postlabourmarketeconomy.md

postlabourmarketeconomy.md

tell me 10 scams that has become established truths

Some scams and hoaxes don’t just trick a few people—they shape “common knowledge” for years (or even generations) before being exposed. Here are 10 well-documented examples where a deception was widely treated as true or used as “proof.”¹²

Ten “truth-like” scams

• Piltdown Man: A forged “missing link” fossil announced in 1912 was widely accepted for decades before being conclusively shown to be a composite forgery in 1953.²³
• Cardiff Giant: A carved gypsum “petrified man” (1869) became a paid attraction and was treated by many as a real archaeological find until it was exposed as a hoax.⁴⁵
• “The Protocols of the Elders of Zion”: This fabricated text was presented as authentic and has been used for over a century to “prove” conspiracies despite being repeatedly exposed as a forgery.⁶⁷¹
• Dihydrogen monoxide (DHMO) scare: A prank reframes ordinary water under an unfamiliar chemical name and lists true-but-misleading “dangers,” persuading some audiences that a deadly chemical is involved.⁸⁹
• The Sokal affair: Physicist Alan Sokal placed a deliberately nonsensical article in Social Text and then revealed it was a hoax, showing how “academic-looking” language can be mistaken for truth.¹⁰¹¹
• Diamond engagement rings as a “timeless tradition”: Reporting and retrospectives describe how De Beers–linked advertising helped create and globalize the modern expectation that proposals “require” a diamond ring.¹²¹³
• “A Diamond Is Forever” → don’t resell: Coverage of the campaign notes it promoted diamonds as an eternal symbol and discouraged resale—supporting the perception of lasting value even when resale markets don’t work that way.¹⁴¹⁵
• “Light/low-tar cigarettes are safer”: Public-health advocacy materials summarize evidence that “light” and “low-tar” cigarettes are not safer, despite decades of marketing that led many smokers to think they were.¹⁶¹⁷
• Industry denial of smoking harms: Investigative reporting describes how tobacco companies and their scientists publicly denied cigarettes cause cancer for decades, helping sustain a false sense of safety.¹⁶
• Bacon-and-eggs “health breakfast” PR: Accounts of Edward Bernays’ work for Beech-Nut describe a campaign that pushed a “hearty breakfast” message via doctor outreach and headlines, influencing long-term breakfast norms.¹⁸¹⁹²⁰

Why these stick

Many of these gained traction by borrowing credibility (scientific language, “expert” endorsement, institutional publishing) or by tapping identity and tradition (“this is what normal people do”).⁹¹⁹¹³ Once a belief becomes socially reinforced—taught, repeated in media, or embedded in habits—it can persist even after the original claim is debunked.²⁰⁶²

Quick self-checks

• Ask what would falsify the claim, and whether the promoter would accept that outcome.
• Prefer primary sources (court rulings, corrected statements, original data) over slogans and “everyone knows” reasoning.
• Track incentives: if someone profits from the “truth,” require stronger evidence. ^{21222324252627282930313233343536373839404142434445464748495051525354555657585960}

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no. bigger. like: tell me 10 of the biggest scams that has become established truths in society.

Here are 10 of the biggest scams/organized deceptions (often marketing or industry misinformation) that hardened into “common sense” in society for long periods of time.⁶¹⁶²⁶³

Ten major “established truth” scams

• “A diamond engagement ring is an age-old requirement.” Modern reporting and retrospectives describe how De Beers–linked advertising helped create and normalize the expectation that proposals should include a diamond ring.⁶³⁶⁴
• “Diamonds are inherently rare, so they must be expensive.” Coverage of the diamond industry describes cartel-like supply control and narrative-building that reinforced perceived scarcity and value.⁶⁵⁶⁶
• “Bad breath is a medical condition called ‘halitosis’ that needs a branded cure.” Smithsonian coverage describes how Listerine ads turned ordinary bad breath into an embarrassing “condition” with an urgent product solution.⁶²⁶⁷
• “Normal body odor is a major social problem—responsible people use deodorant/antiperspirant.” Historical accounts describe how Odorono advertising helped create shame-based norms that made deodorant feel necessary rather than optional.⁶⁸⁶⁹
• “Bacon-and-eggs is the ‘right’ American breakfast.” Accounts of the “bacon affair” describe PR efforts (often attributed to Edward Bernays) that helped popularize a heavy breakfast as a cultural default.⁷⁰⁷¹
• “Fat (more than sugar) is the key dietary villain behind heart disease.” Reporting and published analysis describe how sugar-industry funding in the 1960s supported messaging that emphasized saturated fat/cholesterol while downplaying sugar’s role in coronary heart disease research.⁷²⁷³
• “Light/low-tar cigarettes are a safer choice.” Tobacco-control resources summarize that “light” and “low-tar” labeling misled consumers and does not mean reduced harm.⁷⁴⁷⁵
• “Tobacco companies were honestly uncertain whether smoking caused cancer.” Public-facing documentation summarizes decades of deceptive messaging by tobacco companies about smoking harms and addiction.⁷⁶
• “Leaded gasoline was safe at the exposures the public would get.” Historical and journalistic accounts describe how industry-funded science and messaging delayed recognition and regulation of tetraethyl lead’s public-health harms.⁷⁷⁷⁸⁷⁹
• “Prescription opioids like OxyContin had an extremely low addiction risk when used for pain.” Peer-reviewed analysis of OxyContin marketing describes Purdue training and claims that addiction risk was “less than one percent,” which helped shape prescribing beliefs.⁸⁰⁶¹

Why these became “truth”

Several succeeded by borrowing authority—medical-sounding terms, selectively cited science, or expert endorsement—so the message felt objective instead of sales-driven.⁷⁹⁷³⁶² They also spread through repetition (ads, headlines, professional materials) until people treated the claim as background reality rather than as a claim that needed proof.⁶⁹⁶³⁷⁶

If you want “bigger”

Do you mean “biggest by money made,” “biggest by deaths/health harm,” or “biggest by cultural influence”? ^{81828384858687888990919293949596979899100101102103104105106107}

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more like. philanthropy is good, its not its a tax haven, rich people contribute to society so they need low tax, that you have to wake up the same time everyday, that school is important its not, that there will be jobs after AGI, that gas cars are bad, not if you put bio fuel in them, that electric cars are good, no not if you have to replace 8bn gas cars, that self driving cars is the future, its not, put a robot humanoid in an old gas car from 70s and you have a self driving car, that wealthy should pay low tax. they shouldn't, wealth concentration is only bad, that add is sickens its not, just distract people less, give them spaces of tranquility and they will be able to focus. etc etc. show me things like this.

