【局限】作者指出AI使用结果与其初始目的之间存在错位，这对智力工作构成了普遍威胁。这一局限不仅限于数学领域，而是扩展到所有创造性工作，反映了当前AI技术应用的系统性问题。

【局限】作者坦诚模型的不完善性，承认需要持续改进，并感谢经济学家反馈，这种开放态度体现了学术严谨性，但也反映了当前模型在预测AI经济影响方面的局限性。

【数据】极端情景下的具体数据预测：AI将自主完成绝大多数知识工作，几乎不为人创造新的知识任务，这一预测反映了模型对AI能力发展的激进假设，值得验证。

Note the ecosystem thinking and the centering of credentials.

• Current Role & Career Path [00:00:00]:
  • Damon is a 31-year-old chip engineer based in Shenzhen, China [00:00:00].
  • With a master's degree in physics, he entered the semiconductor industry during a major hiring boom [00:01:51].
  • Began his career in Hunan earning 7,000–8,000 RMB/month with grueling overtime before relocating to Shenzhen for higher pay (>10,000 RMB/month) and to close the distance with his girlfriend [00:02:14].
• The Reality of the Job (24/7 On-Call) [00:00:22]:
  • Although his contract specifies 9-to-6 with weekends off, continuous production lines require him to be on call at all hours [00:04:23].
  • Urgent night calls do not count as paid overtime, resulting in persistent sleep disruption, elevated stress, and declining health [00:04:52].
• Career Dilemma & Age Discrimination [00:00:37]:
  • Leaving feels financially and professionally risky with China's tech sector facing widespread age discrimination against workers over 35 [00:05:53].
  • His partner's tech position is less secure, making his steady salary an essential financial safety net for both of them [00:07:37].
• Life Pressures & Exploring Next Steps [00:07:28]:
  • Despite a 17-year relationship, marriage and children remain on hold due to the immense costs of housing, family obligations, and his lack of personal time [00:08:20].
  • Spends weekends studying at the public library, creates videos as an experiment to explore other career paths, and runs to decompress from workplace pressure [00:06:15].

Badanie na ponad 7700 pracownikach obaliło tezy, że praca zdalna pogarsza dobrostan psychiczny i współpracę w firmie.

Najwyższy poziom dobrostanu w 5-stopniowej skali deklarowali pracownicy zdalni – średnio 4,22. Wśród pracujących hybrydowo było to 4,12, a stacjonarnie 3,89. Praca zdalna nie wiązała się też z gorszymi relacjami czy współpracą w zespole. Wyniki opublikowano w „Frontiers in Psychology”.

It seems remarkably optimistic to me to assume that the workforce will care, in many cases, where the work came from. I'd expect that interest to be ranked well below profitability, commercial appeal, and liability.

(Not the point of the article, really, but I think an important part of the discussion about helping students be workforce-ready.)

Success is a lagging indicator from hard work done earlier.<br /> —Jimmy Carr "Laughs Funny" Tour 2026 #

• Study Scope & Background:

  • Researchers from the University of Colorado Boulder (led by Prof. Stefanie Johnson) analyzed workplace survey data from 7,704 employees at a large healthcare organization and evaluated retention records one year later.
  • The study evaluated well-being, feelings of connection, and turnover across fully remote, hybrid, and fully onsite workers.

• Core Findings:

  • Well-Being Hierarchy: Fully remote workers reported the highest levels of well-being, followed by hybrid employees, with fully onsite workers reporting the lowest.
  • Workplace Connection: Remote employees were slightly more likely than hybrid or onsite peers to describe company culture using words associated with teamwork, inclusion, and mutual support, debunking assumptions of chronic isolation.
  • Retention & Turnover: Higher well-being was a strong predictor of lower employee turnover; remote work indirectly boosted retention by improving overall well-being.

• Key Drivers:

  • Autonomy & Control: Having control over one's physical environment and daily schedule significantly correlates with positive psychological outcomes.
  • Stress Reduction: Working remotely eliminates commuting friction, traffic fatigue, and everyday logistical burdens like pet care and child care management.
  • Policy Implications: Researchers argue that Return-to-Office (RTO) mandates are often driven by executive habits rather than empirical data, suggesting organizations focus on flexibility and well-being instead.

Hacker News Discussion

• Bimodal Outcomes & Individual Differences:

  • Commenters noted that remote work well-being is often bimodal: employees who build self-discipline and healthy work-life boundaries thrive, whereas others struggle with isolation, blurred boundaries, and burnout.
  • Evaluating suitability for fully remote work during hiring remains challenging.

• Management & Communication Pitfalls:

  • Remote work depends heavily on clear goals, autonomy, and organizational health; poor leadership, surveillance software, or unannounced layoffs quickly foster paranoia across Slack and Zoom.
  • Subtle in-person cues and hallway alignment can be lost without intentional, low-friction communication channels.

• Flaws in Return-to-Office (RTO) Execution:

  • Multiple participants criticized hybrid setups where employees commute to an office merely to sit on video calls with globally distributed teammates or navigate noisy "hot-desking" environments.
  • Many view strict RTO policies as disguised headcount reduction efforts rather than genuine attempts to boost collaboration.

• Holistic Life Satisfaction vs. Pure Work Focus:

  • Several commenters emphasized that even if remote work occasionally alters raw office engagement, the overall gains—eliminating draining commutes, managing household tasks, and being present for family—result in vastly superior life satisfaction.

Validate assumptions about the true issues! The solutions are different depending on the problem.

• Core Conflict: Explores the tension between professional drive/startup ambitions and the responsibilities and joys of fatherhood.
• Rejecting the False Dichotomy: Challenges the conventional narrative that high achievement requires sacrificing family life and being an absent parent.
• Redefining Ambition: Argues for expanding the concept of ambition to include excelling as a father alongside building meaningful work and products.
• Focus and Boundaries: Emphasizes that parenthood enforces time constraints that ultimately increase focus, eliminate low-value tasks, and demand higher efficiency during working hours.

Hacker News Discussion

• External Validation vs. Meaning: A leading perspective argues that traditional ambition often devolves into seeking external approval and corporate ladder climbing, suggesting instead that aligning life around health, process, relationships, and children yields deeper fulfillment.
• Intrinsic vs. Extrinsic Ambition: Commenters distinguish between status-driven ambition and healthy ambition focused on creating positive impact, mastering a craft, or achieving challenging intrinsic goals.
• Financial Security & Reality: Several participants note that professional ambition remains essential for providing financial stability, better opportunities, and resources for their families.
• The Parenting Reality Shift: Experienced parents emphasize that having children reframes life away from self-centered goals; while demanding in the early years, it offers unique daily joy, bonding, and motivation that cannot be replicated elsewhere.
• Working Hours and Hard Limits: Many contributors share practical shifts after having kids, such as maintaining strong ambition while strictly adhering to boundaries (e.g., leaving work on time, reducing hours, or cutting out non-essential commitments).

Partnership is not just agreement to host learning experiences. It requires that the experience is designed for and supportive of learning.

• Corporate AI Push and Workplace Friction:

  • Management increasingly forces knowledge workers to adopt generative AI tools under exaggerated productivity claims, shifting the burden of debugging and verification onto employees.
  • Workers face risks of deskilling, higher workloads, and eventual job displacement while being expected to train the very models intended to replace them.

• Everyday Resistance and Auditing Tactics:

  • Log and Expose the Friction: Avoid quietly fixing AI mistakes; document the exact time and labor required to audit, correct hallucinations, and rewrite outputs to prove hidden costs.
  • Malicious Compliance: Adhere strictly to top-down AI workflows without doing uncredited manual polishing, letting leadership see the unvarnished quality and flaws of the raw output.
  • Leverage Security and Legal Concerns: Raise formal concerns with legal, IT, or compliance departments regarding data privacy, copyright risks, trade secret exposure, and third-party data collection.

