#laboreconomics — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #laboreconomics, aggregated by home.social.
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Panelist Spotlight: Ron Hetrick of Lightcast joins BroadbandLive TODAY: AI and Workforce – Combatting Job Displacement.
A leading labor economist with nearly 30 years of experience, Ron will share insights on AI, labor market trends, and preparing for the future of work.
The Livestream is at 12 ET today. Don't miss it.
#AI #FutureOfWork #LaborEconomics #tech #artificialIntelligence #technology
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Panelist Spotlight: Ron Hetrick of Lightcast joins BroadbandLive TODAY: AI and Workforce – Combatting Job Displacement.
A leading labor economist with nearly 30 years of experience, Ron will share insights on AI, labor market trends, and preparing for the future of work.
The Livestream is at 12 ET today. Don't miss it.
#AI #FutureOfWork #LaborEconomics #tech #artificialIntelligence #technology
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Panelist Spotlight: Ron Hetrick of Lightcast joins BroadbandLive TODAY: AI and Workforce – Combatting Job Displacement.
A leading labor economist with nearly 30 years of experience, Ron will share insights on AI, labor market trends, and preparing for the future of work.
The Livestream is at 12 ET today. Don't miss it.
#AI #FutureOfWork #LaborEconomics #tech #artificialIntelligence #technology
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Panelist Spotlight: Ron Hetrick of Lightcast joins BroadbandLive TODAY: AI and Workforce – Combatting Job Displacement.
A leading labor economist with nearly 30 years of experience, Ron will share insights on AI, labor market trends, and preparing for the future of work.
The Livestream is at 12 ET today. Don't miss it.
#AI #FutureOfWork #LaborEconomics #tech #artificialIntelligence #technology
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Panelist Spotlight: Ron Hetrick of Lightcast joins BroadbandLive TODAY: AI and Workforce – Combatting Job Displacement.
A leading labor economist with nearly 30 years of experience, Ron will share insights on AI, labor market trends, and preparing for the future of work.
The Livestream is at 12 ET today. Don't miss it.
#AI #FutureOfWork #LaborEconomics #tech #artificialIntelligence #technology
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The Myth of Meritocracy
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
Baybay City, Leyte, Philippines — Thursday, June 4, 2026, 8:30 a.m. Eastern Time
Meritocracy is widely assumed to be the organizing principle behind modern hiring and governance. The idea is simple and reassuring: the most capable individuals rise to positions of responsibility based on skill, effort, and demonstrated competence. Yet observable outcomes increasingly contradict this assumption. Repeated leadership failures, institutional stagnation, and persistent misalignment between authority and capability suggest that meritocracy, as commonly understood, is not functioning as advertised.
This article examines what merit-based selection would actually require, how current systems operate instead, and why the gap between the two matters for institutional survival.
What Merit-Based Selection Would Require
In practical terms, a merit-based system would prioritize demonstrated ability to perform the work in question. That includes relevant skills, judgment under uncertainty, the capacity to synthesize information across domains, and a documented record of making sound decisions over time. It would also require the ability to challenge faulty assumptions and adapt when conditions change.
Merit-based selection is not about perfection or elite credentials. It is about functional competence: whether an individual can reliably perform the duties of the role in real-world conditions. In complex organizations, this often includes experience navigating failure, recognizing systemic risk, and correcting course without external prompting.
If such criteria were consistently applied, outcomes would show a strong correlation between authority and performance. In many institutions, they do not.
What Current Systems Actually Measure
Modern hiring systems rarely evaluate candidates on their ability to do the work as it is actually performed. Instead, they prioritize signals that can be quickly processed at scale. These include resume formatting, keyword alignment with job descriptions, credential familiarity, and narrative conformity.
The widespread use of automated screening tools has intensified this shift. Resumes are increasingly filtered based on textual similarity rather than substantive capability. Candidates who are adept at mirroring job descriptions, regardless of whether they possess the underlying skills, are rewarded with visibility. Those whose experience does not translate cleanly into standardized language are often excluded early in the process.
This approach favors candidates who understand how to perform alignment rather than those who understand how to perform the work. Over time, this creates a selection bias toward presentation skill and institutional legibility, rather than competence.
