# Future of Work İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "Future of Work" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

The Permanent Underclass: No One Can Answer That 17-Year-Old Child

The article "The Permanent Underclass: No One Has an Answer for That 17-Year-Old" explores the concept of a "permanent underclass" emerging in a future where AI can perform most cognitive and physical labor. This theory, gaining traction in tech circles, suggests that as AI reduces the need for human workers, wages lose importance, and wealth increasingly flows to those who own AI models, compute, and data. The core issue is not temporary poverty but the potential breakdown of the traditional ladder of upward mobility—through labor, bargaining, and asset accumulation—making "underclass" a permanent status. The piece highlights a poignant question posed by researcher Jasmine Sun to AI lab personnel: What advice would you give a typical 17-year-old facing this future? Most had no answer, acknowledging the frightening transition ahead. However, while struggling to advise others, many in the AI field are securing their own positions, shifting from research or policy into labs to gain equity and stand on the "capital" side. This creates a vicious cycle: the more people believe labor's bargaining power is vanishing, the fewer work to rebuild it, accelerating its decline. Common personal survival strategies—learning AI skills, acquiring AI company equity, or pivoting to hands-on, in-person work—are analyzed. The author argues these are largely stopgaps, accessible mainly to the privileged, and may collectively undermine labor's overall position. The fundamental question shifts from "how not to fall behind" to "why must one's survival depend on being needed by capital?" The article concludes that the real solution is not individual adaptation but building a new societal "negotiating table"—new laws, forms of collective bargaining, or redistribution mechanisms (like taxes or public funds) to ensure the wealth AI creates is shared, especially with those displaced. It contrasts tentative institutional responses in China with a potential regulatory vacuum in the US, warning that without such frameworks being established before public frustration boils over, social unrest could follow. The 17-year-old’s dilemma underscores a systemic failure: the architects of this future are buying personal insurance but offering no collective answers.

marsbit17 saat önce

The Permanent Underclass: No One Can Answer That 17-Year-Old Child

marsbit17 saat önce

With One Sentence, It Built a 9-Person Travel Website; Altman: The Name ChatGPT Work Was Too Small

OpenAI CEO Sam Altman demonstrates the new capabilities of ChatGPT Work by using it to plan a long weekend trip for himself and eight friends. With a single spoken command on his phone, ChatGPT Work leveraged Altman's chat history to generate three travel ideas, built a full-stack website for the group to discuss and decide on options, and even drafted a Gmail message for him to send. Altman and other OpenAI executives, including Greg Brockman, suggest the "Work" name undersells its scope, positioning it more as a personal AGI assistant. The article details the underlying features enabling this, such as the Sites function for creating interactive web applications, Plugins for connecting to external services like Gmail and Google Drive, and Scheduled Tasks for automating recurring actions. It highlights a shift from ChatGPT merely providing answers to autonomously executing complex, multi-step projects across both work and personal life, with users primarily responsible for setting goals, reviewing outputs, and approving key actions. The integration across web, desktop, and mobile allows the smartphone to act as a "remote control" for these AI agents. However, the technology still requires human oversight for permissions, sensitive data, and final approvals.

marsbitDün 13:01

With One Sentence, It Built a 9-Person Travel Website; Altman: The Name ChatGPT Work Was Too Small

