing components. Even creative and industrial roles are at risk if they involve repetitive, template-based tasks. Industries like finance, media, and logistics are also experiencing disruption as AI automates standardized workflows. The report from Anthropic highlights that positions requiring low emotional intelligence, physical interaction, or complex decision-making remain safer. To stay relevant, workers must focus on skills AI can’t replicate: empathy, creative problem-solving, and strategic judgment. The key is not to resist AI but to adapt, using it as a tool while deepening human-centric capabilities. The era of AI collaboration has begun—those who evolve will thrive; those who don’t may face obsolescence.

marsbit03/28 00:14

Technological Fantasy or "Career Killer"? Deep Reflection Under the Wave of AI Layoffs

marsbit03/28 00:14

HTX Research Latest Report Deciphers OpenClaw: The Battle for Execution Entry and Huobi HTX's AI Strategic Path

HTX Research, the analytical arm of Huobi HTX, has released a report titled "From the Rise of OpenClaw: How AI Begins to Compete for the True Work Interface." The report analyzes the emerging trend of AI evolving from a conversational tool into an execution layer, using the rapid growth of the open-source project OpenClaw as a key example. OpenClaw is a personal AI assistant that operates on a user's local device. It receives tasks through messaging platforms like WhatsApp, Telegram, Slack, and others, and can execute actions by integrating with files, browsers, calendars, email, and terminals. This signifies a major shift: AI is moving beyond answering questions to actively performing tasks, competing for the "execution interface" of the digital age. The report identifies five converging trends enabling this shift: sufficient model capability for multi-step tasks, the high frequency of messaging apps as a natural interface, open-source distribution, self-hosted models addressing data privacy, and a strong market need for small teams to achieve more with fewer resources. It highlights a particular fit in the Chinese market, where many small and medium teams operate on message-driven platforms like WeCom and Feishu. Some Chinese cities have already begun offering support policies to foster an OpenClaw ecosystem. However, the report also outlines three major hurdles for such tools to become reliable infrastructure: security risks (noting recent malware incidents), the need for robust governance and auditing, and the necessity for industry-specific templates to move beyond early adopters. Complementing this analysis, the report details Huobi HTX's own AI strategy. Rather than building an execution layer, HTX is focusing on becoming a "platform service entrance and ecosystem connector." Its proprietary AINFT product aggregates major AI models (OpenAI, Anthropic, Google) into a single access point for users, with crypto-native features like TronLink wallet sign-ins and a pay-as-you-go model instead of subscriptions. HTX's competitive strategy is differentiated by its focus on integrating AI directly into its trading platform. Its "HTX AI Skills" currently cover spot and futures trading execution, with plans to expand into market analysis, intelligence, and a built-in assistant, aiming to create a closed loop for user experience. In conclusion, while the move of AI into the execution layer is still in its early stages with significant challenges ahead, the direction is clear. The next phase of AI competition will extend beyond model performance to encompass control of interfaces, permission governance, and skill ecosystems. Huobi HTX's early布局 in this area presents a notable case study for how crypto platforms can integrate AI as a core, operational asset.

marsbit03/24 06:21

HTX Research Latest Report Deciphers OpenClaw: The Battle for Execution Entry and Huobi HTX's AI Strategic Path

marsbit03/24 06:21

OpenAI Exposes 'Polaris' Project, the '2028 Great Unemployment' May Really Be Coming

OpenAI has disclosed its "North Star" project, aiming to build a fully automated multi-agent AI research system by 2028. This goal, announced by Chief Scientist Jakub Pachocki, includes launching an "autonomous AI research intern" capable of handling specific research tasks by September 2024. The project represents a major strategic shift, consolidating resources and products like ChatGPT and Codex into a unified super-app. The initiative reflects a broader industry trend, with competitors like Anthropic also advancing AI agent integration into workflows, such as through Claude Code on platforms like Discord. However, Pachocki acknowledges significant challenges in safety and controllability, noting that full control over LLMs remains unresolved. Economically, the project could be highly lucrative, with projections suggesting AI agents—including a $20,000/month "research agent"—generating $29 billion annually by 2029. The core implication is profound: if AI can autonomously conduct research, the pace of scientific and AI advancement could accelerate beyond human limitations, potentially reshaping labor markets and research paradigms.

marsbit03/24 02:46

OpenAI Exposes 'Polaris' Project, the '2028 Great Unemployment' May Really Be Coming

marsbit03/24 02:46

Karpathy Diagnosed with "AI Psychosis"! Not Eating or Sleeping, 16 Hours a Day Raising Lobsters

Andrej Karpathy recently revealed that he has developed what he calls "AI psychosis," an obsessive state where he spends up to 16 hours a day directing AI agents instead of writing code himself. In a podcast with Sarah Guo, he explained that his workflow has shifted from 80% hand-coding and 20% AI-assisted to the reverse, or even more extreme. He now manages multiple AI agents simultaneously, treating them as a team to execute tasks. Karpathy admitted that he’s become addicted to optimizing AI performance, constantly worrying about whether he’s using tokens efficiently or pushing the system to its limit. He highlighted the importance of an agent’s “personality,” noting that Claude Code feels more like a collaborative teammate compared to colder, more mechanical alternatives. He also shared practical applications, such as "Dobby," a Claude-based smart home agent that integrates and controls all his home devices through natural language, replacing six separate apps. In research, his "AutoResearch" project used AI to run 700 experiments, resulting in an 11% training speed improvement for an AI model—discovering optimizations he had missed as a human researcher. Despite the capabilities, Karpathy noted that AI agents still exhibit uneven performance—sometimes brilliant, other times childlike—due to limitations in reinforcement training. He predicts that 2026 will see a "slopacolypse," with AI generating vast amounts of mediocre content. His experience signals a broader shift: humans are becoming directors of AI systems rather than executors, navigating a new era of human-AI collaboration.

marsbit03/23 11:44

Karpathy Diagnosed with "AI Psychosis"! Not Eating or Sleeping, 16 Hours a Day Raising Lobsters