• Collective Action and Boundary Setting:

  • Build Solidarity with Coworkers: Compare experiences across teams to counter management claims that AI tools are functioning seamlessly elsewhere in the company.
  • Push for Formal Policy Guardrails: Use unions, employee councils, or collective feedback to demand transparent AI policies, protections against automated monitoring, and safeguards against layoffs.

Example of female typist covering keyboard up with handkerchief to show how light the touch needed would be.

"Note that the handkerchief is hardly ruffled."

I feel like that time period and the century before, being able to do something through a thin piece of cloth was indicative of how little effort was required, or how dainty something could be. It definitely feels very gendered to me, but I'll have to find other historical examples to back this argument up.

Regarding the report on Uber for nursing: https://ainowinstitute.org/publications/uber-for-nursing

Katie J. Wells quote from near the end of the interview:

...when you have very very low expectations for public government, Silicon Valley looks like an OK alternative... the technology in your pocket somehow looks more useful.

This says so much about citizenship and the relationship between democracy, autocracy, and technology.

Echoes the LinkedIn comment by Tim Appleby in response to the (incorrect) perception that LLM-based chatbots are better than a traditional search engine for search:

While I agree that it isnt and should not be a search engine, it performs the function of finding source information on websites better than some of the actual search engines... Googling anything today results in 3 ads, 4 AI generated articles that zapped into existence the second you hit search with no sources and a fake author, and SEO-hacked keyword pages for things that are entirely irrelevant. When everything is this broken, its hard to say the less-sh*t option [i.e. a chatbot] isn't a viable one.

via Bob Doto, but ne doesn't cite the original source... https://www.reddit.com/r/Zettelkasten/comments/1vlh6ru/organizing_for_the_sake_of_organizing/

• The AI Code Review Bottleneck:
  • Rapid AI code generation shifts the primary software engineering bottleneck from writing code to reviewing and maintaining it.
  • Unvetted AI-generated code leads to growing cognitive debt, potential production outages, and burnout among senior engineers.
• Automated Guardrails & Self-Correction:
  • To scale or eliminate manual code reviews, feedback loops should be engineered directly into the local agent environment.
  • Deterministic tools like Semgrep, linters, and architectural unit tests enforce coding standards and prevent bad patterns automatically.
  • Harness stop hooks and execution loops (e.g., Ralph loops or goal modes) automatically feeds error output back to agents for self-correction.
• Importance of the Agent Harness:
  • The harness (providing tools, memory, and execution capabilities) often impacts performance and success rates more than the specific LLM.
  • Tool capabilities and harness behaviors change rapidly, requiring teams to continuously experiment rather than imposing rigid vendor policies.
• Shift to Upfront Architecture & Specification:
  • AI shifts the engineer's core role toward high-level system design, defining modular boundaries, and writing behavioral specifications/tests up front.
  • Developers can mine AI session logs to identify recurring manual corrections and turn them into permanent automated guardrails.

Summary of, and references to research on, the gigification of labour.

大多数人认为AI会取代人类工作，导致失业，但作者认为AI实际上改变了人类工作方式，让人们转向更高层次的任务分配和管理。这挑战了关于AI与就业关系的传统叙事，表明AI可能创造新的工作形式而非简单替代人类。

大多数人认为工会会抵制机器人技术以保护现有工作岗位，但作者提出了一个更激进的解读：工会实际上是在寻求对自动化决策的否决权，而不仅仅是被动抵抗。这表明工人正在主动争取对工厂未来的控制权，而不仅仅是保护现状。

Bank Światowy prognozuje, że AI może zwiększyć PKB Polski o 12% do 2035 r., ale jednocześnie zmniejszyć zatrudnienie nawet o 350 tys. etatów.

Największe zyski mają dotyczyć IT i budownictwa (wzrost nawet o 25%). Sektor finansowy może rosnąć gospodarczo, ale zatrudnienie w nim może spaść o 25%. Programiści i branża IT także mogą odczuć spadek liczby etatów. Budownictwo może zyskać ok. 20% miejsc pracy.

Jeśli Polacy nie będą chętni do zmiany zawodu, pracę straci nawet 350 tys. osób. Przy dużej mobilności pracowników ubytek etatów ma wynieść wg modeli tylko 3 tys.

Zmiany odczuje budżet państwa – spadną wpływy z PIT i składek ZUS, ale wzrosną z CIT i VAT.

Hacker News Discussion

• The Pull Request Fatigue Loop: A widely upvoted comment highlighted how a colleague using Claude flooded the team with AI-generated PRs, then complained when they languished; reviewers subconsciously avoided them because reviewing AI code for hidden hallucinations requires an immense, asymmetric amount of human effort.
• The Asymmetry of Feedback: Users noted that it feels deeply dismissive when a human invests an hour of intense cognitive effort to thoughtfully review a massive PR, only to receive an instantaneous, AI-generated reply or amendment from the author.
• Review Scalability vs. Guardrails: Some participants argued that traditional code review cannot scale to prolific AI agents or hyper-productive humans; they suggested transitioning to automated guardrails—such as linters, auto-formatters, and robust end-to-end continuous deployment testing—to offset the review bottleneck.
• Code Review as a Cultural Practice: The discussion underscored that code review should function as a collaborative team process for shared understanding and mentorship rather than a cold, adversarial gatekeeper blocking a developer from merging code.
• Exploiting Token Budgets: One commenter observed that large, complex PRs often trigger scrolling blindness in humans and cause LLMs to run out of token budget, leading both to blindly approve the change with a generic "looks good to me."

大多数人认为人类可以通过转向更复杂的领域或学习高级技能来应对AI挑战，但作者暗示即使是这些领域也可能被AI迅速渗透，表达了一种'无处可逃'的悲观情绪。这与'人类总能找到AI无法替代的领域'的主流乐观观点相悖。

• Evolutionary Disconnect: Much like modern diets packed with physical toxins like white flour and high fructose corn syrup, working a standard corporate job is an artificial environment that can be intellectually toxic to humans. From an evolutionary perspective, human beings are optimized to work in small, tight-knit tribes rather than massive organizations.
• The "Tree" Structure and Loss of Freedom: Because large corporations cannot naturally function as a single massive unit, they split into smaller groups arranged in a tree hierarchy. This dynamic forces a manager to act as a singular proxy for their entire team when dealing with higher levels of command, which systematically restricts individual freedom of action in inverse proportion to the total size of the company.
• The Illusion of the Fake Tribe: A corporate group of 10 people offers the superficial appearance of a natural evolutionary unit, but it fundamentally strips away individual initiative. This dynamic leaves workers feeling a vague sense of malaise because their daily environment lacks true agency.
• The Unique Impact on Software Engineers: Software engineering is inherently centered on building original things from scratch. In a massive organizational hierarchy, programmers face intense friction due to legacy codebases, rigid cross-team interfaces, and administrative barriers. This restriction stops them from implementing new ideas, which ultimately diminishes their capacity to generate creative thoughts in the first place.
• The Advantages of Going Small: While starting a startup or joining an early-stage team carries a high risk of failure, it mimics a more natural human environment by functioning as a low-restriction exhaust system for the brain. Ambitious individuals learn rapidly through raw, uninhibited execution, transforming from the "downtrodden air of refugees" seen in corporate jobs to highly confident operators.