Automation and the Scaling of Bias
Hiring platforms did not invent these biases, but they have amplified them. Systems designed to manage large applicant pools rely on reduction: fewer variables, simpler comparisons, and faster elimination. As a result, nonconforming candidates are filtered out not because they are unqualified, but because they are difficult to categorize.
Automation rewards predictability. It penalizes unconventional career paths, cross-disciplinary experience, and non-linear progression—traits that are often associated with problem-solving and innovation. The more an applicant resembles a predefined template, the more likely they are to advance.
This dynamic creates a paradox. As organizations claim to seek innovation and adaptability, their selection systems increasingly favor sameness and risk avoidance.
Observable Outcomes That Contradict Meritocracy
If meritocracy were operating effectively, leadership outcomes would improve over time. Institutions would learn from past failures, retain institutional memory, and demonstrate increasing competence in crisis management. In many cases, the opposite is true.
Across sectors, organizations repeatedly elevate individuals who perform well in stable conditions but struggle under stress. Decision-makers are recycled despite documented failures. Strategic errors are repeated even when prior consequences are well known. Responsibility is diffuse, while accountability is limited.
These patterns are not random. They are consistent with selection systems that prioritize safety, familiarity, and non-disruption over demonstrated ability.
Risk Avoidance as a Selection Principle
One of the least examined drivers of modern hiring is risk avoidance. Selection processes are often structured to minimize perceived downside rather than maximize potential upside. Candidates who might challenge existing assumptions, expose internal weaknesses, or disrupt established hierarchies are treated as liabilities.
This approach is reinforced by legal and reputational pressures. Subjective judgments are embedded in language such as “culture fit,” “communication style,” or “organizational alignment.” These criteria are difficult to measure, difficult to contest, and easy to defend after the fact. They also allow decision-makers to exclude candidates who are competent but inconvenient.
Over time, risk avoidance becomes self-reinforcing. Institutions staffed by individuals selected for minimal disruption become less capable of recognizing or responding to systemic threats.
Why the Meritocracy Myth Persists
Despite mounting evidence, the belief in meritocracy remains deeply entrenched. One reason is that it provides moral cover. If outcomes are assumed to be merit-based, failures can be attributed to individual shortcomings rather than systemic design. This deflects scrutiny from selection mechanisms themselves.
Another reason is that the myth benefits those already in positions of authority. If the system is presumed fair, existing hierarchies appear justified. Questioning the validity of merit-based selection challenges not only hiring practices, but the legitimacy of current leadership.
As a result, institutions often invest more effort in defending the appearance of meritocracy than in examining whether it functions in practice.
Why This Matters Beyond Hiring
Selection systems shape more than careers. They shape institutional intelligence. When organizations consistently choose candidates based on legibility and conformity rather than capability, they reduce their capacity to learn, adapt, and respond to change.
In periods of stability, these weaknesses may remain hidden. In periods of disruption, they become visible quickly. The consequences include delayed responses, misallocation of resources, and an inability to correct course before failures become systemic.
Meritocracy is often framed as a moral ideal. In reality, it is a functional requirement. Institutions that fail to align authority with competence do not merely become unfair. They become fragile.
This article begins a series examining how modern hiring and governance systems have drifted away from merit-based selection, what has replaced it, and why that shift is undermining institutional performance. Subsequent articles will document the specific mechanisms that now govern access to decision-making authority, and the costs of maintaining them.
For more social commentary, please see Occupy 2.5 at https://Occupy25.com
References
Berg, J. M., Grant, A. M., & Johnson, V. (2010). When callings are calling: Crafting work and leisure in pursuit of unanswered occupational callings. Organization Science, 21(5), 973–994. https://doi.org/10.1287/orsc.1090.0497
Cappelli, P. (2019). Your approach to hiring is all wrong. Harvard Business Review Press.