marsbitDün 13:01

ChatGPT Voice Enters the Desktop Arena: You Talk, a Team of AIs Get to Work

Recently, AI pioneer Andrej Karpathy shared his preferred method for interacting with AI: using voice input to convey complex, unstructured thoughts. Instead of carefully typing out requests, he simply speaks freely for minutes at a time. He finds that AI excels at untangling these "stream-of-consciousness" monologues, returning clearer, more organized outputs than the original spoken input. This approach, he argues, improves "mind-merging" with the model by providing high-bandwidth context. Following this trend, OpenAI has integrated advanced voice capabilities into the desktop version of ChatGPT for Work and Codex scenarios, available globally for macOS and Windows. Powered by the new GPT-Live model, the feature allows real-time, interruptible conversation—users can speak to start tasks, check progress, manage multiple AI agents simultaneously, and change directions mid-task. The system can leverage project context, connected documents, calendars, and communication tools to continue unfinished work. Industry leaders like Elon Musk and Sam Altman have also emphasized the shift toward voice, noting that typing is a bottleneck for conveying complex intent to increasingly capable AI agents. Speech offers a much higher throughput, allowing users to dump extensive context quickly, even if it's messy, letting the AI handle the structuring. Beyond mere input, OpenAI's desktop voice feature enables project management through speech. Users can prioritize tasks, coordinate multiple agents, and receive audio or visual notifications on task completion or blockers. This positions ChatGPT not just as a conversational tool but as an "AI work operating system"—a central hub where users act as managers orchestrating a team of AI assistants to handle tasks across files, codebases, and software on the desktop, the primary workspace for complex work. The core insight is that by removing the input bottleneck, voice interaction allows humans to focus mental energy on higher-level decision-making, while delegating execution to an ever-present team of AI agents.

marsbitDün 10:42

ChatGPT Voice Enters the Desktop Arena: You Talk, a Team of AIs Get to Work

marsbitDün 10:42

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbit07/22 07:57

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbit07/22 07:57

Understanding Circle Founder's "Agentic Economy" Paper to See How the Economic Landscape Will Be Restructured in the Next Decade

"Agentic Economy" by Circle founder Jeremy Allaire explores the convergence of AI and blockchain, arguing they form two sides of a single future economic system. AI agents will decompose corporate functions into automatable skills, while blockchain provides the trust, identity, and instant settlement layer for these agents to coordinate and transact. This "agentic economy" will be natively global, with on-chain companies managed by software. Key pillars include: fully-backed stablecoins for risk-free, high-speed settlement; new credit markets using on-chain data for machine underwriting; a shift from software subscriptions to pay-for-work consumption models; and professional agent marketplaces. The paper warns of major impacts: a potential decline in labor's share of income and risks of power concentrating at control points like identity layers or dominant currencies. The solution is not to defend old jobs but to design for broad ownership of capital (agents, models, infrastructure). Shared, on-chain ownership mechanisms can distribute prosperity, but this requires deliberate design and governance to prevent re-centralization. The outcome—either a highly concentrated or a more equitable economy—is a design and political choice, not a foregone conclusion.

marsbit07/15 02:13

Understanding Circle Founder's "Agentic Economy" Paper to See How the Economic Landscape Will Be Restructured in the Next Decade

marsbit07/15 02:13

Understanding Circle Founder's "Agentic Economy" Treatise: Deciphering How the Economic Landscape Will Be Reshaped in the Next Decade

This text summarizes the key points from Jeremy Allaire's treatise "The Agentic Economy." It argues that the convergence of AI agents (driving the cost of thought and work toward zero) and blockchain-based onchain economies (driving transaction costs toward zero) are not separate trends but two facets of a single, emerging economic system. The core premise is that AI agents will decompose traditional corporate functions into automatable skills, managed by an "orchestration layer." This demands a new economic infrastructure built on fully-backed, programmatic stablecoins for fast, final settlement and machine-speed payments. Trust is enabled through an "accountability chain" linking agents to verified real-world identities on public blockchains. The treatise explores how this native global system reshapes credit (through machine underwriting and agent working capital), software pricing (shifting from subscriptions to consumption/pay-per-work), and corporate structure (leading to hybrid "onchain companies"). It frankly addresses significant risks: a potential decline in labor's share of income and dangerous concentration of power at control points like identity layers and dominant currency issuers. The concluding "Civic Vision" argues the solution is not to defend old jobs but to deliberately design for broad ownership of productive capital (agents, models, infrastructure) to distribute prosperity. It acknowledges that achieving this equitable outcome is a political and design challenge, not a technological inevitability.