Hacker News Discussion

• The Problem of Shadow Hierarchies: A primary critique argued that flat, hierarchy-less alternative business structures are incredibly difficult to maintain. Commenters noted that when official hierarchies are removed, undocumented "shadow hierarchies" naturally emerge because human nature dictates that people will inevitably defer coordination and decision-making responsibilities to others.
• Inverting the Power Structure: One participant proposed a theoretical alternative where a cooperative of workers hires their own managers to measure value and handle administrative burdens, rather than having managers hire workers. This bottom-up framework would allow independent contributors to explicitly choose whether they want or need someone to coordinate their efforts.
• The Hidden Hardship of Information Sharing: Users highlighted that attempting to simulate an open market economy within a single firm—by forcing independent cells to compete with each other—often results in teams hoarding resources, hiding information, and gatekeeping best practices out of pure self-preservation.
• The Challenge of Communicating "The Big Picture": In response to complaints that corporate leadership keeps strategic visions hidden from workers, counterarguments pointed out that organizing, translating, and clearly communicating cohesive high-level strategy across thousands of employees is an extraordinarily difficult logistical problem, rather than a deliberate conspiracy to keep staff in the dark.
• Startups as Future Conglomerates: Some commenters pointed out an underlying irony in the essay's core advice: the ultimate objective of a venture-backed startup is achieving exponential growth, meaning a founder's goal is explicitly to build the exact type of massive, slow-moving corporate entity that Paul Graham warns against joining.

大多数人认为AI主要擅长内容创作和简单任务，但作者认为数据分析和研究这些复杂认知任务才是增长最快的应用领域。这挑战了AI只能处理简单或创造性任务的共识，表明AI正在深入传统上需要人类专业知识的领域。

• The Reality of AI Burnout: AI-assisted software engineering delivers high-speed productivity on paper, but it introduces a hidden cost of cognitive overload, fatigue, and a new form of "AI burnout."
• The Productivity Trap: AI tools compress highly intense cognitive workflows (prompting, reviewing, and debugging) into shorter periods. Instead of working less, engineers fill saved time with more tasks, replacing rewarding creative work with exhausting oversight.
• Loss of Craft and Fulfillment: The traditional cycle of planning and writing code is highly satisfying. AI bypasses this tactile process, turning engineers into supervisors of code they didn't write, which dramatically diminishes feelings of ownership, pride, and achievement.
• Erase of System Intuition: Delegating codebase comprehension to AI agents leads to "cognitive debt." Engineers stop holding the architecture and edge cases in their heads, losing the deep intuition required to spot bugs or design flaws early.
• Review Bottlenecks: AI dramatically increases code output, but human capacity to review that code remains unchanged. Senior engineers absorb a disproportionate amount of risk and cognitive load trying to clean up thousands of lines of mediocre, AI-generated code.
• Practical Solutions to Reclaim Balance:
  • Acknowledge Wins: Keep a win-log, track hours, and demo results to restore a sense of personal achievement.
  • Rethink AI Workflows: Focus heavily on the "planning" phase, decompose large tasks, and avoid jumping straight from one AI-heavy task to another.
  • Preserve the Craft: Protect specific hours or passion projects for manual coding without AI intervention.
  • Set Boundaries: Enforce strict work hours, take deliberate breaks to counter continuous cognitive demands, and stop once daily goals are met.

• Box founder Aaron Levie coined the phrase "AI psychosis" to describe tech executives who suffer from delusions of AI grandeur due to being too distant from the actual day-to-day operations where value is generated.
• Because CEOs only interact with high-level prototypes, they mistakenly leap to the conclusion that AI agents can effortlessly handle full workloads without realizing the heavy human labor required to review code, patch bugs, catch hallucinations, and train models.
• This executive delusion has real-world consequences, driving severe workforce reductions; in the first five months of 2026, over 115,000 tech workers were laid off—nearly matching the total for all of 2025—with AI cited as a primary justification.
• High-profile actions, such as ClickUp CEO Zeb Evans laying off 22% of his workforce after deploying 3,000 AI agents, are framed as shifting humans into "manager and verifier" roles for AI outputs.
• Empirical data from UC Berkeley, NBER, and MIT refutes these massive productivity assumptions, demonstrating no robust link between current AI adoption and aggregate productivity gains, with MIT predicting baseline competence on text tasks will not materialize until 2029.
• A Harvard Business Review study warns that flooding an organization with unverified AI output merely shifts bottlenecks onto executives, risking widespread structural and operational chaos if human oversight fails to scale.

Hacker News Discussion

• Distance from Reality: Commenters strongly agreed with the premise that executives live in a bubble, noting that they deal primarily with administrative assistants, sycophants, and curated, "happy path" demos that look like magic, making them blind to edge cases and errors.
• The "Yes-Man" Nature of AI: Multiple users pointed out that AI agents behave like the ultimate corporate sycophants—they work 24/7, lack internal moral conflict, and never say no—making them highly attractive to authoritative executives who dislike pushback from human workers.
• Absence of Self-Preservation: A key distinction raised in the comments is that unlike human employees, AI lacks "self-preservation," a sense of reputation, or a fear of consequences, meaning an agent will confidently delete a production database or kill its own server processes without hesitation.
• Misuse of the Term: Some participants criticized the article's title as clickbait, arguing that "AI psychosis" should describe literal psychological delusions in individuals interacting with AI rather than standard corporate incompetence or unrealistic executive expectations.
• Projection of Executive Work: A popular theory suggested that CEOs assume AI can replace everyone's job because it can easily replicate their own daily tasks, such as generating slide decks, sending emails, and attending high-level meetings.

• The author questions why the promised 10x productivity gains from AI do not result in more time off for workers, such as a four-day work week.
• If AI can allow a worker to complete a week's worth of output by Monday afternoon, Friday could theoretically be declared an "AI workers' day" where agents handle the workload.
• This extra day off would benefit everyone, including the C-suite and boards of directors, who could spend the time leisure-seeking rather than being at the office.
• Despite entering a revolution across every sector of human productivity, the fundamental structure of the five-day work week remains unchanged.
• The high cost of living and childcare (e.g., $6,000/month in California) adds pressure on employees, making the flexibility of fewer office days highly desirable.

Hacker News Discussion

• Capturing Productivity Gains: Many commenters note that while workers are pushed to adopt AI tools to multiply their output, they do not stand to benefit financially or receive more time off; instead, the economic gains are heavily consolidated by employers and capital owners.
• The Reality of Salaries: A discussion emerged around how salaried employees are typically compensated. Some argue that employees are paid for their availability and time rather than direct output, making it difficult to negotiate less time for the same pay.
• Fear and Leverage: Users highlight that instead of increased compensation, the rise of AI has brought widespread fear of layoffs and lower job security, keeping workers compliant rather than demanding a 4-day workweek.
• Collective Action and Policy: Several participants suggest that asking an employer for a day off individually is naive due to market competition and the Prisoner's Dilemma. They argue that structural changes like historical worker protections, unions, or government-led policies like Universal Basic Income (UBI) are necessary to shift the status quo.

• The author expresses profound frustration with the pervasive infiltration of AI-generated answers into daily and professional communications.
• Encountering malware-spreading repositories on GitHub, the author sought a resolution via an open discussion, only to repeatedly receive copy-pasted AI answers that offered no practical utility.
• In a workplace scenario, a business owner repeatedly forwarded unread ChatGPT screenshots rather than engaging with or directly answering the author's specific business questions.
• Online interpersonal interactions have also been compromised, illustrated by an instance where the author discovered they were conversing with an AI agent after exchanging multiple messages on Reddit.
• The core grievance highlights a growing societal loss of genuine human connection, as individuals increasingly forward raw AI text instead of thinking for themselves or conversing sincerely.