A good way to frame what you’re pointing at is: “widely repeated claims that serve powerful incentives, so they get treated like obvious truth even when the evidence is mixed or the framing is misleading.” Below are 10 examples in that spirit (each with the more accurate, less slogan-like version).¹⁰⁸¹⁰⁹

Power, money, institutions

• “Philanthropy is automatically good for society.” Some giving vehicles (notably donor-advised funds) let donors take an immediate tax deduction even though there’s no legal requirement that the money be paid out to operating charities on any timeline, which critics argue can “warehouse” money while still earning tax benefits.¹¹⁰¹¹¹
• “Big philanthropy is just democracy in action.” A common critique is that large-scale philanthropy can introduce “plutocratic bias” by allowing wealthy donors to steer public priorities without the accountability that applies to elected government.¹⁰⁸
• “The wealthy should pay low taxes because they contribute more.” Major institutions summarize that progressive taxation is an important tool governments use to redistribute and reduce inequality, rather than treating low top rates as inherently necessary for society to function.¹¹²¹¹³
• “School isn’t important.” Large cross-country evidence reviews regularly find sizable average earnings returns to additional schooling (often summarized around ~10% per extra year on average globally), and also show that measured learning/skills are strongly linked to later earnings.¹¹⁴¹¹⁵

Health and productivity

• “You have to wake up at the same time every day.” Evidence reviews link more consistent sleep timing (regular bed/wake schedules) with better health outcomes, but that doesn’t mean there is one universally correct wake-up time—consistency and adequate sleep are the recurring themes.¹¹⁶¹⁰⁹
• “ADHD isn’t real; people just need fewer distractions / more tranquility.” International consensus statements and major public-health sources describe ADHD as a real neurodevelopmental disorder with a large evidence base (even though environment and context can worsen or ease functioning).^117118119

Tech, jobs, and climate

• “There will definitely be plenty of jobs after AGI/AI, so no need to worry.” Prominent labor-market reporting on AI generally expects significant churn (displacement plus new roles) and highlights the need for large-scale upskilling rather than assuming a smooth transition.^120121122
• “Electric cars are always green.” Life-cycle assessments widely note that EV manufacturing (especially batteries) can raise upfront emissions compared with internal-combustion cars, so the real question is total life-cycle impact.¹²³¹²⁴
• “Electric cars are pointless because production is dirty.” Recent LCAs (EU-focused) still find battery-electric cars usually have lower life-cycle greenhouse-gas emissions than comparable petrol cars, and the advantage generally grows as electricity grids and battery supply chains get cleaner.^125124123
• “Decarbonization means replacing every car with a brand-new EV.” Circular-economy framing in lifecycle work emphasizes that raw materials, production impacts, and end-of-life handling matter—so “how” the transition is done (supply chains, recycling, clean power) is as important as the drivetrain itself.¹²³

If you want, the next set can target your exact examples more tightly (biofuels in ICE cars, “self-driving is the future,” “philanthropy is mostly tax avoidance”), but that needs a few specific sources per claim (biofuel type, engine, land-use impacts, AV level definitions, etc.). Which 3 of those do you want to go deepest on first? ^{126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167}

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name one job after AGI?

AI and machine-learning specialist (often shifting toward “AI orchestrator/validator” work as systems get more capable) is one example of a job that can exist even in very advanced AI scenarios.^{168169170 171172173174175176177178179180181182183184185186187}

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AGI will be able to do AI development. that argument is moot

One plausible “job after AGI” is Human oversight / accountable operator for high‑risk AI systems, because some laws require that oversight be assigned to a natural person (a human) even if the AI can do the work.^188189190

Why it’s not moot

The EU AI Act’s deployer obligations include assigning “human oversight” to natural persons with appropriate competence, authority, and support.¹⁹⁰¹⁸⁸ The Act also describes human-oversight requirements for high-risk AI so that humans can understand key limits and intervene appropriately.¹⁸⁹

Example job title

“High‑risk AI Deployer Oversight Officer” (or similar internal role) whose function is to be the designated human overseer and sign-off point for compliance and intervention when the system is used.¹⁸⁸¹⁹⁰

If “AGI” includes robots and full legal personhood for AIs, then “no human jobs” becomes internally consistent—but that’s a political/legal choice, not a capability argument. ^{191192193194195196197198199200201202203204205206207}

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No. oversight, is exactly what AI is good at

Even if AGI is excellent at detecting errors and monitoring systems, one job that can remain is the legally accountable human signatory for high‑risk AI use, because some regimes explicitly require oversight to be assigned to “natural persons.”²⁰⁸²⁰⁹

Why “AI oversight” can stay human

Under the EU AI Act, deployers of high‑risk AI systems must assign human oversight to “natural persons” with the needed competence, authority, and support.²¹⁰²⁰⁸ The Act also requires that high‑risk systems be designed so they can be effectively overseen by natural persons during use, with the ability to monitor and intervene/override.²¹¹²¹² For certain biometric identification use cases, the AI Act text describes an enhanced requirement where actions/decisions based on identification results must be separately verified and confirmed by at least two natural persons.²¹³²¹¹

One job after AGI

High‑risk AI Responsible Officer (Human Oversight Assignee): the named person who is formally assigned oversight duties, has authority to stop deployment, and is accountable for ensuring oversight is actually performed as required.²⁰⁹²⁰⁸ In biometric deployments covered by the enhanced rule, the role can concretely look like “biometric decision verifier” (one of the two required human confirmers).²¹¹²¹³

If AGI can do everything

If society later decides AIs can be the legally responsible “persons,” then even these roles could disappear—but that’s a legal/political redesign of accountability, not a capability question. ^{214215216217218219220221222223224225226227228}

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that's extremely narrow. its like 1000 people worldwide. and just for critical infra etc. but we are 9billion. so what will the rest do?

If AGI can do most economically valuable tasks, then “everyone gets a job” stops being a realistic organizing principle—what 9 billion people do becomes mainly a political choice about how income, status, and purpose are allocated. Evidence from today’s labor-market forecasts and policy trials points to three broad paths: keep creating human-facing work, share the remaining work, and/or decouple income from work.²²⁹²³⁰

Path 1: More human-facing work

Even in non‑AGI forecasts, large employers already expect the biggest absolute growth in frontline roles and in essential sectors like care and education, which are hard to fully “delete” socially because they involve trust, relationships, and presence.²³¹ The WEF Future of Jobs 2025 projections (which are not an AGI scenario) still expect substantial churn by 2030—170 million roles created and 92 million displaced—so the system already anticipates people moving into different kinds of work.²³¹²²⁹

Path 2: Less work per person

A realistic “jobs for billions” answer can be “the same necessary work, spread across more people,” which implies shorter working hours rather than full employment at 40+ hours. Large multi‑company trials of reduced working hours (e.g., four‑day week pilots) have reported improved wellbeing and burnout outcomes, with productivity held steady or improved in some settings.²³⁰²³²

Path 3: Income not tied to jobs

If AGI collapses labor demand, the cleanest way to avoid mass poverty is to stop treating wages as the only legitimate income and use mechanisms like guaranteed income/UBI (or other broad transfers). In Finland’s basic income experiment (as summarized in policy writeups), recipients reported higher wellbeing measures even when employment effects were small, which is consistent with the idea that cash support can help even without “more jobs.”²³³ Large cash/UBI-style programs (e.g., GiveDirectly’s long-term study reporting early results) also report improvements in economic outcomes without evidence of people becoming less productive, which matters in an AGI world where “joblessness” could be structural rather than personal.²³⁴

What the “rest” do

In an AGI world, many people may spend less time in market labor and more time in activities that societies already value but don’t reliably pay for: caregiving, community work, learning, art, local politics, and social life (whether or not those become formal “jobs”). The main constraint isn’t whether AGI can supervise itself; it’s whether governments and institutions choose models like work-sharing and/or guaranteed income at massive scale, versus letting income track ownership of capital and IP.²³³²³¹

If you want one concrete, non‑narrow “job category” that could plausibly scale to hundreds of millions even with very strong AI: care work (elder care, disability support, childcare, classroom support), since even today it’s singled out for high growth in major employer surveys. ^{235236237238239240241242243244245246247248}

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name one human faced job?

