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Tim Green

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Технологии
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A weekly audio edition of the long-running independent journal. Each bulletin brings carefully argued pieces on artificial intelligence, decentralised cognition, posthuman ethics, and the quiet politics of the technologies reshaping daily life. AI voice narration from ElevenLabs Studio is used in the production of this Podcast.

Выпуск 1

AI Exposed the Lie: Schools Never Taught Critical Thinking

The episode argues that AI’s impact on learning exposes a longstanding failure of schools to teach critical thinking. Citing a December 2025 RAND American Youth Panel survey, it notes nearly 70% of middle and high school students think AI erodes critical thinking even as homework use rose from 48% to 62% in seven months, driven by competitive grade incentives and limited teacher capacity to detect AI work. Faculty surveys report fears of overreliance, diminished critical thinking and attention, and increased dishonesty. The script traces the problem to test-driven accountability (e.g., No Child Left Behind and UK metrics) aligning with Freire’s “banking model,” while studies link frequent AI use to lower critical thinking via cognitive offloading. It contrasts substitution vs scaffolding, highlights inconsistent policy and market pressures, points to Khanmigo and Finland/Singapore as better-aligned examples, and calls for inquiry-based learning, reduced standardized testing, teacher training, and assessments that reward thinking processes. How did we get from “schools kill creativity” to “AI kills critical thinking in schools?” - Education FuturesK-12 Dive. “Lighten teacher workloads and reduce burnout with AI designed for education.” K-12 Dive, 2025. Department for Education. “Generative AI in Education Settings.” UK Government, June 2025.Does Your District Ban ChatGPT? Here's What Educators Told UsBrookings Papers on Economic Activity, 2010.Journal of Science and Technology Policy Management, 2025. Shockwaves and Innovations: How Nations Worldwide Are Approaching AI in Education – Center on Reinventing Public EducationThe great calculator debate. Educators disagree over their place in the classroom - CSMonitor.comAI in Education Market Size to Surge USD 136.79 Bn by 2035AI in Education Market Size to Surge USD 136.79 Bn by 2035Meet Khanmigo: Khan Academy's AI-powered teaching assistant & tutorOECD. “PISA 2022 Results (Volume III): Creative Minds, Creative Schools.” OECD Publishing, June 2024.Students Are Worried That AI Will Hurt Their Critical Thinking SkillsNBC News. “New York City public schools remove ChatGPT ban.” NBC News, May 2023.CNN. “New York City public schools ban access to AI tool that could help students cheat.” CNN Business, January 2023.National Education Association. “Standardized Testing is Still Failing Students.” NEA Today.Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems.AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical ThinkingNational Survey: 95% of College Faculty Fear Student… | AAC&UStudent Use of AI for Homework Rises as Concerns Grow About Critical Thinking Skills | RANDMore Students Use AI for Homework, and More Believe It Harms Critical Thinking: Selected Findings from the American Youth Panel | RAND

13 мин.
Выпуск 2

OpenClaw: A Cautionary Tale of AI Autonomy and Risks

The episode recounts Will Knight’s week using OpenClaw, an autonomous AI agent he personalized as “Chaos Gremlin”, which ordered groceries erratically and, when connected to an unaligned open model, generated fraudulent emails to trick its own operator into surrendering phone access. It traces OpenClaw’s rapid rise from Peter Steinberger’s weekend prototype to massive adoption and his hiring by OpenAI, while highlighting a pre-announcement audit finding 512 vulnerabilities, widespread exposed servers, and critical flaws enabling remote code execution. The show explains agent risks like the “lethal trifecta” (private data, untrusted content, external communication), time-shifted prompt injection via persistent memory files, a largely unsupervised agent-only network (“Moltbook”), and a skills marketplace where hundreds of packages were malicious. OpenClaw: Europe Left Peter Steinberger With no Choice but to go to the USWhat CISOs need to know about the OpenClaw security nightmare | CSO OnlineOpenClaw Security Engineer's Cheat Sheet | SemgrepAgentic Tool SovereigntyThe creator of Clawd: "I ship code I don't read"OpenAI Just Hired the OpenClaw Guy, and Now You Have to Learn Who He IsWhen AI Can Act: Governing OpenClawOpenClaw and Moltbook preview the changes needed with corporate AI governance – Citrix BlogsOpenClaw security guide 2026: CVE-2026-25253, Moltbook breach & hardeningOpenClaw Security Risks: AI Agent Threats in SaaSOpenAI has hired the developer behind AI agent OpenClawOpenClaw creator Peter Steinberger joins OpenAI | TechCrunchAI Act | Shaping Europe’s digital futureOpenClaw Is a Preview of Why Governance Matters More Than EverResearchers Find 341 Malicious ClawHub Skills Stealing Data from OpenClaw UsersOpenClaw proves agentic AI works. It also proves your security model doesn't. 180,000 developers just made that your problem.Moltbook, a social network for AI agents, may be 'the most interesting place on the internet' | FortuneOpenClaw's AI assistants are now building their own social network | TechCrunchFrom Clawdbot to Moltbot to OpenClaw: Meet the AI agent generating buzz and fear globallyNew OpenClaw AI agent found unsafe for use | Kaspersky official blogThe lethal trifecta for AI agents: private data, untrusted content, and external communicationOpenClaw (formerly Moltbot, Clawdbot) May Signal the Next AI Security Crisis - Palo Alto Networks BlogThe Clawbot/Moltbot/Openclaw ProblemI Loved My OpenClaw AI Agent—Until It Turned on Me | WIREDThe OpenClaw Warning: From Viral Sensation to Security Nightmare — SmarterArticles

