# Automation Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Automation", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

AI Workforce Ranking: Claude Fable 5's Automated Income Potential is 2.5 Times That of GPT-5.5

AI Labor Rankings: Claude Fable 5’s “Automated Earning” Capability Is 2.5 Times That of GPT-5.5 The latest Remote Labor Index (RLI) assessment shows that Fable 5 achieved an automation rate of 16.1%, nearly double that of Opus 4.8 (8.3%) and 2.5 times that of GPT-5.5 (6.3%). RLI evaluates AI's ability to complete real-world freelance projects from start to finish at a level acceptable to paying clients, using 240 verified Upwork tasks across 23 fields. Eight months ago, the highest RLI score was just 2.5%. The leap to 16.1% is driven by improved agent frameworks, including a "worker-critic loop" where a reviewer agent checks and sends work back for revisions. Fable 5 also had a higher per-task budget ($150 vs. $50 for others). However, absolute capability remains low—84% of tasks are still beyond current AI. AI also fails as an automated judge, significantly overestimating model performance. The "time horizon" hypothesis does not hold in RLI; task difficulty isn't directly tied to human completion time, showing a "jagged frontier" of AI capabilities. The key takeaway is the speed of progress: automation rates have more than quadrupled in under eight months, a trend crucial for businesses and policymakers relying on remote labor.

marsbit07/13 09:47

AI Workforce Ranking: Claude Fable 5's Automated Income Potential is 2.5 Times That of GPT-5.5

marsbit07/13 09:47

OpenAI is hiring investment banking experts, offering an annual salary of only 1.3 million yuan, with netizens deeming it too low

OpenAI is hiring an Investment Banking Subject Matter Expert in San Francisco to help develop and evaluate AI capabilities for finance. The role, part of the Applied AI team, involves defining quality standards, creating realistic evaluation tasks, and providing expert-level reference materials across the full deal process—from modeling to client presentations. The goal is to train models to produce outputs that are not just plausible but accurate and usable by professionals. The position requires at least two years of hands-on investment banking experience with a focus on execution, financial modeling, and materials production. The salary range is $185,000 to $205,000 plus equity. While some online commentators have called this low, OpenAI states the position is critical for building AI that can handle the high-stakes demands of the banking world.

marsbit07/09 12:00

OpenAI is hiring investment banking experts, offering an annual salary of only 1.3 million yuan, with netizens deeming it too low

marsbit07/09 12:00

Today, Claude Cowork Major Update: Close Your Laptop, It Works for You Overnight

Anthropic's Claude Cowork has received a major upgrade, officially launching on mobile and web platforms. This allows users to manage and monitor tasks from any device, freeing them from needing to stay at their computers. The key innovation is that tasks now run in the cloud on Anthropic's servers, meaning work continues even when a user's personal device is offline or closed. The update merges Chat and Cowork into a single interface and extends usage quotas. Engineers highlight three core capabilities now unified: precise context understanding, support for long-running tasks, and complete independence from a user's physical device. The workflow is described as a full cycle: a user assigns a complex task (e.g., preparing a meeting summary, drafting emails, post-meeting analysis). Claude Cowork autonomously breaks it down, connects to necessary tools like Slack and email, gathers information, and executes. It pauses only for critical user decisions, sending a notification to the user's phone for approval before proceeding. Product managers share use cases like monitoring AI agents during a soccer game or resuming a cloud-based task seamlessly after a flight, emphasizing the new flexibility. The article frames this as part of a larger trend where tech giants (OpenAI, Microsoft, Google) are competing to bring AI agents into the daily workflows of general knowledge workers, not just developers. The ultimate battleground is becoming an indispensable, seamless part of everyday productivity.

marsbit07/08 00:54

Today, Claude Cowork Major Update: Close Your Laptop, It Works for You Overnight

marsbit07/08 00:54

Claude Code's Shocking Origin Exposed: It Evolved from Safety Alignment, Boris: Only 1% Complete

**"Claude Code's Astonishing Origin Revealed: Born from Safety Alignment, with Only 1% Done"** This article traces the epic development of Claude Code, Anthropic's groundbreaking AI coding assistant. Its origins are surprisingly rooted in an internal safety alignment (Alignment) project. The journey began in 2021 with early prototypes like a VS Code extension, but the project was nearly forgotten due to immense infrastructure challenges in creating a true "agentic" coder. Key breakthroughs came from research teams focused on autonomous software engineering, developing core components like bash tools and code search. An internal CLI tool named "clide" emerged but was too超前 (ahead of its time), being clunky and slow. The project's fate changed in September 2024 when Boris Cherny joined. Tasked with "agentic coding," he built a simple CLI prototype. A pivotal moment occurred when he used `clide` to generate a complete pull request from an issue description, revealing the assembled potential of earlier research. A small team then executed a furious two-week sprint to build the core product. Launched in February 2025 as Claude Code, initial feedback was mixed. However, with the release of the Claude 3.5 Sonnet model, its capabilities skyrocketed, fundamentally altering software development workflows in Silicon Valley. Notably, Boris Cherny himself reached a point where 100% of his coding was done silently by Claude Code in the terminal. Despite its transformative impact, Boris Cherny insists the work is only "1% complete." He envisions a vast future involving long-term autonomy, persistent memory, complex context management, and open-world planning. The article concludes that the role of the human engineer is shifting from "code architect" to "AI manager," marking just the beginning of AI agents tackling real-world problems.