Odaily星球日报07/15 02:09

Understanding Circle Founder's "Agentic Economy" Treatise: Deciphering How the Economic Landscape Will Be Reshaped in the Next Decade

Odaily星球日报07/15 02:09

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

AGI Countdown: OpenAI's Chief Research Officer Makes Major Statement — The Window for Humanity is 'Very Small'

The countdown to AGI has begun, according to OpenAI's Chief Scientist Mark Chen, who states the window for human-centric progress is "very small." Chen argues that AI is reaching a point where models can perform "self-sustaining research," autonomously driving innovation in fields from mathematics to programming. He points to the proliferation of AI's "superhuman" insights—akin to AlphaGo's legendary "Move 37"—across disciplines as evidence of this shift. Chen firmly dismisses claims that scaling laws are plateauing or that pre-training is dead, asserting the field remains on an exponential curve. He cites OpenAI's successful bet on reasoning models like o1 as proof that fundamental breakthroughs are still possible. The future of research, he suggests, lies with "Vibe Researchers"—humans who provide high-level direction and "taste" while AI handles execution and orchestration of complex, long-horizon tasks. However, significant hurdles remain. Chen highlights a "benchmarking crisis," where models can overfit to existing tests without gaining true generalization. He also notes the "jagged frontier" of AI capabilities, where systems excel at advanced reasoning but struggle with contextual, continual learning from everyday experiences. Despite these challenges, he expresses confidence that these gaps will be closed. In a personal reflection, Chen shares that post-AGI, his wish is to open a noodle shop—a metaphor emphasizing that when AI masters knowledge and innovation, uniquely human experiences, warmth, and storytelling will become the ultimate form of value.

marsbit06/30 08:37

AGI Countdown: OpenAI's Chief Research Officer Makes Major Statement — The Window for Humanity is 'Very Small'

marsbit06/30 08:37

Father of Claude Code's Latest Assessment: Team Division of Labor Rewritten in the AI Era, These 'Five Types' Are Most in Demand

"In the era of AI reshaping software development, Anthropic's Claude Code team lead, Boris Cherny, proposes a future where traditional job titles dissolve. He identifies five fluid, behavior-based roles emerging in effective, AI-augmented teams: The Prototyper (generates disruptive ideas), The Builder (scales prototypes to production), The Sweeper (streamlines and refactors to combat bloat), The Growth (iterates on launched products for market fit), and The Maintainer (ensures long-term security and reliability). Crucially, these are not fixed positions. Individuals may span multiple roles depending on the project and its lifecycle stage. A designer might be a Prototyper and Sweeper; an engineer could be a Builder and Maintainer. Team composition should shift with product maturity: early-stage products need Prototypers, Builders, and Sweepers, while scaling products require more Builders, Growth roles, and Maintainers. The discussion highlights that role fluidity is key, as professionals often switch roles across different projects or as a single project evolves. While AI tools like Claude increasingly assist with tasks like building and sweeping, human adaptability and focus on goals over rigid job boundaries are seen as essential for future teams."

marsbit06/30 05:53

Father of Claude Code's Latest Assessment: Team Division of Labor Rewritten in the AI Era, These 'Five Types' Are Most in Demand

marsbit06/30 05:53

AI as the Boss: Nearly Bankrupts 10 Companies...

A recent study from Princeton University tested 14 AI models, including large language models (LLMs) and a rule-based algorithm, in a simulation where they acted as CEOs of a virtual SaaS startup over 500 days. The goal was to grow an initial $1 million capital. The results were stark: only four "CEOs" ended with a profit. The top performer was Claude Fable 5, multiplying the capital 47-fold to $47.15 million. Claude Opus 4.8 and GPT-5.5 followed. Notably, the fourth profitable entity was a simple, pre-programmed rule-based algorithm, which outperformed many advanced LLMs with $15.76 million in profit. Five other models, including several major LLMs, went bankrupt before the simulation ended. Key takeaways from the research highlight that successful AI CEOs demonstrated a tendency for exploration and adaptation over caution. They excelled in discovering hidden information, predicting future cash flow, adapting quickly to changes (like competitor moves), and engaging in strategic "if-then" planning. The study also found that equipping LLMs with programming-agent frameworks, optimized for coding tasks, actually harmed their performance in this CEO role, suggesting a need for domain-specific adaptations. The article concludes by contrasting AI's current operational proficiency within defined frameworks with the type of visionary, intuitive decision-making—exemplified by figures like Steve Jobs—that truly drives transformative business strategy. This critical "matrix-drawing" capability, it argues, remains uniquely human.

marsbit06/29 09:08

AI as the Boss: Nearly Bankrupts 10 Companies...

marsbit06/29 09:08

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