Highhouse, S., Brooks, M. E., & Gregarus, G. (2009). An organizational impression management perspective on the formation of corporate reputations. Journal of Management, 35(6), 1481–1493. https://doi.org/10.1177/0149206309348788
Rivera, L. A. (2012). Hiring as cultural matching: The case of elite professional service firms. American Sociological Review, 77(6), 999–1022. https://doi.org/10.1177/0003122412463213
Autor, D., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. Quarterly Journal of Economics, 118(4), 1279–1333. https://doi.org/10.1162/003355303322552801
#credentialism #eliteHiring #governanceAnalysis #hiringPractices #institutionalFailure #laborEconomics #meritocracy #organizationalRisk #workforceSystems -
In 1961, a robot took a job no one wanted. The same robot would have been helpless in a kitchen. Danger isn't a property of an environment. It's a relationship between an environment and the body encountering it. New post on the ecology of work and what robots taught us about human fitness:
https://jameshoward.us/2026/05/29/the-ecology-of-work/ -
In 1961, a robot took a job no one wanted. The same robot would have been helpless in a kitchen. Danger isn't a property of an environment. It's a relationship between an environment and the body encountering it. New post on the ecology of work and what robots taught us about human fitness:
https://jameshoward.us/2026/05/29/the-ecology-of-work/ -
In 1961, a robot took a job no one wanted. The same robot would have been helpless in a kitchen. Danger isn't a property of an environment. It's a relationship between an environment and the body encountering it. New post on the ecology of work and what robots taught us about human fitness:
https://jameshoward.us/2026/05/29/the-ecology-of-work/ -
In 1961, a robot took a job no one wanted. The same robot would have been helpless in a kitchen. Danger isn't a property of an environment. It's a relationship between an environment and the body encountering it. New post on the ecology of work and what robots taught us about human fitness:
https://jameshoward.us/2026/05/29/the-ecology-of-work/ -
In 1961, a robot took a job no one wanted. The same robot would have been helpless in a kitchen. Danger isn't a property of an environment. It's a relationship between an environment and the body encountering it. New post on the ecology of work and what robots taught us about human fitness:
https://jameshoward.us/2026/05/29/the-ecology-of-work/ -
📈📉 Standard Error is delighted to announce that Sarah Winton (LSE ’26) has been awarded the Best Paper Prize at the 7th Workshop on the Economics and Politics of Migration. 🧵 1/9 stderr-editors.com/blog/best-pa... #Migration #Refugees #LaborEconomics #DevelopmentEconomics
Standard Error awards Best Pap... -
📈📉 Standard Error is delighted to announce that Sarah Winton (LSE ’26) has been awarded the Best Paper Prize at the 7th Workshop on the Economics and Politics of Migration. 🧵 1/9 stderr-editors.com/blog/best-pa... #Migration #Refugees #LaborEconomics #DevelopmentEconomics
Standard Error awards Best Pap... -
📈📉 Standard Error is delighted to announce that Sarah Winton (LSE ’26) has been awarded the Best Paper Prize at the 7th Workshop on the Economics and Politics of Migration. 🧵 1/9 stderr-editors.com/blog/best-pa... #Migration #Refugees #LaborEconomics #DevelopmentEconomics
Standard Error awards Best Pap... -
📈📉 Standard Error is delighted to announce that Sarah Winton (LSE ’26) has been awarded the Best Paper Prize at the 7th Workshop on the Economics and Politics of Migration. 🧵 1/9 stderr-editors.com/blog/best-pa... #Migration #Refugees #LaborEconomics #DevelopmentEconomics
Standard Error awards Best Pap... -
📈📉 Standard Error is delighted to announce that Sarah Winton (LSE ’26) has been awarded the Best Paper Prize at the 7th Workshop on the Economics and Politics of Migration. 🧵 1/9 stderr-editors.com/blog/best-pa... #Migration #Refugees #LaborEconomics #DevelopmentEconomics
Standard Error awards Best Pap... -
Fear and Loathing of AI (Part III): “Learn AI” Is the New “Learn to Code”
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
There is a sentence that shows up in every technological cycle right before the disappointment phase begins.
“Just learn the skill.”
It sounds empowering. It sounds reasonable. It sounds like personal agency.
It is also a lie we have been telling people for decades.
The obedience script
“Learn to code” was never about opportunity.
It was about discipline.It trained people to accept that:
- structural failures are personal problems,
- economic insecurity is an individual moral test,
- and survival depends on constant retraining at your own expense.