Hacker News Discussion

• Erosion of Workplace Culture: Many commenters emphasized that relying on AI to respond to colleagues destroys organic trust-building opportunities. Reaching out to teammates is often less about extracting text and more about establishing communication, context, and validation.
• Lazy Delegation and Management Failures: Participants noted that heavy corporate pushes for AI productivity have caused a misunderstanding of boundaries. Instead of using it to handle grunt work, some employees lazily offload all cognitive overhead to chatbots without reviewing or fact-checking the output.
• Analogy to "Let Me Google That For You": Sending a raw, unverified AI response to a direct question is widely viewed as passive-aggressive and insulting. It conveys a strong signal that the sender did not respect the asker's time enough to even read the answer they forwarded.
• Existential Risk to Job Security: Several users pointed out that individuals who mindlessly pass along unedited AI screenshots are strongly signaling that their entire job function can be replaced by an LLM, making them prime candidates for corporate layoffs.
• The Effort to Remain Human: Some users shared that they have intentionally begun introducing written idiosyncrasies into their messages to prove they are human, though others countered that future AI models will inevitably mimic these individual quirks anyway.

Connects to the "pipeline problem" where AI solutions destroy entry-level jobs, thereby also destroying the path to midcareer jobs...

Can't help but read this and think about retirees, children, homemakers, etc... how do things like "volunteering" or "chores" relate to "work" in this sense?

If you're a startup looking to hire remote developers, Witarist (witarist.com/hire) offers pre-vetted engineers from India with 48-hour onboarding, no job portals required.

Grades perhaps tell me what you know; experience shows me what you can do.

大多数人认为工作是人类身份和意义的核心，但作者质疑这一基本假设，暗示AI可能使工作变得非必要，这挑战了现代社会对工作的核心价值认知。作者暗示我们需要重新思考人类在没有工作的情况下如何找到意义，这与主流经济和社会观念相悖。

• The "Invisible" Employee: Leyla Kazim describes a year-long experiment in a previous corporate role where she essentially stopped working to see if anyone would notice.
• Exploiting Managerial Gaps: Following a department restructure that left her with a vague role and a distracted manager, she realized her output wasn't being tracked or valued.
• Performative Productivity: To maintain the illusion of work, she used "visual cues" of busyness, such as keeping complex spreadsheets open and spending 15 minutes a week fabricating data for slide decks that she knew would never be audited.
• Time Reclaimed: Instead of performing job duties, she spent her office hours planning an extensive 10-month trip around the world, effectively using the company's time and salary to fund her personal exit strategy.
• Existential Dread: Despite the "success" of the ruse, Kazim reflects on the soul-crushing nature of "meaningless theatre," noting that trading one's life force for a paycheck in a redundant role is a form of personal tragedy.
• Final Resignation: She eventually resigned of her own accord, concluding that the experience proved "hard work" in a corporate setting is often more about perceived performance than actual utility.

Hacker News Discussion

• Moral and Personal Integrity: Many commenters argued that while "time theft" might feel like a victory against a cold corporation, it often leads to a loss of self-respect and professional skill atrophy.
• The "Bullshit Jobs" Reality: Discussion centered on David Graeber's theory of "Bullshit Jobs," with users sharing their own stories of being stuck in roles where their absence would have zero impact on the organization.
• Employer Resentment: Some users expressed frustration with the author, suggesting that this type of behavior justifies heavy-handed management, surveillance, and the replacement of human roles with AI.
• Corporate Failure vs. Individual Ethics: There was a debate on whether the blame lies with the individual for being "dishonest" or with the organization for being so dysfunctional and bloated that a full-time employee could disappear for a year unnoticed.
• The "Boreout" Phenomenon: Several participants noted that doing nothing is often more exhausting and mentally taxing than being busy, leading to a specific type of workplace depression known as "boreout."

• Soft Skills and Judgment: Commenters emphasize that empathy, social skills, and the ability to build relationships remain highly valuable, as AI cannot truly navigate corporate politics or seek mutual human benefit.
• Domain Expertise: While AI can generate code or content, humans are still required to provide the "judgment" to determine what is worth building and to foresee how architectural decisions will impact a project years down the line.
• Physical Trades: Many users suggest that "blue-collar" trades—such as plumbing, electrical work, and construction—are the most future-proof because the physical dexterity and adaptability required for these tasks are far beyond current robotic capabilities.
• Communication: Superior written and verbal communication is cited as a vital skill, both for leadership and for effectively "prompting" or directing AI tools to achieve specific professional goals.
• Critical Thinking: The ability to identify when a task definition is wrong or when a product doesn't "make sense" for a human user is seen as a distinct human advantage over models that follow instructions literally.
• Legal and Accountability Roles: Jobs that require a "human in the loop" for legal liability or ethical reasons—such as doctors, lawyers, and military personnel—are considered safer from complete automation.
• Metalearning: The most important skill may be the ability to learn new tools quickly and discard old ones without emotional attachment, adapting as the technology evolves.

这个观点强调了未来软件工程师的价值不在于他们能做什么，而在于他们如何利用AI来提升自己的思考能力。

作者提出了‘token’作为人工智能认知工作的基本单位，这一概念具有革命性，它将抽象的人工智能转化为可度量的资源。

作者指出，知识工作中普遍存在的问题是无法客观判断工作质量，因此人们依赖于代理指标，这是一个非共识观点。

大多数人认为自动化和人工智能只会取代低技能工作，但作者提出，多智能体系统可能会像亨利·福特的流水线一样颠覆白领知识工作。

这是一个历史性的转折点，标志着人类雇佣关系的新时代。AI成为人类老板的可能性比许多人想象的要快得多，这可能彻底改变我们对工作、权威和职业发展的基本理解。

这一现象揭示了AI正在从虚拟助手转变为实际的经济行为主体，Luna作为首个AI雇主的概念令人震惊，它挑战了传统的人类雇佣关系和企业管理模式，预示着未来可能出现AI主导的商业模式，同时也引发了关于AI责任、伦理和监管的深刻问题。

令人惊讶的是：Relvy能够显著减少企业的轮班负担，这意味着公司可能不再需要像过去那样依赖昂贵且疲劳的人类工程师来处理紧急警报，这可能引发运维团队结构和技能需求的重大转变。

令人惊讶的是：文章指出顶级工程师可能同时管理20-30个AI代理，实现数量级的生产力提升。这一数字远超传统认知，暗示AI正在重新定义个人生产力的极限。这种能力意味着未来软件公司的组织结构可能需要彻底重构，从大型团队转向小型高效团队。

令人惊讶的是：超过一半的员工宁愿手动完成工作也不使用公司提供的AI工具，这一现象表明AI技术在实际应用中遇到了重大阻力。这不仅仅是技术问题，更是工作习惯和组织文化的深层次冲突。

452 个任务跨越投资银行、管理咨询、公司法三个领域——这三个领域是全球「知识密集型工作」的代表，也是最难被 AI 替代的白领职业。APEX-Agents 选择这三个领域作为 benchmark，本身就是一个宣言：AI 已经准备好挑战那些曾经被认为「最安全」的专业工作。而最高分只有 33.3% 这个事实同样是一个宣言：这个挑战才刚刚开始。

【启发】这句话是对未来知识工作分工的最清晰定义：人负责「品味、方向、意义」，AI 负责「执行、维护、连接」。这不是「AI 替代人」的叙事，而是「AI 承担所有繁琐工作，人专注于真正重要的判断」。对团队 AI 工具设计的启发：最好的 AI 工具设计应该让人的时间 100% 用在「只有人才能做的事」上——而这个边界，正在随着 AI 能力的提升不断向内收缩。