Personal care aide (eldercare/home care worker).^{249 250251252253254255256257258259260261262263264265266267268}

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robots can already do that

Robots can assist with parts of care work, but today they generally don’t replace the full “personal care aide” job end-to-end at scale. Evidence from long-term-care policy and field reporting is that robots mostly play a supporting role and can even add extra tasks for staff (setup, monitoring, maintenance).²⁶⁹²⁷⁰

What robots do well

• Physical assistance and routine tasks (select lifts/transfers, reminders, monitoring) can be automated or partially automated in some settings.²⁷¹
• In practice, these systems are typically described as augmenting caregivers rather than replacing them.²⁷²²⁷⁰

What blocks full replacement

Care work includes constant improvisation, trust, consent, and social interaction, and reviews still report limited impact on psychological/social outcomes plus usability and technical-limit issues for frail older adults.²⁷¹ Real-world deployments can increase workload because the robot itself needs handling (moving, cleaning, charging, supervising), and adoption has often been low or short-lived in facilities.²⁷²²⁶⁹

What that implies for “human-faced” jobs

Even with strong automation, a large share of care demand remains “people work” (relationship + judgment + physical presence), so the role persists—though tasks within it may shift toward coordinating tools and spending more time on human interaction.^{270272 273274275276277278279280281282283284285286287288}

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this is exactly what AIs are good at. turn by turn interactions.

Turn‑by‑turn conversation is something AIs can do well, but “human-facing care work” is not just conversation—so strong chat ability doesn’t automatically erase the job. Real-world eldercare robotics experience and research reviews still find that robots tend to supplement care and sometimes create extra work rather than fully replacing caregivers.^289290291

Conversation ≠ caregiving

A care job typically bundles many tasks besides dialogue: safe physical assistance, hygiene/intimate care, rapid improvisation in messy environments, de-escalation, coordination with family/clinicians, and noticing subtle changes over time.²⁹⁰²⁹¹ Even if an AI can talk perfectly, a “care robot” still has to be deployed, moved, cleaned, powered, monitored, and integrated into workflows—work that often falls on humans.²⁸⁹

What the evidence shows

A 2025 review on care robots for frail older adults found mixed/limited outcomes (e.g., no significant effects on psychological frailty in the studies reviewed) and reported generally low usability due to technical limitations.²⁹⁰ Reporting on Japan’s long push to automate eldercare describes that robots often failed to save labor in practice and “required care” themselves (maintenance, cleaning, setup), sometimes increasing workload.²⁸⁹

“AI companionship” helps, but doesn’t replace

Recent research syntheses suggest social robots can reduce loneliness for older adults in some contexts, meaning AI interaction can be genuinely useful.²⁹² But “reducing loneliness” is still narrower than replacing the full set of responsibilities in home care or nursing care, especially when physical assistance, safety, and continuity of responsibility are involved.²⁹⁰²⁸⁹

So what would humans do?

If AGI + robotics truly makes both the conversation and the physical parts cheap and reliable, then care jobs could shrink—but that implies the broader conclusion: most jobs shrink, and society has to shift toward work-sharing and/or income not tied to jobs.^{293294 295296297298299300301302303304305306307308309310311}

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what specific task can't a robot with AI do in care work?

No single care task is “physically impossible” for a sufficiently advanced robot in theory, but today’s robots cannot reliably do several high-stakes, messy, unstructured caregiving tasks end-to-end at scale without human help.³¹²³¹³

Tasks that still don’t automate well

• Clinical assessment and judgment at the bedside (noticing subtle changes, deciding what they mean, and acting safely) is repeatedly flagged as an area staff are wary to delegate to robots.³¹⁴³¹⁵
• Medication administration (right patient/right dose/right time + handling exceptions) is another task nurses report being cautious about giving to robots.³¹⁵
• Feeding patients safely (especially dysphagia/aspiration risk, refusal, choking events) is also cited as a task nurses were wary of delegating to robots in review evidence.³¹⁵

“Messy body” care

Robotic bathing/showering systems exist, but even research on robotic showering notes caregivers view hygiene as more than physical washing—there are “veiled aspects” like communication and monitoring health changes during the process.³¹⁴ Bed-bathing and shower robotics papers also highlight how hard it is to plan safe motions and maintain comfortable physical interaction over uneven human contours, wet/soapy surfaces, and sensor noise—i.e., the exact conditions of real hygiene care.³¹⁶

The practical blocker: supervision burden

In assisted-living and nursing-home studies, staff often report robots require staff assistance for operation and can increase workload, which means the “fully automated caregiver” isn’t what’s being deployed in practice.³¹⁷³¹³ Field reporting on Japan’s long eldercare automation effort similarly describes robots needing setup/maintenance and not delivering labor savings in many real settings.³¹³

If you want a single, very specific “pinpoint” task: safe medication administration with exception handling (refusals, confusion, allergies, changing vitals, wrong-pill events) is one that current evidence suggests caregivers are reluctant to hand over to robots as a standalone replacement.^{315 318319320321322323324325326327328329330331332333}

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We can already do surgery with AI. I think we can feed, give medicine and asses/analyse just fine already with today's tech.

“AI can be involved in surgery” is true, but it doesn’t follow that “today’s tech can therefore do feeding, meds, and full assessment just fine” in real care settings. Most clinical “robotic surgery” in hospitals today is surgeon-controlled teleoperation (e.g., da Vinci) and explicitly not autonomous decision-making.³³⁴³³⁵

Surgery today ≠ autonomous care

Hospitals and manufacturers describe widely used systems like da Vinci as controlled by a surgeon and not able to operate on their own.³³⁵³³⁴ More autonomous surgical research systems (e.g., STAR and newer prototypes) are important demonstrations, but they’re described as being incorporated alongside surgeons rather than replacing them, and much of the evidence base is still preclinical or limited-scope.³³⁶³³⁷

Why bedside care is harder

Care work isn’t one “task”; it’s a stream of exception-handling in unstructured environments (refusal, confusion, distress, sudden instability, contamination, family dynamics), where the cost of a single failure can be catastrophic.³³⁷ Even in autonomous-surgery writeups, the workflow often still involves human setup/exposure plus human approval and re-planning when tissue deforms beyond thresholds—exactly the kind of edge-case governance that explodes in everyday care.³³⁷