When the promised jobs didn’t materialize—or paid far less than advertised—the story shifted seamlessly: you didn’t learn the right language, the right framework, the right stack.
Now the phrase has been updated.
“Learn AI.”
Same script. Same pressure. Same outcome.
Skills don’t collapse — markets do
Coding did not fail because people were lazy or incapable. It failed because markets flooded, tools commoditized, and labor lost leverage.
AI will follow the same arc, only faster.
The moment a skill becomes:
- widely accessible,
- easily automated,
- and expected rather than rewarded,
it stops being a path to security and becomes a baseline requirement for staying afloat.
The reward for compliance is not prosperity.
It is continued participation.Training as cost transfer
Here is what “learn AI” really means in practice:
- You pay for the courses.
- You absorb the time cost.
- You shoulder the career risk.
- You adapt repeatedly as tools change.
- You accept lower pay because “AI makes you more efficient.”
None of that is accidental.
It is a system designed to push costs downward while extracting value upward.
The more often you are told to retrain, the clearer it becomes that training itself is the product.
The illusion of agency
People are encouraged to believe that mastery equals control.
But control does not come from skill alone.
It comes from:- ownership,
- bargaining power,
- regulation,
- and collective leverage.
Without those, skill is just labor dressed up as self-improvement.
Learning AI may help you keep your job a little longer.
It will not protect you from the logic of the system deploying it.What learning actually means now
This does not mean you should refuse to learn.
It means you should learn without illusions.
Learn AI the way you learn any tool:
- to reduce friction,
- to save time,
- to extend what you already do.
Do not learn it expecting salvation.
Do not learn it expecting loyalty from platforms.
Do not learn it expecting the market to reward you for effort.Markets reward leverage, not diligence.
The quiet truth
The most dangerous part of “learn AI” is not that it is false.
It is that it is incomplete.
It tells people how to adapt, but never who benefits.
It demands flexibility, but never offers stability.
It promises relevance, but never guarantees dignity.We have seen this cycle before.
And it did not end with freedom.
It ended with exhaustion.
For more social commentary, please see Occupy 2.5 at https://Occupy25.com
#AISkills #ArtificialIntelligence #economicPrecarity #futureOfWork #laborEconomics #learnToCode #Occupy25 #platformCapitalism #technologyHype #workforceRetraining #WPSNews -
Fear and Loathing of AI (Part III): “Learn AI” Is the New “Learn to Code”
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
There is a sentence that shows up in every technological cycle right before the disappointment phase begins.
“Just learn the skill.”
It sounds empowering. It sounds reasonable. It sounds like personal agency.
It is also a lie we have been telling people for decades.
The obedience script
“Learn to code” was never about opportunity.
It was about discipline.It trained people to accept that:
- structural failures are personal problems,
- economic insecurity is an individual moral test,
- and survival depends on constant retraining at your own expense.
When the promised jobs didn’t materialize—or paid far less than advertised—the story shifted seamlessly: you didn’t learn the right language, the right framework, the right stack.
Now the phrase has been updated.
“Learn AI.”
Same script. Same pressure. Same outcome.
Skills don’t collapse — markets do
Coding did not fail because people were lazy or incapable. It failed because markets flooded, tools commoditized, and labor lost leverage.
AI will follow the same arc, only faster.
The moment a skill becomes:
- widely accessible,
- easily automated,
- and expected rather than rewarded,
it stops being a path to security and becomes a baseline requirement for staying afloat.
The reward for compliance is not prosperity.
It is continued participation.Training as cost transfer
Here is what “learn AI” really means in practice:
- You pay for the courses.
- You absorb the time cost.
- You shoulder the career risk.
- You adapt repeatedly as tools change.
- You accept lower pay because “AI makes you more efficient.”
None of that is accidental.
It is a system designed to push costs downward while extracting value upward.
The more often you are told to retrain, the clearer it becomes that training itself is the product.
The illusion of agency
People are encouraged to believe that mastery equals control.
But control does not come from skill alone.
It comes from:- ownership,
- bargaining power,
- regulation,
- and collective leverage.
Without those, skill is just labor dressed up as self-improvement.
Learning AI may help you keep your job a little longer.
It will not protect you from the logic of the system deploying it.What learning actually means now
This does not mean you should refuse to learn.