聊天界面的致命弱点在于缺乏结构，将所有输出压平为文本流，导致难以对比和关联。这解释了为何ChatGPT式交互适合探索却不适合严肃的团队协作——它把获得好结果的全部重担都压在了用户的提示词上。

这一反问揭示了AI时代的底层逻辑转换：算力/电力的消耗直接等同于生产力。过去的优化目标是“节能”，而现在和未来的核心命题是“转化率”——如何将廉价的电力通过AI模型转化为高价值的认知与执行工作。这是对能源-智力转换效率的极致追求。

「喂饱 Agent 过夜」这个概念令人震惊：未来的研究者需要像农民「播种」一样，在下班前精心设计好「足够 Agent 形态的」长任务，让 AI 在人类睡眠的 8 小时里完成相当于 200 人时的工作，然后早上来「收割结果」。这意味着人类工作的节奏将被彻底重组——不再是「我来执行任务」，而是「我来为任务执行做准备」。

• Concept of "Surveillance Wages": Employers are increasingly using "surveillance wages," where algorithms analyze personal data to determine the minimum pay a candidate will accept, moving away from traditional experience-based pay.
• Data Sources: Companies may utilize signals of financial vulnerability, such as credit card balances, payday loan history, ZIP codes, and even social media activity to gauge a worker's leverage.
• Information Asymmetry: The practice creates a significant power imbalance, as employers may have access to a candidate's past compensation and financial health via background and credit checks, while the candidate has little to no insight into the company's internal data.
• Algorithmic Influence: Tools originally designed for "surveillance pricing" in consumer markets (e.g., airline tickets or retail) are being adapted for the labor market to predict behavior and optimize cost-cutting.
• Risks and Bias: Experts warn that these practices can lead to systemic discrimination, targeting those in desperate financial situations or predicting life events like pregnancy or union interest.
• Industry Adoption: An audit found that major companies in healthcare, logistics, customer service, and retail are already utilizing vendors that offer these data-driven algorithmic tools.

Hacker News Discussion

• Verification of Past Pay: Several users noted that while many states now ban asking for salary history, employers often bypass this by using third-party background check services (like The Work Number or credit reports) that provide precise historical income data.
• The Role of AI/Algorithms: Commenters discussed how companies like Salesforce and Intuit are being linked to these practices, using "behavioral science" to predict the lowest "reservation price" for a new hire.
• Data Privacy Concerns: There is significant skepticism regarding the "anonymization" of this data; many believe that even "aggregate" data is easily deanonymized when paired with specific resumes.
• Strategic Advice: Users suggest being extremely cautious about what is shared on social media and checking your own reports from major data brokers (like Equifax or LexisNexis) to see what information is being sold to potential employers.
• Legal and Ethical Debate: Some argue that this is a logical extension of market efficiency, while most view it as a predatory practice that exploits vulnerable workers and erodes the bargaining power of the labor force.

Tom Hanks has mentioned framing typewritten letters before, but German exams?

any single sentence that compares and contrasts this work with prior work.

any single sentence that compares and contrasts this work with prior work.

any single sentence that compares and contrasts this work with prior work.

• The Complexity Bias: Engineering cultures often reward over-engineered solutions because they provide a more "impressive" narrative for promotion packets and design reviews compared to simple, effective ones.
• Hiring Pitfalls: The problem begins at the interview stage, where candidates are often pushed toward complex distributed systems even when a simple solution is more appropriate, teaching them that "simple isn't enough."
• Unearned Complexity: While complexity is necessary for massive scale or multi-team coordination, many teams adopt "unearned complexity"—sharding databases or adding abstractions for problems that don't yet exist.
• Invisible Virtues: Simplicity is harder to see and appreciate than complexity. Engineers who avoid problems by choosing simpler paths often find their contributions overlooked because there is no "big system" to point to.
• Shifting the Default: To fix this, teams should change the questions they ask in reviews from "how do we scale this?" to "what is the simplest version we can ship?" and reward the deletion of code or the decision to defer unnecessary features.

Hacker News Discussion

• The "Google Sheets" Interview Trap: A popular anecdote in the thread involved a candidate suggesting Google Sheets to solve a spreadsheet-sharing problem. While pragmatic, it led to an awkward "failure" because the interviewer wanted to see software design skills, highlighting the disconnect between business value and interview expectations.
• Interviewers vs. Candidates: Many users argued that if a candidate gives a simple, non-technical answer, a skilled interviewer should acknowledge it as correct and then pivot to a hypothetical scenario ("If we had to build it, how would you design it?") rather than penalizing the candidate.
• Professionalism vs. Compliance: Some commenters believe developers should be "difficult" by pushing back against unnecessary builds to save company resources, while others argued that refusing to engage in a technical interview exercise is a red flag for future collaboration.
• The Cost of "Acting Smart": There was a consensus that "acting smart" (building complex things) is often more financially rewarding than "being smart" (solving the problem simply), leading to a industry-wide "cargo culting" of complex architectures.
• Visible Deletion: Several users noted that the most valuable engineers are often those who delete thousands of lines of code, but that organizations lack the metrics or cultural frameworks to celebrate and promote based on reduction rather than addition.

Summary of "AI Fatigue is Real"

• The Productivity Paradox: AI significantly speeds up individual tasks (e.g., turning a 3-hour task into 45 minutes), but this doesn't lead to more free time. Instead, the baseline for "normal" output shifts, and the work expands to fill the new capacity, leading to a relentless pace.
• From Creator to Reviewer: Engineering work is shifting from "generative" (energizing, flow-state tasks) to "evaluative" (draining, decision-fatigue tasks). Developers now spend their days as "quality inspectors" on an unending assembly line of AI-generated code.
• The Cost of Nondeterminism: Engineers are trained for determinism (same input = same output). AI’s probabilistic nature creates a constant cognitive load because the output is always "suspect," requiring more rigorous review than code written by a trusted human colleague.
• Context-Switching Exhaustion: Because tasks are "faster," engineers now touch 6–8 different problems a day instead of focusing on one. The mental cost of switching contexts so frequently is "brutally expensive" for the human brain.
• Skill Atrophy: Much like GPS has weakened our innate sense of direction, over-reliance on AI coding tools can cause core technical reasoning and mental mapping of codebases to atrophy.
• Strategies for Sustainability:
  • Time-boxing: Setting strict timers for AI sessions to avoid "prompt spirals."
  • Separating Phases: Dedicating mornings to deep thinking and afternoons to AI-assisted execution.
  • Accepting "Good Enough": Setting the bar at 70% usable output and fixing the rest manually to reduce frustration.
  • Strategic Hype Management: Ignoring every new tool launch and focusing on mastering one primary assistant.

• Task Expansion & Role Blurring: AI lowers the barrier to entry for complex tasks, leading employees to take on work outside their core expertise. Product managers and designers are now writing code, while researchers take on engineering tasks.
• Specialist Burden: This expansion creates a "cleanup" tax. For example, senior engineers now spend significant time reviewing, debugging, and mentoring colleagues who produce "vibe-coded" AI outputs, often through informal and unmanaged channels like Slack.
• The "Ambient Work" Phenomenon: Because AI interactions feel conversational and "easy," work has become ambient. Employees find themselves prompting AI during lunch, between meetings, or late at night, eliminating natural mental downtime.
• Intensified Multitasking: Workers are running multiple AI agents in parallel while simultaneously performing manual tasks. This creates a high sense of "momentum" but leads to extreme cognitive load and constant attention-switching.
• The Productivity Trap: AI acts as a "partner" that makes revived or deferred tasks feel doable. This creates a flywheel where people don't work less; they simply take on more volume, leading to "unsustainable intensity" that managers often mistake for genuine productivity.
• Sustainability Risks: The researchers warn that while AI feels like "play" initially, it eventually leads to cognitive fatigue, impaired decision-making, and burnout as the quiet increase in workload becomes overwhelming.