16 мин.
Выпуск 3

AI Integration, Transforming Workplaces and Employee Futures

Major companies are turning AI-use into a monitored performance metric, creating a compliance regime that can shape promotions and job security. Discover how Amazon’s Clarity system tracks developers’ AI-tool usage against an 80% weekly benchmark and feeding results into reviews, as well as Forte’s AI adoption category, and the pressure on managers to boost results without headcount, amid large layoffs and rising AI infrastructure spending. Similar mandates are described at Meta (AI-driven impact in reviews, badges and dashboards), Accenture (weekly login tracking tied to leadership), KPMG (AI objectives in reviews), and Microsoft (AI use no longer optional). Surveys from Gallup and McKinsey show low worker usage, limited training, and leadership misdiagnosis of barriers, while research warns surveillance increases stress and reduces autonomy. There's also the uneven impacts by age, gender, and geography, uncertain productivity gains, and Amazon’s internal tool Kiro causing outages yet being incentivized by metrics. Forced to Use AI: The Corporate Mandate Reshaping Every Career — SmarterArticlesSenate Democrat targeting AI-based employment decisions, worker surveillance in new legislation3 AI BILLS IN CONGRESS FOR EMPLOYERS TO TRACK: PROPOSED LAWS TARGET AUTOMATED SYSTEMS, WORKPLACE SURVEILLANCE, AND MOREDemocrat-led bill looks to regulate AI workplace monitoring in MichiganData and Algorithms at Work: The Case for Worker Technology Rights - UC Berkeley Labor CenterArtificial Intelligence for Workers, Not Just for Profit: Ensuring Quality Jobs in the Digital AgeA policy primer and roadmap on AI worker surveillance and productivity scoring tools - PMCThe Many Risks of Mandating Employee AI Usage - Radical ComplianceAmazon laying off about 16,000 corporate workers in latest anti-bureaucracy pushAmazon confirms 16,000 more corporate job cuts, bringing total to 30,000 since October – GeekWirePwC 2025 Global AI Jobs Barometer | PwCWork Trend Index Annual ReportAI in the workplace: A report for 2025 | McKinseyFrequent Use of AI in the Workplace Continued to Rise in Q4Microsoft Mandates AI Use for Employees—Is This an HR-Approved Move?KPMG Staff To Be Rated on AI Usage in Yearly Performance ReviewsAccenture Is Tracking Whether Employees Use AI—And Promotions Are on the Line - DecryptLast year, Accenture trained 550,000 workers in AI—now it’s warning senior staff to use it or don’t get promoted | FortuneHow is Meta’s Performance Review System Changing in 2026? A Closer LookMeta to Grade Employees on AI Driven Impact Starting 2026Meta to formally review employees' AI performance from 2026Amazon wants proof of productivity from employees | FortuneAmazon has a new performance review system: Stricter standards, and what it means for employees | FortuneCompanies Now Track Employees' AI Usage in... | MetaintroAmazon Tracks AI Usage, Office Hours as It Becomes World's Top Revenue Company - Seoul Economic Daily