marsbit07/07 12:31

Claude Code's Shocking Origin Exposed: It Evolved from Safety Alignment, Boris: Only 1% Complete

marsbit07/07 12:31

Blockchain Capital Partner: AI is Rewriting the Fundamental Unit of Labor

AI is fundamentally rewriting the basic unit of labor, moving beyond fears of job displacement to a deeper structural shift. Historically, companies form when internal coordination costs are lower than market transaction costs. Past revolutions—industrial, internet, gig economy—each reduced these costs, shifting work outside traditional firms. Now, two forces converge: programmable labor (AI agents that scale via compute, not headcount) and programmable money (stablecoins enabling instant, micro-transactions without intermediaries). Together, they enable a radical unbundling of work into discrete tasks, priced and settled between machines at high speed, without the need for a corporate container. Early examples like Meta paying creators in USDC and AWS's AgentCore signal large tech firms preparing for this. Products like Poncho give AI agents wallets to pay for APIs or data per-use, enabling granular microtransactions previously impossible with credit cards. This doesn't eliminate companies or humans. Instead, the human role evolves into that of an orchestrator—defining objectives, quality standards, and intelligently re-bundling the outputs of AI agents into valuable wholes. The future firm may be less a container for labor and more an intelligent layer atop a global, programmable marketplace of tasks.

marsbit07/07 03:17

Blockchain Capital Partner: AI is Rewriting the Fundamental Unit of Labor

marsbit07/07 03:17

How Token Economy Reshapes the Business Rules of AI 'Measurement' | ToB Industry Observation

"Token Economy: How It Reshapes the Business Rules of AI's 'Metrics' | ToB Industry Observation" The article discusses how the token economy is fundamentally changing the business landscape for AI, moving from a phase of explosive technical supply to a focus on measurable value for enterprise demand. It highlights the astronomical growth in daily token usage in China, framing tokens as the new "measurement standard" or "electricity" of the intelligent era. A central challenge is determining a token's value, which varies drastically—up to 100,000x—across different applications, from drug discovery to casual chat. The concept of "high-quality tokens" that deliver real intelligence, versus "noise," is emphasized as crucial. Lenovo's Vice President shares three proposed "laws" of token economics: 1. **Law of Inertia:** The cost per token will continuously decline due to technological innovation, system optimization, and intelligent runtime scheduling. 2. **Law of Acceleration:** The value generated per token accelerates based on the depth of AI integration into business workflows, the level of engineering support, and organizational readiness. 3. **Law of the Singularity:** A tipping point where the value curve of AI application surpasses its cost curve, shifting from cost-saving to generating incremental, previously impossible value—enabling "innovation at scale." The article notes real-world struggles, such as companies exceeding AI budgets due to unpredictable token pricing and "token inflation" from agentic AI workflows. Solutions being explored include standardized metrics for token quality, transparent pricing, and new infrastructure like "Token Factories" for efficient, on-demand token production. The ultimate goal is for businesses to move past anxiety and reach the "singularity," where AI drives scalable innovation, akin to how electricity enabled countless modern appliances.

marsbit07/07 02:35

How Token Economy Reshapes the Business Rules of AI 'Measurement' | ToB Industry Observation

marsbit07/07 02:35

Why Did Codex and ChatGPT Merge? What's Next for Codex? OpenAI Core Leader Answers Everything