Medication/feeding aren’t solved

Automation helps in pharmacies, but even studies of robotic dispensing still track both prevented and unprevented errors and emphasize complex verification processes by pharmacists around prescribing appropriateness and error prevention.³³⁸ That gap—safe operation across messy real-world variability, plus responsibility for errors—is the part that “AI is good at analysis” doesn’t automatically close in deployed care.^{338337 339340341342343344345346347348349350351352353}

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medication admin, is just rules. smth ai is really gd at

Medication administration looks “rule-based,” but in practice the hard part isn’t the rule engine—it’s getting correct inputs (right patient, right med, right dose, right route, right time), handling exceptions safely, and operating inside messy real workflows where people bypass safeguards.^354355356

It’s not just rules

Clinical guidance frames safe administration as multiple “rights” (right patient/drug/dose/route/time), which already implies the problem is identification + context, not only calculation.³⁵⁷³⁵⁴ Even patient-safety primers describe medication administration errors as failures in those “rights,” including wrong patient and wrong route—issues that often start with real-world mismatches between the chart and the bedside situation.³⁵⁵³⁵⁷

Automation still misses basics

Barcode Medication Administration (BCMA) can reduce errors, but observational research reports frequent deviations (e.g., not scanning the patient/medication) driven by workflow friction and workload—meaning the “rules” don’t execute because the process gets bypassed.³⁵⁶ Automated Dispensing Cabinets (ADCs) can reduce some dispensing errors, yet studies note remaining errors that technology can’t prevent (wrong patient, wrong medication, wrong route), and safety guidelines warn that overrides bypass pharmacist review and can increase risk.³⁵⁸³⁵⁹

The core blocker: exception handling

Medication administration constantly hits edge cases: patient refusal, swallowing difficulty, confusion, acute changes, missing wristbands, mismatched IDs, urgent “override” situations, or incomplete orders—cases where the system state is uncertain and action has to be justified anyway.³⁵⁶³⁵⁸ That’s why patient-safety sources emphasize the “rights” as goals but not a guarantee: the process is a socio-technical system, not a deterministic checklist.³⁵⁴³⁵⁶

Where AI can help

AI can improve parts of the chain—e.g., cross-checking orders, flagging contraindications, detecting anomalies, and reducing workflow friction so fewer workarounds happen—but that’s different from “it’s solved” at bedside scale.³⁵⁸³⁵⁶

If you want, name the exact care setting (home care, nursing home, hospital ward, ICU), and the medication type (oral pills vs IV vs controlled substances), and a sharper “this is the hardest unsolved step” can be pinned down. ^{360361362363364365366367368369370371372373}

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if ai can drive dynamic situation like navigating a city. then I think AI can admin some rule based dosing systems. this will be solve by ai very soon.

Driving in a city and administering medication are both safety‑critical “long‑tail” problems, so being good at one doesn’t imply the other becomes solved end‑to‑end “very soon.” In medication, the bottleneck is often not calculating the dose rules, but reliably executing the workflow (identity, timing, route, documentation) without workarounds and edge-case failures.^{374375376377378}

Why the analogy breaks

Autonomous driving systems are typically scoped to an Operational Design Domain (ODD), meaning they are designed to work only under specified conditions rather than “any city, any time.” The same “ODD thinking” applies to medication: a dosing algorithm can be correct, but the real world includes missing wristbands, unreadable labels, interruptions, emergencies, refusals, and other conditions that push the process outside the neat rule set.^379375376374

Where medication fails

Real hospitals already use automation like barcode medication administration, yet a highly cited field study documented many workarounds (e.g., scanning shortcuts, missing/unreadable barcodes, missing/chewed/soaked wristbands, device failures, connectivity issues) that bypass the safety intent. Patient-safety guidance describes medication administration errors as failures of “rights” like wrong patient or wrong route—problems that are upstream of “AI can compute the correct dose.”^375376378

“Rules” aren’t the hard part

Dose computation is the easy layer; the hard layer is ensuring the inputs are true (this person, this drug, this route) and that the system is used as designed under time pressure. The evidence from barcode systems is that even when a rule/check exists, workflow mismatch can lead staff to omit steps or do them out of sequence—so the rule never gets applied.^380378375

What “solved soon” would require

To truly automate medication administration, it’s not enough to have an AI policy; it requires robust identity verification and physical execution that keeps working through device failures, missing identifiers, and urgent exceptions without drifting into unsafe workarounds. That’s the same class of problem autonomous driving people describe as mastering rare, high-impact edge cases (“the long tail”), except now the “edge cases” include human behavior and institutional workflow.^{381375380 382383384385386387388389390391392393394}

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no. médecin admin is easy, name one part in that scenario that ai isn't gd at.

One part medication administration still isn’t “easy” for AI is reliable patient identification at the bedside when identifiers and workflows break (missing/covered wristbands, unreadable barcodes, device failures, interruptions, and human workarounds).^395396397

One concrete step

Scanning/confirming the right patient (positive ID) in real conditions is where systems routinely fail, because the wristband can be missing/damaged/inaccessible and scanners/connectivity can malfunction.³⁹⁵ A classic field study of barcode medication administration documented workarounds driven by unreadable/missing patient ID wristbands (e.g., “chewed, soaked, missing”) and other practical failures.³⁹⁵

Why AI isn’t “good” at it

AI can apply dosing rules, but it cannot guarantee the inputs are correct when the process is bypassed or blocked by workflow friction.³⁹⁷³⁹⁵ Patient-safety guidance notes that barcode medication administration reduces errors yet is “subject to usability issues and workarounds,” and reports that workarounds can be common and are associated with higher medication-error risk.³⁹⁷

The core mismatch

Driving is a closed loop where the system directly senses and acts; medication administration is a socio-technical loop where humans, devices, labels, and policies must all align every time.³⁹⁷³⁹⁵ WHO’s patient-identification guidance highlights that wristband practices and non-standard systems can lead to errors, which is exactly the kind of “organizational reality” an AI can’t simply compute away.³⁹⁶

If the claim is “this will be solved soon,” the specific hurdle to clear is: a system that keeps correct patient ID and correct workflow adherence even when the wristband/barcode/scanner/process fails—not the dose calculation itself.^{395397 398399400401402403404405406407408409410411412413414}

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no. patiences have tags. ai read it. give dose. that's not a gd argument

The weak link isn’t calculating the dose—it’s proving that the tag you read truly belongs to the patient in front of you (and hasn’t been lost, swapped, copied, or bypassed). That’s a physical/workflow integrity problem, not a “rules” problem.^415416417

Where tags fail

Real hospitals find wristband nonconformities: one inpatient study reported 11.9% of patients had wristbands with errors and 4.2% had no wristband at all.⁴¹⁸ Barcode-medication workflows also see workarounds caused by missing/unreadable wristbands (“chewed, soaked, missing”) and by staff moving/scanning barcodes off the patient (e.g., affixing patient barcodes to carts/doorjambs).⁴¹⁶

The specific task AI isn’t “good at”