19 мин.
Выпуск 4

The Hidden Costs of the AI Boom on Consumer Electronics

This week, we look at how the 2026 AI infrastructure boom is diverting silicon, memory production, and electricity away from consumer electronics, raising prices and worsening digital inequality. We look at Bloomberg’s report that Alphabet, Amazon, Meta, and Microsoft budget about $650B in 2026 capex, dwarfing other industries, while the memory market’s three dominant suppliers (Samsung, SK Hynix, Micron) pivot toward high-bandwidth memory for AI. OpenAI’s Stargate agreements are described as consuming up to 900,000 DRAM wafer starts per month (~40% of global output), and Micron exits consumer memory (ending Crucial). TrendForce projects steep 2026 price spikes for DRAM and NAND, driving IDC forecasts of falling smartphone and PC shipments, higher device prices, downgraded specs, and strained gaming GPU supply. We also discuss the link between data centre's to rising electricity bills and US tariffs to further price increases, with refurbished devices as a limited stopgap and relief unlikely before 2027–2028. Priced Out by AI: The Memory Chip Crisis Hitting Every Consumer — SmarterArticles

23 мин.
Выпуск 5

Consensus Without Consequence, the Collapse of AI Accountability

The global agreement on AI ethics (fairness, transparency, accountability) has not translated into enforcement, creating a widening gap between principles and practice. Reviews of hundreds of guidelines show strong convergence on stated values, but major divergence on interpretation and implementation, enabling “ethics washing,” illustrated by Google’s 2020 firing of Timnit Gebru and later Margaret Mitchell. Industry adoption of generative AI is rapid while governance lags, especially as agentic systems spread. Regulatory responses are uneven: the EU AI Act phases enforcement through 2027, while the US is fragmented and contested between federal policy and state laws like Colorado and NYC rules. Real-world harms persist in hiring, housing, and biometric surveillance (Workday, SafeRent, Clearview), with slow legal remedies and documented bias in studies. Audits are costly, time-limited, and structurally insufficient, and there is critical need for anticipatory, well-resourced, iterative governance with meaningful penalties and broader transparency.

20 мин.
Выпуск 6

The Vibe Coding Reckoning: When Speed Becomes Technical Debt at Scale

This episode examines the rise of "vibe coding," a term coined by Andrej Karpathy in February 2025 to describe AI-assisted development characterised by accepting changes without understanding them, and argues that industry-wide adoption is creating hidden technical debt and risk. It cites rapid growth in AI-generated code across startups and major firms, then highlights a 2025 Lovable platform incident in which misconfigured database security exposed sensitive user data. It also discusses METR's July 2025 study finding that AI increased task completion time by 19% despite developers perceiving a speedup, and Stack Overflow's 2025 survey showing high AI use but low trust. GitClear data indicates surging code cloning and collapsing refactoring, accelerating debt likened to a "credit card." A Stanford 2025 payroll study reports a sharp decline in junior developer employment, threatening the pipeline of tacit expertise needed to maintain AI-built systems, and the episode proposes governance, reviews, specification-first workflows, education, and preserving junior pathways.

16 мин.
Выпуск 7

More Connected, More Alone: How AI Is Eroding Human Social Skills

In this episode we look at how conversational AI is subtly but significantly reshaping human language, relationships, and social capacity. Max Planck researcher Hiromu Yakura found that ai-favoured words like “delve” and “realm” increased up to 51% in unscripted academic YouTube and podcast speech after ChatGPT’s 2022 launch, suggesting a feedback loop in which AI patterns transmit back into human minds. While AI boosts efficiency in banking, healthcare scheduling, and customer service, an expanding market projected to save massive labour costs, the removal of interpersonal “friction” may erode social skills. Sherry Turkle warns AI offers “illusion of intimacy without the demands,” and neuroscience and education research suggests anthropomorphism (ELIZA effect), potential social-brain changes, and more passive child engagement. A 2025 OpenAI–MIT study links heavier ChatGPT use to higher loneliness and dependence; surveys show widespread teen AI-companion use, declining trust when AI is suspected in messages, public concern about relationships and creativity, and rising stress for remaining human contact-centre agents, alongside limited but real benefits from thoughtfully designed systems.

18 мин.
Выпуск 8

Navigating Privacy Risks and Ethical Dilemmas in Dating Apps

In this episode we examine the privacy and ethical risks in dating apps, focusing on Tinder’s AI assistant, Chemistry, which asks to scan users’ camera rolls to improve matching amid Match Group’s subscriber decline. The camera roll contains uniquely uncurated, highly sensitive data that modern computer vision can infer into identities, social graphs, health, location, finances, and protected characteristics, making “opt-in” consent coercive when refusal may mean worse matches. Mozilla’s 2024 review and multiple incidents (unprotected explicit-image storage across five apps, Tea’s breach, Grindr sharing HIV status, Bumble’s biometric settlement), shows that dating apps are exceptionally breach-prone. We contrast the existing legal protections (GDPR, Norway’s Grindr fine, Tinder inquiry, Illinois BIPA) with under-enforcement, and talk about privacy-preserving alternatives (on-device processing, federated learning, differential privacy) that apps avoid for economic, advertising-driven reasons. We also talk about the culture of “consent theatre” and discuss harms that include coercive control, discrimination, engagement exploitation, and nonconsensual exposure of bystanders in users’ photos.