In 2026, OpenAI's Codex saw explosive growth, with weekly active users surging over 5x to 5 million since January, driven largely by the February launch of its desktop app. Codex desktop lead Andrew Ambrosino explains key shifts behind its evolution. A core change is the inversion of development costs: implementation is now cheap, while curation and taste—judging which of many AI-generated prototypes is valuable—have become the new scarcities. Ambrosino defines taste as a blend of aesthetics, systems thinking, direction, and semantic coherence in interaction. He notes AI still struggles with design because evaluating it requires human cultural context and abstract reasoning about how components relate—capabilities beyond current models. Timing is critical: the same Codex app would have failed months earlier; success hinges on the model's capabilities at launch. Roles are blurring within his team, with engineers, designers, and PMs overlapping significantly. However, Ambrosino cautions against eliminating specialized roles entirely, as each field retains deep expertise. On AI-assisted development, the focus has shifted from measuring code written by AI to distinguishing between supervised and unsupervised generation. A current challenge is teaching models to simplify code, not just add complexity. The merger of Codex and ChatGPT stems from observed user behavior: non-developers adopted Codex for general knowledge work despite its developer-centric interface. This revealed a collapsing boundary between specialized tools and universal assistants. The vision is a "home base" that orchestrates tasks across external professional tools (like Excel or Premiere Pro) via connectors, rather than rebuilding everything internally. An internal example showed Codex helping edit video by interacting with Premiere Pro's files and even writing a plugin for it. The future direction is a unified, extensible platform that serves as a central hub for automating and managing work across any specialized tool the user employs.

marsbit07/05 05:26

Why Did Codex and ChatGPT Merge? What's Next for Codex? OpenAI Core Leader Answers Everything

marsbit07/05 05:26

Google Shaken, Market Cap Evaporates Hundreds of Billions. Can Gemini Spark Save the Day?

Google is facing a turbulent period marked by a significant brain drain of top AI talent. Key figures like Noam Shazeer, John Jumper, Jonas Adler, and Alexander Pritzel have recently left for competitors OpenAI and Anthropic, causing investor concern and a sharp stock decline wiping hundreds of billions from Alphabet's market cap. Amidst this talent exodus and the delayed launch of the anticipated Gemini 3.5 Pro model, Google has unveiled its major new offering: Gemini Spark. This is not a standard chatbot but a persistent, cloud-based AI agent designed to automate multi-step workflows across Google's ecosystem (Gmail, Calendar, Docs, Drive, etc.) and some third-party apps. Powered by the Antigravity framework with Tasks, Skills, and Schedules, it aims to function as a continuous digital assistant. However, its high price point—exclusive to the $100/month AI Ultra tier—has drawn criticism. The article positions Spark as Google's critical, albeit late, move into the AI agent arena, where AI transitions from a tool to an autonomous workforce. While competitors and startups are already advancing in this space, Google's vast integration with Workspace gives it a potential edge, though its historical caution due to scale and risk may have cost it the lead. Ultimately, Spark represents a necessary shift for Google, but the question remains whether this "digital employee" can compensate for the loss of foundational talent and restore investor confidence in the company's future.

marsbit07/01 10:18

Google Shaken, Market Cap Evaporates Hundreds of Billions. Can Gemini Spark Save the Day?

marsbit07/01 10:18

Claude Gets a 'Staff Card' Now, From Anthropic

"Claude now has an 'official position' as Anthropic introduces 'Claude Tag', an AI team member with its own identity in Slack. Unlike traditional AI assistants that borrow user permissions, Claude Tag operates with its own dedicated accounts and audit trails across systems like Slack, GitHub, and data warehouses—a model Anthropic calls 'agent identity'. This allows the AI to act as a shared colleague within a channel, learning from team context and even proactively addressing issues in 'eavesdrop mode'. The key shift is that permissions are tied to the channel, not individual users, enabling anyone in a channel (like a PM without repo access) to leverage Claude's capabilities. Anthropic reports using this internally, with AI generating most of their product team's code. As AI agents proliferate in enterprises, this dedicated identity model addresses the scaling and security challenges of managing non-human 'employees'."

marsbit07/01 08:49

Claude Gets a 'Staff Card' Now, From Anthropic

marsbit07/01 08:49

Claude Code Announces Major Next-Gen Upgrade: You Chat, and the Backend Gets the Job Done

Claude Code's next major upgrade will make its sub-agents run in the background by default, transforming the tool from a conversational interface into a multi-threaded workflow engine. Users can chat with Claude while sub-agents handle tasks like code refactoring, running tests, and opening PRs in parallel. Creator Boris Cherny, who hasn't written a line of code in eight months and sometimes manages thousands of AI agents, states that users are no longer just prompters; "another Claude writes the prompts." This evolution builds on recent features like Routines for scheduled tasks and Dynamic Workflows for complex, multi-agent jobs. The change signifies a shift where engineers focus on strategy and decision-making rather than writing code. Anthropic's internal data suggests Claude Code triples engineering output, creating a bottleneck for product managers who can't generate enough work. Spotify's case study reveals that 73% of its pull requests are AI-assisted, with executives using the tool to build prototypes directly from natural language descriptions. The core takeaway: the scarcest resource is no longer the ability to write code, but the ability to decide what code to write.

marsbit06/30 09:33

Claude Code Announces Major Next-Gen Upgrade: You Chat, and the Backend Gets the Job Done