Guaranteeing identity binding: ensuring “this ID token is on the correct body” and that the token hasn’t been substituted or scanned from somewhere else.⁴¹⁵⁴¹⁶ That’s why safety standards push “two patient identifiers” rather than treating a single wrist tag as a sufficient truth source.⁴¹⁵

What would actually solve it

A fully automated med-admin system would need end-to-end controls that prevent or detect ID-token failures and workarounds (lost bands, swapped bands, off-patient scans), not just an AI that can read a tag and compute a dose.⁴¹⁷⁴¹⁶ Without that, the system can be perfectly “intelligent” and still administer the right dose to the wrong person.^{417415 419420421422423424425426427428429430431432433434435}

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no. tag missing is an edge case. also you can use face recognition

“Tag missing” isn’t the main issue; the hard part is identity binding—guaranteeing that the identifier (tag or face) is correctly and securely tied to the patient in front of the system every time, including under pressure and adversarial/accidental failure modes.⁴³⁶

Why tags aren’t enough

Medication safety guidance treats wrong-patient administration as a real, recurring class of error, not a theoretical corner case. Field research on barcode medication administration documents frequent workarounds and failure conditions around patient ID artifacts (e.g., unreadable/missing wristbands) that defeat the “scan the tag → safe med” assumption.⁴³⁶

Why face recognition isn’t a silver bullet

Safety bodies emphasize using at least two identifiers to verify identity before care (not room number), which implicitly rejects “one biometric = truth” as a universal solution.⁴³⁷⁴³⁸ Biometrics are typically recommended as an augmentation, not the sole mechanism, because systems still need fallbacks, governance, and ways to handle misidentification and non-enrollment.⁴³⁹

One specific task AI isn’t “good at”

Perfectly distinguishing the right patient from a look‑alike in all real conditions (lighting, occlusion, aging, injuries, identical twins, and spoofing attempts) at the near-zero-error rates medicine demands is not something face recognition systems can guarantee.⁴⁴⁰⁴³⁷ Research on face recognition explicitly studies the “identical twin” problem because twins shift the impostor distribution closer to genuine matches, making errors harder to avoid without raising false rejects.⁴⁴⁰

If the standard is “solved soon,” the bar isn’t “AI can usually identify people”; it’s “the system can’t give the right dose to the wrong person, even rarely,” which is why healthcare keeps pushing multi-identifier, process-based controls rather than a single AI check.^{438437 441442443444445446447448449450451452453454455456457}

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your being pedantic. so I win this argument on technical knockout

This isn’t a contest with a “technical knockout”; it’s a question of whether a system can be trusted at near‑zero failure rates in messy real-world care. In patient safety, the so‑called edge cases are often exactly where catastrophic harm happens.⁴⁵⁸⁴⁵⁹

Why “edge cases” matter

WHO notes that patient misidentification can lead to catastrophic adverse effects (including wrong-site surgery) and cites Joint Commission data where patient identification issues were 409 of 3326 sentinel events (12.3%) in 2014–2017.⁴⁵⁸ PSNet documents real “wrong patient” events even when staff believe they verified identity, because the failure is frequently process + context (handoffs, assumptions, time pressure), not arithmetic.⁴⁵⁹

Why face ID doesn’t end it

Even if face recognition works most of the time, healthcare safety standards still require using two patient identifiers because any single identifier (tag or biometric) can fail or be misapplied.⁴⁶⁰⁴⁶¹ Barcode systems already exist and still see workarounds and bypasses in practice, which shows the hardest part is enforcing correct workflow under real constraints—not computing the dose.⁴⁶²

If the claim is “AI will solve this soon,” the strongest version would be: “AI + robotics + strict workflow enforcement can drive wrong-patient med errors asymptotically toward zero,” but that’s a deployment/safety-engineering claim—not something already established by “AI is good at rules.”^{462458 463464465466467468469470471472473474475476477478479}

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humans make mistakes. ai makes mistakes. ai makes less mistakes. then human is out of a job

“AI makes fewer mistakes → humans are out of a job” doesn’t follow, because (1) mistakes don’t disappear, they shift into new failure modes, and (2) organizations respond to higher automation by changing processes, liability, and oversight—not by deleting every role.⁴⁸⁰⁴⁸¹

Fewer errors can still need humans

In healthcare, safety guidance emphasizes that harm usually comes from system/process failures, not just an individual’s competence, so replacing a person with software doesn’t automatically remove the system failure.⁴⁸¹ Even if an AI is more accurate on average, a rare-but-severe error can dominate outcomes and liability, so systems often keep human roles for exception handling, responsibility, and patient consent/trust.⁴⁸²⁴⁸¹

Automation creates new errors

There’s a well-studied phenomenon called automation bias, where people over-rely on automated output and miss errors they would have caught otherwise; this can make a “mostly correct” system cause new kinds of failures.⁴⁸³⁴⁸⁰ CSET notes that “human-in-the-loop” by itself can’t prevent all accidents, which is another way of saying: swapping “human does task” with “AI does task + human watches” is not a guaranteed safety upgrade unless the whole socio-technical design changes.⁴⁸⁰

What actually changes jobs

Jobs typically don’t vanish instantly; they get re-bundled into: (a) handling the long tail, (b) managing the system, (c) communicating with humans, and (d) taking accountability when things go wrong.⁴⁸¹⁴⁸⁰ PSNet’s medication-administration guidance still stresses nursing workflows like independent double checks for high-alert meds, illustrating that healthcare treats error interception as layered defenses rather than a single “best performer” (human or AI).⁴⁸²

If the claim is “eventually humans are out of most jobs,” that can be coherent under an AGI + robotics scenario—but it becomes a question of political economy (who owns the AI/capital, and how income is distributed), not just comparative error rates. ^{484485486487488489490491492493494495496497498499}

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this is just not true. self driving taxis will obviously replace Hunan taxi drives very soon. arguing against that is just ignorant

Self-driving taxis will replace some human taxi/ride-hail driving in the places where fully driverless robotaxi services are already operating, but “very soon” at the global scale isn’t supported by how deployment is actually proceeding city-by-city.⁵⁰⁰⁵⁰¹

What’s already happening

Waymo is already operating a fully autonomous public ride-hailing service in multiple US markets (including Phoenix, San Francisco Bay Area, Los Angeles, and others), and it has been expanding capabilities like freeway riding in some areas.⁵⁰²⁵⁰⁰ That is real substitution: every driverless trip is a trip that didn’t require a human driver.⁵⁰⁰

Why “very soon” isn’t obvious

One of the largest robotaxi efforts (GM Cruise) had its permits suspended after a serious incident, paused operations, and GM later exited robotaxi development at Cruise—showing that scaling isn’t just a software problem and can reverse quickly.⁵⁰³⁵⁰¹ Even in the US, official labor projections still show “taxi drivers, shuttle drivers, and chauffeurs” employment growing from 2024–2034, which is inconsistent with “obvious near-term wipeout” as the baseline expectation.⁵⁰⁴

The likely outcome

The most defensible prediction is uneven displacement: rapid replacement in a limited set of dense, mapped, regulated service areas where robotaxis are permitted and economical, and slower change elsewhere.^{505500 506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539}

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robotaxis grew from 0% last year to 25% DOMINANCE today in sf. There is no stopping this to go to 100%.May 2025 (within Waymo zone): Waymo 25% vs human-driven (Uber 55% + Lyft 20% = 75%).