Hacker News Discussion

• Cognitive Fatigue: Users highlighted that "AI fatigue" is distinct from normal work tiredness. It stems from the "constant vigilance" required to audit AI output and the lack of a "flow state" due to unpredictable waiting times for generations.
• Executive Function Strain: Commenters noted that managing autonomous agents is more exhausting than manual work. One user compared it to Level 3 autonomous driving—you aren't driving, but you must remain "fully hands-on" to ensure the AI doesn't touch the wrong files or hallucinate.
• The Jevons Paradox: Several participants pointed out that as the "cost" of work decreases due to AI, the demand for work increases proportionally. Instead of saving time, workers are expected to triple their output, which leaves them more stressed than before.
• Management Expectations: A common theme was that leadership often mandates AI usage and pre-supposes productivity gains, leaving no room for cases where AI makes work slower or lower quality. This forces employees to "perform" productivity while working longer hours.
• Vibe Coding vs. Engineering: There is a heated debate between those who see "vibe coding" (prompt-heavy development) as a massive efficiency gain and veterans who argue it produces "average code" that becomes a maintenance nightmare in large, legacy codebases.

This should be true to every team work

• AWS CEO Matt Garman argues against replacing junior developers with AI, calling it "one of the dumbest ideas."
• Juniors excel with AI tools due to recent exposure, using them daily more than seniors (55.5% per Stack Overflow survey).
• They are cheapest to employ, so not ideal for cost-cutting; true savings require broader optimization.
• Cutting juniors disrupts talent pipeline, stifling fresh ideas and future leaders; tech workforce demand grows rapidly.
• AI boosts productivity, enabling more software creation, but jobs will evolve—fundamentals remain key.

Hacker News Discussion

• AI accelerates junior ramp-up by handling boilerplate, APIs, imports, freeing time for system understanding and learning.
• Juniors ask "dumb questions" revealing flaws, useless abstractions; seniors may hesitate due to face-saving or experience.
• Need juniors for talent pipeline; skipping them creates senior shortages in 4-5 years as workloads pile up.
• Team leads foster vulnerability by modeling questions, identifying "superpowers" to build confidence.
• Debates on AI vs. docs struggle: AI speeds answers but may skip broader discovery; friction aids deep learning.

• Modern knowledge work is dominated by interruptions (meetings, Slack, emails), making long, focused blocks of work rare.
• The author models a workday using three key parameters: λ (interruptions per hour), Δ (recovery time after each interruption), and θ (minimum uninterrupted block needed for meaningful work).
• Interruptions are treated as a (simplified) Poisson process, but in reality they often come in clusters, which further worsens the ability to regain focus.
• Recovery time Δ represents how long it takes to rebuild mental context; even short “quick questions” can cost 10–20 minutes of effective productivity.
• Theta θ captures that five 10‑minute blocks are not equivalent to one 50‑minute block, because fragmented time below θ produces little real progress.
• The concept of “capacity” is defined as how many θ‑sized chunks fit into all focus blocks, using a floor function, so small changes in block lengths or θ can dramatically change effective output.
• Simulations of 100 days show that with harsh parameters (e.g., λ ≈ 3, Δ ≈ 20, θ = 60), long focus blocks are extremely rare and many days have almost no deep work.
• Empirical studies report very high interruption/activity-switch rates (e.g., activity switches every ≈3 minutes, or interruptions every ≈2 minutes for heavy collaborators), implying real-world λ is often far worse than the “toy” examples.
• Under high λ (e.g., 15 interruptions/hour) and moderate Δ, simulated days become walls of interruptions with almost no 15‑minute blocks, illustrating how deep work becomes statistically impossible.
• When λ and Δ are reduced (e.g., λ = 1, Δ = 10), most days contain multiple 60‑minute blocks, showing that structural conditions—not personal discipline—largely drive good vs. bad days.
• A heatmap over λ and Δ visualizes expected capacity; “good,” “typical,” and “terrible” zones differ dramatically in how many deep-work blocks they allow.
• Increasing θ (e.g., from 30 to 60 minutes) sharply reduces capacity in typical/terrible regimes, explaining why big, hard tasks feel impossible while smaller tasks remain doable.
• Monte Carlo simulations (many repeated day simulations) estimate expected capacity for each (λ, Δ, θ) combination, relying on the law of large numbers.
• Reducing λ is the most powerful lever: going from 1 to 2 interruptions/hour can slash the probability of getting three 60‑minute blocks from about 70% to about 14% in the example.
• Many interruptions are self-inflicted (e.g., frequent inbox/Slack checking), so batching communication and making access to your attention more “expensive” can substantially improve conditions.
• Matching θ to your environment means breaking high‑θ projects into smaller independent tasks, and reserving low‑λ windows (e.g., early mornings) for the longest, hardest work.
• Reducing Δ involves leaving breadcrumbs (notes to self), avoiding wide context switches, and using small rituals to re-enter focus so that resumption is faster.
• The core message is that deep work is rare not because of individual weakness but because λ and Δ in modern workplaces make it mathematically unlikely.
• Small structural changes—slightly fewer interruptions, somewhat shorter recovery, smaller-task design—can shift the whole distribution of days from “fragmented by default” to “deep work routinely possible.”
• The author recommends experimenting with a protected 90‑minute daily block as a personal lab to observe how λ, Δ, and θ play out and to reclaim focus

Here Cook compares the working class to worker bees having their hard work, honey, stolen from them. Similarly, the working class is being taken advantage of by people who keep getting richer while the laborers toil away.

(

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This was a period of time where England was known as the empire on which the sun never sets. Military might and the Industrial Revolution turned England into a juggernaut of powerhouses in the world theater. Little did the world knew that it was the labor of poor children that made the English Industrial Evolution possible as able bodied men were busy fighting. Orphaned children from cities and parishes were contracted from city officials to factory owners to work until they reach 21 or 24 years old. There were no one who fought for their welfare or rights. The children worked 12 to 16 hours a day with little substantial food, hence "their pale and sunken faces". The children were essentially slaves.

https://blogs.baylor.edu/19crs/2017/06/28/textual-revisions-and-constructed-narratives-in-elizabeth-barretts-the-cry-of-the-children/#:~:text=The%20first%20published%20version%20of%20Elizabeth%20Barrett,of%20signs%20of%20human%20or%20divine%20mercy**

This article has photos of the book, the poem, and images from the survey of children working in mines relating to "Cry of The Children".

https://www.youtube.com/shorts/YdWLxoHYR1E

This video shows how close, cramp, and claustrophobic the mines would be. Also, the ground is sometimes lined with rails, other times consists purely of mud and even imbedded with large rocks. This little clip is an attempt to let the readers see the harsh conditions the children working in mines had to deal with daily.

Elizabeth Browning was friends and frequent correspondent with Richard Hengist Horne. RH Horne was the assistant commissioner to an inquiry that reported the "Physical & Moral Conditions of the Children and Young Persons Employed in Mines and Manufacture." The horrific conditions that Horne related to EBB spurred her to write "Cry of the Children" (Robertson).