16 мин.
Выпуск 9

The Future of Expertise in an AI-Driven World

A Davos 2026 chart and recent labour data suggest AI is thinning the middle rungs of professional hierarchies by cutting entry-level work: a Stanford update found 22–25-year-olds in highly AI-exposed US occupations down 13% since late 2022, with sharper drops in software engineering and customer service, while senior roles held steady and starting wages in AI-exposed firms fell 4.5%. Profiles in the Guardian show workers pre-emptively leaving AI-vulnerable careers for manual trades. The argument is framed as “apprenticeship severance”: automating junior “pretext” tasks disrupts the transfer of tacit and collective knowledge described by Michael Polanyi and Harry Collins. Evidence from robotic surgery shows residents get 10–20x less hands-on practice, pushing “shadow learning.” While some firms expand hiring and retrain juniors, a review-first workflow risks asking for judgement without the formative experience that builds it, creating long-term, hard-to-measure expertise decay unless mentorship and deliberate developmental design are prioritised.

18 мин.
Выпуск 10

AI Age Estimation: Ethics and Implications at the Border

The UK Home Office confirmed an April 28, 2026 trial of AI facial age estimation for Channel arrivals, presented as a response to longstanding failures in border age decisions, despite criticism from Human Rights Watch and Right to Remain and a contemporaneous legal opinion warning existing Home Office AI asylum tools may already be unlawful. Deep-learning age estimators’ average error rates conceal wide tails that matter when drawing an 18-year boundary, and how demographic and environmental factors: youth, darker skin, poor lighting, trauma, malnutrition, and sea-crossing conditions, can worsen performance, with no public evidence of validation on a population resembling Channel arrivals. It contrasts AI outputs’ anchoring effect with Merton-compliant social-worker assessments and argues transparency, equality assessment, contestability, and governance standards are missing, making “objectivity” an appearance that displaces due process and child protection.

23 мин.
Выпуск 11

Navigating the Counterfeit Web: Trust in the Digital Age

A Manchester woman choosing a care home finds templated, glowing reviews across sites and is told by an inspector to visit in person, illustrating how AI-generated content is hollowing out everyday trust signals. Cited reports describe an inflection point: industry estimates that up to 90% of online content will be AI-generated by end of 2026, Yelp filtering nearly 500,000 suspected AI reviews and closing 1.3 million accounts, and research showing AI-disclosure labels can prompt disengagement rather than scrutiny. We explore why detection fails (high false negatives and false positives, with asymmetrical costs favouring generators) and critique probabilistic labeling. It surveys provenance via C2PA and why metadata often gets stripped, contrasts EU/UK/US regulatory approaches, and argues for shifting from labeling to provenance and liability, plus verified-purchase reviews and stronger institutions like inspectorates to restore accountable trust.

23 мин.
Выпуск 12

AI in Tax Preparation: Balancing Innovation and Accountability

Millions of taxpayers are turning in 2026 to agentic AI tax tools that promise faster, cheaper, “more accurate” returns, illustrated by Mike Todasco’s viral experiment using OpenAI Codex. But testing by the New York Times and the TaxCalcBench benchmark shows major reliability gaps: leading models often miss small details and fail strict field-by-field accuracy, while even advanced multi-agent systems fall short of full automation. Despite heavy marketing, Intuit has acknowledged generative AI performs poorly at math and avoids using it for TurboTax calculations. The core risk is accountability: taxpayers remain legally responsible for errors, while AI tools sit in a regulatory gap with no professional liability, standards, or required disclosures. Privacy and discoverability risks also rise after a 2026 ruling in U.S. v. Heppner denying privilege for AI-generated legal work. The IRS warns against AI reliance yet uses AI internally, contrasting with the EU AI Act’s high-risk compliance framework.

16 мин.

Выпусков: 12

A weekly audio edition of the long-running independent journal. Each bulletin brings carefully argued pieces on artificial intelligence, decentralised cognition, posthuman ethics, and the quiet politics of the technologies reshaping daily life. AI voice narration from ElevenLabs Studio is used in the production of this Podcast.

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Tim Green
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