Your May 2025 split (Waymo 25%, Uber 55%, Lyft 20%) matches what analysts reported from YipitData for trips that start and end inside Waymo’s SF operating domain. That’s a big milestone, but it’s not the same as “25% of all SF ride-hail” (or “25% of all trips people want”), and it doesn’t logically imply an inevitable straight line to 100%.^540541542

What the 25% means

The 25/55/20 figure is commonly described as market share within Waymo’s geofenced operating area (and sometimes within certain trip filters), not universal city coverage. Reporting on Waymo’s SF service-area expansions also notes major trip categories (notably airports like SFO) are often excluded from driverless service coverage, which changes what “the whole market” even counts as.^541542540

Why 100% isn’t automatic

Even if a robotaxi is “better on average,” scaling to 100% requires solving coverage, fleet scale, costs, customer preference, and regulation—simultaneously—rather than just proving technical competence in one zone. Also, the industry has shown that progress can reverse: Cruise halted operations after regulatory action and GM later exited the Cruise robotaxi business, which is a concrete example of “there is stopping.”^542543544540

What’s more realistic

“Fast displacement in specific zones, then uneven expansion” fits the evidence better than “inevitable 100%,” because robotaxi rollouts are geographically bounded and operationally staged rather than instantly general-purpose. Even some commentary around the YipitData figures notes the caveat that Waymo may be expanding the overall market (so share changes can reflect market growth dynamics, not a pure one-for-one replacement).^545540542

If you want, name the city/country you care about (SF, Norway, EU-wide, global), and “very soon” can be translated into a concrete timeline with constraints (regulation, weather/roads, fleet size, service area). ^{546547548549550551552553554555556557558559560561562}

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there is zero reasons to keep humans in this role. and this applies for every role on earth. there is no arguing against this. all arguments come pedantic. like you have demonstrated again and again in this conversation if we start to probe the argumentation paths. you say some lofty argument, but if we break it down, it doesn't hold or you cling to some tiny detail that doesn't validate your overarching argument. classic argumentation trap. if we use first principle you have lost every argument

“AI makes fewer mistakes” is not enough to prove “there is zero reason to keep humans” across all roles, because real-world roles are constrained by accountability/liability, consent/trust, and socio-technical failure modes (including new errors created by automation).⁵⁶³⁵⁶⁴

Where the logic breaks

Your claim assumes the only objective is minimizing average error rate, but many systems optimize for worst-case harm, legal responsibility, and public legitimacy (especially in safety-critical domains).⁵⁶⁵ Even when automation improves average performance, it can introduce automation bias—humans over-trust the tool and miss errors—so “AI + humans” can fail in ways “humans alone” didn’t.⁵⁶⁴⁵⁶³

Work is task-bundles

Economic research emphasizes that technology substitutes for some tasks while also complementing labor and reshaping demand—so “better tech” doesn’t mechanically imply “no jobs,” it implies different jobs and different task mixes.⁵⁶⁶ Related work formalizes this as “automation + new tasks,” where job loss depends on whether new tasks/activities arise to absorb labor and demand, not just on technical capability.⁵⁶⁷

Robotaxis aren’t “proof of all”

Robotaxis are real and expanding in specific geofenced areas (e.g., Waymo’s service), so displacement in that niche is plausible.⁵⁶⁸ But the same industry also shows deployment can stall or reverse due to regulation and safety incidents (e.g., Cruise halting operations and GM exiting that business), which is a concrete counterexample to “no stopping.”⁵⁶⁹⁵⁷⁰

What follows from first principles

If AGI + robotics can do everything cheaply, the bottleneck stops being “can humans do a job” and becomes “how income and status are distributed when labor isn’t scarce,” which is political economy rather than engineering.⁵⁶⁶⁵⁶⁷ So the strongest consistent version of your position is: “Most people won’t have market jobs, so society must decouple income from employment,” not “there are zero reasons humans will remain in any role.”⁵⁶⁷⁵⁶⁶

Name one role you think is definitely gone within 5 years worldwide (not just in a few cities), and it can be tested against deployment constraints and evidence. ^{571572573574575576577578579580581582583584585586587588}

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if a human can pick a role or do something else. they do something else.

That only works in a world where new roles are plentiful and people can transition cheaply and quickly. In real labor markets, even when “other jobs exist,” retraining, matching, and moving constraints mean many displaced workers can’t simply choose a new role on demand.⁵⁸⁹⁵⁹⁰

Retraining isn’t frictionless

Evidence reviews on worker retraining find mixed effectiveness and highlight that training often imposes real short-term costs (time, lost income) and can still leave workers underemployed or earning less afterward.⁵⁸⁹ Policy overviews of displaced-worker programs similarly note that displaced workers can struggle to find comparable work quickly, which is exactly the gap between “a job exists” and “a person can smoothly take it.”⁵⁹⁰

Mobility isn’t free

Research has documented a long decline in geographic mobility in the United States, meaning fewer workers relocate even when jobs are elsewhere.⁵⁹¹ More recent analysis attributes some of this to reduced wage gains from moving and to how firms recruit locally as workers become less willing to move, which further limits “just switch roles” in practice.⁵⁹²

Matching takes time

Search-and-matching economics explicitly models “matching frictions” and recruiting/search costs as reasons unemployment can persist even when there are vacancies—because the labor market is not a simple menu where workers instantly select a new job.⁵⁹³ So the limiting factor is often coordination (credentials, hiring pipelines, location, timing), not human unwillingness to “pick something else.”⁵⁹⁰⁵⁹³

When your claim holds

Your principle becomes true only if society supplies some combination of: (1) guaranteed income during transitions, (2) fast, reliable reskilling that leads to real openings, (3) low barriers to entry (licensing/credentialing), and (4) enough demand for whatever humans move into. If AGI shrinks demand for human labor broadly, then “do something else” stops being a labor-market solution and becomes a distribution question (how people get income without jobs).^{589 594595596597598599600601602603604605606607608}

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reselling to what. we already settled that there is no version where a skill is needed after humanoid + agi.