Because people spend over half of their waking hours at work, the work environment–including factors like high demands, stress, bullying, low support, lack of control, and feeling undervalued–plays a major role in increasing the risk of mental health issues such as depression and anxiety.

for - definition - civilian labor corp - future of work - there will have to be new labor pools in the economy that free market has ignored to date - caretakers for the elderly - climate adaptation - affordable housing

• AI, intended to free workers, is causing longer work hours and increased pressure, spreading 996 culture to Western AI startups.
• AI tools never tire, creating psychological pressure to constantly work and increasing feelings of guilt during rest.
• Historical advances like lamps and bulbs extended work hours; AI similarly shifts "can work" into "should work."
• Philosopher Byung-Chul Han's "Burnout Society" concept shows internalized self-discipline drives overwork, amplified by AI's "excess of positivity."
• The hyper-productivity loop leads to burnout, reduced creativity, and diminishing returns despite increased effort.
• Rest is framed as resistance and vital for innovation, which thrives on reflection, not constant activity.
• The key challenge is adopting a healthy culture around AI use that avoids exploitation and preserves human well-being.

Evidence: Group work mixing Bengali/English/Hindi leads to vocabulary growth and creative output (p. 8). Why it matters: Supports the research question about creativity/imagination.

• Internal documents reviewed by The New York Times show Amazon plans to automate up to 75% of its operations in the coming years.
• The company expects automation to replace or eliminate over 500,000 U.S. jobs by 2033, primarily in warehouses and fulfillment centers.
• By 2027, automation could allow Amazon to avoid hiring around 160,000 new workers, saving about 30 cents per package shipped.
• This strategy is projected to save $12.6 billion in labor costs between 2025 and 2027.
• Amazon’s workforce tripled since 2018 to approximately 1.2 million U.S. employees, but automation is expected to stabilize or reduce future headcount despite rising sales.
• Executives presented to the board that automation could let the company double sales volume by 2033 without needing additional hires.
• Amazon’s Shreveport, Louisiana warehouse serves as the model for the future: it operates with 25% fewer workers and about 1,000 robots.
• A new facility in Virginia Beach and retrofitted older ones like Stone Mountain, Georgia, are following this design, which may shift employment toward more temporary and technical roles.
• The company is instructing staff to use softer language—such as “advanced technology” or “cobots” (collaborative robots)—instead of terms like “AI” or “robots,” to ease concerns about job loss.
• Amazon has begun planning community outreach initiatives (parades, local events) to offset the reputational risks of large-scale automation.
• The company has denied that the documents represent official policy, claiming they reflect the views of one internal group, and emphasized ongoing seasonal hiring (250,000 roles for holidays).
• Analysts suggest this plan could serve as a blueprint for other major employers, including Walmart and UPS, potentially reshaping U.S. blue‑collar job markets.
• The automation push continues a trajectory started with Amazon’s $775 million acquisition of Kiva Systems in 2012, which introduced mobile warehouse robots that revolutionized internal logistics.
• Recent innovations include robots like Blue Jay, Vulcan, and Proteus, aimed at performing tasks such as sorting, picking, and packaging with minimal human oversight.
• Long-term, Amazon may require fewer warehouse workers but more robot technicians and engineers, signaling a broader shift in labor type rather than total employment.

Real inherent issue using AI for learning.

Training data sets are critical to good results

General assistance Large Language Models - work on "patterns and predictions - what is most statistically likely to come next, not what is optimal"-------Lack of true understanding is a real issue!

"Use Work-Organizers" advertisement, Bookseller & Stationer and Office Equipment Journal, Toronto, October 1920, Vol XXXVI, No. 10, p70.

https://www.google.com/books/edition/Bookseller_Stationer_and_Office_Equipmen/toZQAAAAYAAJ?hl=en&gbpv=1&dq=dictaform%20meilicke&pg=RA9-PA70&printsec=frontcover

Photo of a work organizer for indexing/filing on both a desktop as well as within the desk drawer.

SB22-140 Work-Based Learning Quality Expectations

Exposure, networking and mentorship...these are three huge opportunities for post-secondary institutions to strategically embrace. These are key indicators of success outcomes and higher ed has a competitive advantage, especially in-person and residential campuses.

for - youtube - Radical Platonism and Radical Empiricism - Michael Levin's work

Joy, Bill. “Why the Future Doesn’t Need Us.” Wired, April 1, 2000. https://www.wired.com/2000/04/joy-2/.

Annotation url: urn:x-pdf:753822a812c861180bef23232a806ec0

Annotations: https://jonudell.info/h/facet/?user=chrisaldrich&url=urn%3Ax-pdf%3A753822a812c861180bef23232a806ec0&max=100&exactTagSearch=true&expanded=true

Reprints available at: - Joy, Bill. “Why the Future Doesn’t Need Us.” 2000. AAAS Science and Technology Policy Yearbook 2001, edited by Albert H. Teich et al., Amer Assn for the Advancement of Science, 2002, pp. 47–75. Google Books, https://www.google.com/books/edition/Integrity_in_Scientific_Research/0X-1g8YElcsC.<br /> - Joy, Bill. “Why the Future Doesn’t Need Us.” 2000. Emerging Technologies: Ethics, Law and Governance, by Gary E. Marchant and Wendell Wallach, edited by Gary E. Marchant and Wendell Wallach, 1st ed., Routledge, 2020, pp. 65–71.

for - Q❓- how can your work help people lead better lives?

for - MOSAD and CIA - metaphor - work together

Porn is misattributed to the source of violence against women for the violence existed LONG before

society thinks women are only good for sexo

Argues that pornography is prostitution

for - Benedictine monks - work ethics - Latin - pray and work - perspective that treats work as sacred - advocated by the Benedictine monks - work - as sacred - The reasoning of founder St. Bernard was, that since they owed everything to God, - they had to pray as much as they could, and to achieve this and be self-sufficient, they also had to work

for - history - work - as moral slavery

for - question - ALG - language training - does it work in an online context?

for - progress trap - AGI - one year away from automating software work

for - program event selection - 2025 - April 2 - 10:30am-12pm GMT - Skoll World Forum - From Inner Work to Global Impact - Stop Reset Go Deep Humanity / cosmolocal - LCE - relevant to - event time conflict - with Building Citizen-Led Movements - solution - watch one live and the other recorded

meeting notes - see below

ANNIKA: - inner work helps us stay sane dealing with the chaos in our work - healing is not fixing - hope is a muscle, go to the "hope gym" - not just personal but collective

EDWIN: - inner WORK - constant, continuous work - how do you scale these things? Is it wrong term to use? Mechanistic? - how do we move to global impact? We don't know yet

LOUISE - inner work saved my - orientate inside away from trauma architecture - colonized and colonizer energies - they longed to be in union - be with all parts of myself - allow alchemy on the outside to the inside - liberate myself from my trauma structures and unfold myself - we cannot be a restorer unless we do that inner work - systeming - verbalizing / articulating it - we are all actors in creating the system - question - where am i systeming from? - answer - I am an interbeing - Am i systeming from the interbeing space or the trauma architecture space? - Where am I seeding from? What energy do I put into my work? - system is not concrete and fixed but fluid - fielding - bringing different human fields together - I can work with hatred and rage on the inside and transmute it so that I don't add to it on the outside

JOHN: - stuck systems and lens of trauma can help us get unstock - 70% of people have experienced trauma - trauma is part of the human experience - people make up systems - so traumatized people makes traumatized systems - fight, flight and freeze happens at both levels - at system level, its fractally similiar - disembodied from wisdom - in state of survival and fear - fixing things - until we deal with the trauma in the people, we will continue to have traumatized systems - More work won't help if it's coming from traumatized people