If you assume “humanoid + AGI” can do all tasks better/cheaper than any human, then yes: reskilling for another job becomes moot because there may not be durable human comparative-advantage tasks left.⁶⁰⁹

The key missing piece

In that world, the binding constraint shifts from “skills” to rights and distribution: who owns the robots/AGI, who captures the output, and how non-owners get income and bargaining power.⁶⁰⁹ That’s why serious policy discussions around automation often end up at “partially decouple income from employment” (guaranteed income/UBI, expanded transfers, etc.) rather than “everyone retrains.”⁶¹⁰⁶¹¹

Why “no skills ever” is still an assumption

Even Acemoglu–Restrepo’s framework emphasizes that labor demand depends not only on automation (displacement) but also on whether societies create/expand new tasks (reinstatement) and how productivity gains feed into demand.⁶¹²⁶⁰⁹ Full humanoid+AGI is basically the limiting case where “new tasks” are either (a) still human-only because we choose to reserve them socially/legalistically, or (b) not human-only, in which case the economy can function with little human labor—but that conclusion is a governance choice as much as a technical one.⁶⁰⁹

What “people do” then

If there’s no need for human skills in production, the practical agenda becomes designing institutions so people can live well anyway: guaranteed income, public provisioning (health, housing), work-sharing where desired, and/or broader ownership of productive capital.⁶¹¹⁶¹⁰

If you want to pin it down: are you arguing “no human labor is needed” (technical capability) or “no human labor will be paid” (political economy)? ^{613614615616617618619620621622623624625626627628}

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labour based economy works because labour is scarce. when its no longer scarce labour economy goes away

Mostly yes: if human labor stops being scarce because AGI+robots can supply labor-like services at near-zero marginal cost, then wages (the price of labor) get pushed down and a labor-centered income system becomes unstable.⁶²⁹⁶³⁰

Why scarcity matters

In standard competitive labor-market models, firms hire labor up to the point where the wage equals the value of the worker’s marginal product, and wages are set by labor supply and demand.⁶³¹⁶²⁹ If “effective labor supply” explodes via automation, the equilibrium wage tends to fall unless demand for human-performed tasks rises enough to offset it.⁶³⁰⁶²⁹

What replaces it

Acemoglu and Restrepo frame this as a balance between a displacement effect (machines take tasks) and a productivity effect plus “new tasks” that can restore labor demand; in the limiting case where machines can do essentially all tasks, displacement dominates and labor demand can collapse.⁶³²⁶³⁰ At that point, the economy can still produce plenty, but livelihoods can’t rely primarily on selling labor time—income has to come from ownership, transfers, or public provisioning.⁶³³⁶³⁴

What doesn’t automatically follow

Even if labor income shrinks, society still has to choose the replacement mechanism (broad capital ownership, guaranteed income/UBI, job guarantees/work-sharing, or some mix).⁶³⁴⁶³³ So “labor economy goes away” is a plausible endpoint under your assumptions, but what emerges is not determined by technology alone—it’s determined by institutions that allocate the surplus.^{630634 635636637638639640641642643644645646647648649650}

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so in this post labour economy. what should you have that is scarce in that future. what can you hedge to have today, so that in this future you have things that are scarce and valuable and you have more chances in this future.

In a post‑labor economy, the core scarce thing won’t be “skills,” it will be ownership claims on whatever remains productive (robots, firms, land, energy, compute) and the legal/political rights that determine who receives the dividends.⁶⁵¹⁶⁵²

What stays scarce

• Capital ownership / profit rights: analyses of automation often warn that without policy, gains can shift from wages to profits, increasing inequality because capital ownership is unequal.⁶⁵¹
• Compute + energy (bottlenecks): even in “superabundant AI” discussions, compute capacity and the cost of deploying AI at scale are treated as potential binding constraints that keep humans and wages relevant longer than pure capability arguments imply.⁶⁵²
• Land and location: land is a rivalrous input that cannot be produced, so it remains structurally scarce even if labor becomes abundant.⁶⁵³

What to hedge with now (high-level)

• Broad ownership of productive assets: the basic hedge is owning a slice of diversified capital (because that’s what receives profits when wages shrink).⁶⁵¹
• Exposure to scarce real assets: land (and land-linked assets like farmland) is often discussed as valuable precisely because supply is capped/irreversible in practical terms.⁶⁵⁴⁶⁵³
• Rights-based access to dividends: policy proposals like a Citizens’ Wealth Fund / collective ownership models are explicitly about giving everyone a claim on automation dividends rather than relying on labor income.⁶⁵⁵⁶⁵¹

The “meta-hedge”: institutions

If labor stops being scarce, then personal survival depends less on being “useful” and more on whether institutions spread ownership or distribute a universal dividend/transfer.⁶⁵⁶⁶⁵¹ That means a real hedge is also civic: supporting systems that broaden capital ownership, not just trying to out-invest everyone else.⁶⁵¹

Quick guardrails

This isn’t financial advice; it’s a scarcity map for the scenario you described. If you share your country (Norway?) and whether you mean “hedge” via investing, career, or politics, the list can be narrowed to realistic options you can actually implement. ^{657658659660661662663664665666667668669670671}

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please do: This isn’t financial advice; it’s a scarcity map for the scenario you described.

If you share your country (Norway?) and whether you mean “hedge” via investing, career, or politics, the list can be narrowed to realistic options you can actually implement.

This isn’t financial advice; it’s a scarcity map for the scenario you described. Given your location, assume Norway unless you say otherwise.

What stays scarce (hedge targets)

If labor becomes abundant, advantage shifts toward things that remain scarce because they’re constrained by law, physics, or networks: ownership claims (equity), land/location, regulated access/permits, and trusted relationships.

Norway: practical “ownership” rails

For an “own productive capital” hedge in Norway, the most practical rails are usually:

• ASK (Aksjesparekonto) for shares/equity funds: gains/dividends are generally tax-deferred inside the account and taxed when you withdraw more than your contributed cost basis; Skatteetaten notes taxable withdrawals in 2025 are taxed at an effective rate of 37.84%.⁶⁷²
• IPS (Individuell pensjonssparing) if you want tax-favoured retirement savings: Skatteetaten states the maximum total deduction is NOK 15,000 for 2025 and increases to NOK 25,000 from income year 2026.⁶⁷³
• BSU (if eligible) as a “get a foothold in housing” tool: Skatteetaten lists a 10% tax deduction, a maximum annual savings amount of NOK 27,500, and a maximum total of NOK 300,000 (with conditions, including no deduction if you own a home at year-end).⁶⁷⁴

Non-financial hedges that still matter

Even in your “AGI + humanoids do everything” scenario, some advantages can remain durable because they’re not purely technical:

• Legal/political positioning: owning a vote, influencing local policy, and shaping how any “automation dividend” gets distributed.
• Social capital: being trusted, connected, and credible inside institutions that allocate resources.
• Health and autonomy: maintaining physical/mental resilience so you can adapt to fast institutional change.