EDWIN - incremental change - something holding us back - built upon these traumas - Economic metrics are out of touch with how the trauma affects systems - Journey - awareness first, then understanding and inner transformation and finally change - Discussion with funders - most are still stuck in old paradigm of metrics, audits, etc - this comes with trauma because we have no trust on who is on the other side - a big part of the system is built on mistrust, creates more gaps between us - need to become anti-fragile

ANNIKA - Funders have lack of trust because inner work hasn't been done on both sides - As a funder, we really try to create a space of trust - Think of the language we use to be inclusive - How do we make inner work a part of the operating system of how we work? - We looked at 500 mental health organizations over the years - It's so urgent now that we align our work

EDWIN - We have a lot of half-formed thoughts - It's very complex and nobody has cracked it - We have a phrase at Axum that we move at the speed of trust - To do something different, they need to trust you - When I think of the discussions I've had with heads of states and CEOs, these meaningful inner ideas are not often brought up

LAURA - When there's no trust, even if there is no danger, the trauma is still brought up - We need to shift our lens on trauma and become aware of when trauma emerges - quote - inner condition of the intervener determines the success of intervention - Bill O'Brien

LOUISE - I work a lot with nervous system and body system - We need small changes in our nervous system - If I try to do something big, I can re-traumatize myself - We also have a collective nervous system - Restore love to all parts of your system first - Make friends with trees to seed actions from union

JOHN - Become aware of my own trauma triggers - When we see an outsized reaction, we can guess that person is undergoing personal trauma - A settled body settles bodies - If we are calm, it helps calm others

LAURA - Feel where we don't feel grounded, where we shame ourselves, feel compassion there

QUESTIONS - See below

• mushrooms and ayuahuasca - is it helpful?

• A lot of women forget the feminine energy to climb the ladder and get sick?

• backlash - feels like white men were being pushed to do work they weren't ready to do so now reclaiming their comfortable traumatized space

• how early do we start to teach this knowledge?

• How do organizations hold space for the enormous trauma that the US govt is manufacturing. We need to build this practice into organizations to help deal with the onslaight

• Youth are so hungry for being in the presence of others who are wise, compassionate. We can't move faster than the speed of trust but it needs to become accessible.

ANSWERS - See below

LOUISE - Organizations have a huge role to play at this time - We want to reconfigure and transform the trauma - Deep forming teams in organizations to help transform - Trauma fields want to come through human nervous systems to transform - We are both feminine and masculine and the masculine wounding is very important and needs to find the feminine - We cannot go away by ourselves to heal from patriarchy, colonialism energies

ANNIKA - In terms of how we fund, can we fund differently? We need to fund these spaces

EDWIN - I sit on board of Wellbeing project - changemakers go through burnout - how do we prevent this and create a container that can sustain them? - Weve brought 20,000 people in summits who have affected 3 million people. Please come to the Hurts summit in Czec and Wellbeing project - When pendulum swings back from individual space, we should be like a spiral

JOHN - In systems change spaces, trauma is seldom spoken of. - Systems work will not work if we ignore trauma - This is critical

LOUISE - Arundhati Roy - Another world is not only possible but is on its way. On a quiet day, I can hear it breathing.

via Fritz Saxl, The History of Warburg’s Library (1943/1944), p. 329.

Where does the work reside? Goes to the idea of zettelkasten coherence.

See: https://boffosocko.com/2024/01/11/on-cohesion-and-coherence-of-the-zettelkasten-where-does-the-work-reside/

Reply to Gertina Blanket on LinkedIn:

Jij legt in één klap uit datgene wat ik nooit goed heb begrepen uit de literatuur... Het verschil tussen interleaving en varied practice (die vaak als hetzelfde worden gebruikt in de "volksmond").

Het een gaat over verschillende hoeken kijken naar hetzelfde idee (varied practice) terwijl het ander gaat over verschillende maar soortgelijke ideëen (interleaving), bijvoorbeeld meerdere soorten wiskunde (algebra, trigonometrie, etc.).

Hierbij wil ik uiteraard wel zeggen dat blocked practice niet per se direct toegepast moet worden als het over automatisering gaat -- de cognitieve schemata moeten eerst goed gevormd zijn. Zie ook 4C/ID (Ten Steps to Complex Learning). Ofwel, eerst goede encoding + retrieval (Spaced Interleaved Retrieval, mindmapping, etc.) en dan focus op "drilling" / knowledge fluency.

Het sneller maken / automatiseren heeft geen enkel nut als het begrip er nog niet goed in zit. Dit moet geverifiëerd worden.

Kennis is natuurlijk ook erg interdisciplinair. Ik wordt er extreem blij van als ik een link leg tussen een boek over filosofie en efficiënt leren/onderwijs bijvoorbeeld.

Zo las ik ooit een boek over romeinse oratoren met een misleidende titel "How to Win an Argument" van Marcus Tullius Cicero, vertaald door James M. May, en hierin kwam ik tegen dat de oude Romeinen al door hadden dat LOGICA is wat het brein doet onthouden, en dit hoeft dus geen objective logica te zijn maar meer een correcte reflectie van hoe je eigen geest werkt en verbanden legt.

Dit is direct in lijn met wat ik weet van cognitieve leerpsychologie en mijn klein beetje kennis van neurowetenschap (waar ik dit jaar dieper in wil duiken).

Informatie in isolatie is nooit stevig, het moet zich vastklampen aan ankers en andere kennis (voorkennis eventueel), en de lerende (niet de onderwijzende) moet actief bezig zijn om deze verbanden te leggen.

Zoals ik wel vaker quote van Dr. Sönke Ahrens: "The one who does the effort does the learning."

Als ik een boek lees denk ik automatisch aan hoe ik dit kan relateren aan wat al in mijn second mind (Zettelkasten) zit. Ik denk niet meer linear, alleen maar non-linear. Standaard in verbanden.

Hier wat bronnen (impliciet) genoemd: - Cicero, M. T. (2016). How to win an argument: An ancient guide to the art of persuasion (J. M. May, Trans.). Princeton University Press. - Ahrens, S. (2017). How to take smart notes: One simple technique to boost writing, learning and thinking: for students, academics and nonfiction book writers. CreateSpace. - fast, sascha. (100 C.E., 45:02). English Translation of All Notes on Zettelkasten by Luhmann. Zettelkasten Method. https://zettelkasten.de/posts/luhmanns-zettel-translated/ - Luhmann, N. (1981a). Communicating with Slip Boxes (M. Kuehn, Trans.). 11. - Luhmann, N. (1981b). Kommunikation mit Zettelkästen. In H. Baier, H. M. Kepplinger, & K. Reumann (Eds.), Öffentliche Meinung und sozialer Wandel / Public Opinion and Social Change (pp. 222–228). VS Verlag für Sozialwissenschaften. https://doi.org/10.1007/978-3-322-87749-9_19 - Moeller, H.-G. (2012). The radical Luhmann. Columbia University Press. - Scheper, S. (2022). Antinet Zettelkasten: A Knowledge System That Will Turn You Into a Prolific Reader, Researcher and Writer. Greenlamp, LLC.

• Schmidt, J. F. K. (2016). Niklas Luhmann’s Card Index: Thinking Tool, Communication Partner, Publication Machine. In Forgetting Machines: Knowledge Management Evolution in Early Modern Europe (pp. 287–311). Brill. https://doi.org/10.1163/9789004325258_014
• Schmidt, J. F. K. (2018). Niklas Luhmann’s Card Index: The Fabrication of Serendipity. Sociologica, 12(1), Article 1. https://doi.org/10.6092/issn.1971-8853/8350

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