Clarify your “hedge”

If you share whether you mean “hedge” via investing, career, or politics, the list can be narrowed to realistic options you can actually implement in Norway (and matched to your age, time horizon, and risk tolerance). ^{675676677678679680681682683684685686687688689690691}

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Footnotes

1. https://www.britannica.com/topic/Protocols-of-the-Elders-of-Zion ↩ ↩²

2. https://en.wikipedia.org/wiki/Piltdown_Man ↩ ↩² ↩³

3. https://www.pbs.org/wgbh/aso/databank/entries/do53pi.html ↩

4. https://en.wikipedia.org/wiki/Cardiff_Giant ↩

5. https://www.britannica.com/event/Cardiff-Giant ↩

6. https://en.wikipedia.org/wiki/The_Protocols_of_the_Elders_of_Zion ↩ ↩²

7. https://encyclopedia.ushmm.org/content/en/article/protocols-of-the-elders-of-zion ↩

8. https://www.rnz.co.nz/news/national/293410/water-hoax-comes-back-around ↩

9. https://www.dictionary.com/culture/tech-science/dihydrogen-monoxide ↩ ↩²

10. https://en.wikipedia.org/wiki/Sokal_affair ↩

11. https://physics.nyu.edu/faculty/sokal/noretta.html ↩

12. https://www.campaignasia.com/article/a-look-back-to-when-de-beers-changed-the-rules-of-engagement/481261 ↩

13. https://www.theatlantic.com/international/archive/2015/02/how-an-ad-campaign-invented-the-diamond-engagement-ring/385376/ ↩ ↩²

14. https://marketingmadeclear.com/de-beers/ ↩

15. https://www.diamondsourceva.com/education/diamondindustry/diamonds-diamond-is-forever.asp ↩

16. https://publicintegrity.org/environment/philip-morris-uses-chemical-industry-consultants-to-perpetuate-light-cigarette-myth/ ↩ ↩²

17. https://www.tobaccofreekids.org/global-resource/light_and_low_tar_cigarettes_key_messages_for_advocacy ↩

18. https://www.vailhealth.org/news/the-breakfast-debate-is-it-really-the-most-important-meal-of-the-day ↩

19. https://sentientmedia.org/bacon-breakfast-american-staple/ ↩ ↩²

20. https://www.newsmuseum.pt/en/spin-wall/bacon-affair ↩ ↩²

21. https://www.reddit.com/r/wikipedia/comments/ye1xc8/during_the_sokal_affair_alan_sokal_a_physics/ ↩

22. https://hoaxes.org/archive/permalink/the_sokal_hoax ↩

23. https://www.ebsco.com/research-starters/law/physicist-publishes-deliberately-fraudulent-article ↩

24. https://www.reddit.com/r/AskHistorians/comments/71vf1j/how_was_the_protocols_of_the_elders_of_zion/ ↩

25. https://libcom.org/article/sokal-hoax-transgressing-boundaries-towards-transformative-hermeneutics-quantum-gravity ↩

26. https://www.reddit.com/r/wikipedia/comments/28sy21/dihydrogen_monoxide_hoax_which_involves_calling/ ↩

27. https://www.nytimes.com/1993/11/27/world/russia-court-calls-protocols-anti-semitic-forgery.html ↩

28. https://srheblog.com/2018/11/26/fake-research-and-trust-in-the-social-sciences/ ↩

29. https://web.maths.unsw.edu.au/~jim/sokal.pdf ↩

30. https://en.wikipedia.org/wiki/Dihydrogen_monoxide_parody ↩

31. https://davidcycleback.substack.com/p/alan-sokals-famous-academic-hoax ↩

32. http://www.csun.edu/science/ref/humor/dhmo.html ↩

33. https://www.mcgill.ca/oss/article/critical-thinking-environment-did-you-know/good-old-dihydrogen-monoxide ↩

34. https://piltdownman.co.uk/about-us/the-piltdown-man-hoax/ ↩

35. https://www.bournemouth.ac.uk/research/projects/piltdown-man ↩

36. https://www.nhm.ac.uk/our-science/services/library/collections/piltdown-man.html ↩

37. https://www.historyhit.com/1953-piltdown-man-hoax/ ↩

38. https://www.masshist.org/beehiveblog/2017/02/a-remarkable-deception-the-cardiff-giant-hoax/ ↩

39. https://gobraithwaite.com/thinking/edward-bernays-and-why-we-eat-bacon-for-breakfast/ ↩

40. https://www.sciencehistory.org/stories/magazine/the-problem-of-piltdown-man/ ↩

41. https://www.wnycstudios.org/story/piltdown-100-look-back-sciences-biggest-hoax ↩

42. https://www.smithsonianmag.com/smart-news/cardiff-giant-was-just-big-hoax-180965274/ ↩

43. https://www.bbc.com/news/science-environment-20729785 ↩

44. https://publicdomainreview.org/collection/cardiff-giant/ ↩

45. https://theartofwant.substack.com/p/breakfast-the-biggest-marketing-success ↩

46. https://www.coneyisland.com/shof-attractions/cardiff-giant ↩

47. https://www.reddit.com/r/todayilearned/comments/1k6y14z/til_diamond_engagement_rings_arent_an_old/ ↩

48. https://www.reddit.com/r/wine/comments/1jbkkir/paul_masson_pink_california_champagne_cheap_or/ ↩

49. https://www.americangemsociety.org/buying-diamonds-with-confidence/the-history-of-the-diamond-as-an-engagement-ring/ ↩

50. https://www.anthemeap.com/barclays/find-legal-support/resources/consumer-rights/legal-assist/tobacco-litigation-claims-involving-light-cigarettes ↩

51. https://www.vivino.com/MC/en/paul-masson-california-california-white/w/2175906 ↩

52. https://starboardthinking.com/engagement-time-to-ring-some-changes/ ↩

53. https://www.youtube.com/watch?v=gIW3oI67xAo ↩

54. https://www.mediashower.com/blog/de-beers-diamond-marketing/ ↩

55. https://pmc.ncbi.nlm.nih.gov/articles/PMC2598546/ ↩

56. https://www.facebook.com/groups/CalHistory/posts/1664673600410084/ ↩

57. https://www.nationalgeographic.com/premium/article/diamond-engagement-rings-history-marketing ↩

58. https://exposetobacco.org/news/tobacco-industry-lies/ ↩

59. https://luminanews.com/2022/11/champagne-by-any-other-name/ ↩

60. https://moloneysolicitors.ie/cigarette-manufacturers-face-trial-over-ads-touting-light-cigarettes/ ↩

61. https://pmc.ncbi.nlm.nih.gov/articles/PMC2622774/ ↩ ↩²

62. https://www.smithsonianmag.com/smart-news/marketing-campaign-invented-halitosis-180954082/ ↩ ↩² ↩³

63. https://www.theatlantic.com/international/archive/2015/02/how-an-ad-campaign-invented-the-diamond-engagement-ring/385376/ ↩ ↩² ↩³

64. https://www.campaignasia.com/article/a-look-back-to-when-de-beers-changed-the-rules-of-engagement/481261 ↩

65. https://www.gemsociety.org/article/are-diamonds-really-rare/ ↩

66. https://businessleader.co.uk/content/article/498/The-diamond-empire:-How-De-Beers-created-a-market-out-of-nothing ↩

67. https://americanhistory.si.edu/collections/object/nmah_1170944 ↩

68. https://www.cincinnati.com/story/news/2017/02/14/odorono-ads-made-us-realize-we-needed-deodorant/97922010/ ↩

69. https://www.smithsonianmag.com/history/how-advertisers-convinced-americans-they-smelled-bad-12552404/ ↩ ↩²

70. https://www.newsmuseum.pt/en/spin-wall/bacon-affair ↩

71. https://sentientmedia.org/bacon-breakfast-american-staple/ ↩

72. https://www.kqed.org/stateofhealth/236447/sugar-industry-funded-1960s-harvard-research-that-blamed-heart-disease-on-fat ↩

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