It means you should learn without illusions.
Learn AI the way you learn any tool:
- to reduce friction,
- to save time,
- to extend what you already do.
Do not learn it expecting salvation.
Do not learn it expecting loyalty from platforms.
Do not learn it expecting the market to reward you for effort.Markets reward leverage, not diligence.
The quiet truth
The most dangerous part of “learn AI” is not that it is false.
It is that it is incomplete.
It tells people how to adapt, but never who benefits.
It demands flexibility, but never offers stability.
It promises relevance, but never guarantees dignity.We have seen this cycle before.
And it did not end with freedom.
It ended with exhaustion.
For more social commentary, please see Occupy 2.5 at https://Occupy25.com
#AISkills #ArtificialIntelligence #economicPrecarity #futureOfWork #laborEconomics #learnToCode #Occupy25 #platformCapitalism #technologyHype #workforceRetraining #WPSNews -
Fear and Loathing of AI (Part III): “Learn AI” Is the New “Learn to Code”
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
There is a sentence that shows up in every technological cycle right before the disappointment phase begins.
“Just learn the skill.”
It sounds empowering. It sounds reasonable. It sounds like personal agency.
It is also a lie we have been telling people for decades.
The obedience script
“Learn to code” was never about opportunity.
It was about discipline.It trained people to accept that:
- structural failures are personal problems,
- economic insecurity is an individual moral test,
- and survival depends on constant retraining at your own expense.
When the promised jobs didn’t materialize—or paid far less than advertised—the story shifted seamlessly: you didn’t learn the right language, the right framework, the right stack.
Now the phrase has been updated.
“Learn AI.”
Same script. Same pressure. Same outcome.
Skills don’t collapse — markets do
Coding did not fail because people were lazy or incapable. It failed because markets flooded, tools commoditized, and labor lost leverage.
AI will follow the same arc, only faster.
The moment a skill becomes:
- widely accessible,
- easily automated,
- and expected rather than rewarded,
it stops being a path to security and becomes a baseline requirement for staying afloat.
The reward for compliance is not prosperity.
It is continued participation.Training as cost transfer
Here is what “learn AI” really means in practice:
- You pay for the courses.
- You absorb the time cost.
- You shoulder the career risk.
- You adapt repeatedly as tools change.
- You accept lower pay because “AI makes you more efficient.”
None of that is accidental.
It is a system designed to push costs downward while extracting value upward.
The more often you are told to retrain, the clearer it becomes that training itself is the product.
The illusion of agency
People are encouraged to believe that mastery equals control.
But control does not come from skill alone.
It comes from:- ownership,
- bargaining power,
- regulation,
- and collective leverage.
Without those, skill is just labor dressed up as self-improvement.
Learning AI may help you keep your job a little longer.
It will not protect you from the logic of the system deploying it.What learning actually means now
This does not mean you should refuse to learn.
It means you should learn without illusions.
Learn AI the way you learn any tool:
- to reduce friction,
- to save time,
- to extend what you already do.
Do not learn it expecting salvation.
Do not learn it expecting loyalty from platforms.
Do not learn it expecting the market to reward you for effort.Markets reward leverage, not diligence.
The quiet truth
The most dangerous part of “learn AI” is not that it is false.
It is that it is incomplete.
It tells people how to adapt, but never who benefits.
It demands flexibility, but never offers stability.
It promises relevance, but never guarantees dignity.We have seen this cycle before.
And it did not end with freedom.
It ended with exhaustion.
For more social commentary, please see Occupy 2.5 at https://Occupy25.com
#AISkills #ArtificialIntelligence #economicPrecarity #futureOfWork #laborEconomics #learnToCode #Occupy25 #platformCapitalism #technologyHype #workforceRetraining #WPSNews -
Fear and Loathing of AI (Part III): “Learn AI” Is the New “Learn to Code”
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
There is a sentence that shows up in every technological cycle right before the disappointment phase begins.
“Just learn the skill.”
It sounds empowering. It sounds reasonable. It sounds like personal agency.
It is also a lie we have been telling people for decades.
The obedience script
“Learn to code” was never about opportunity.
It was about discipline.It trained people to accept that:
- structural failures are personal problems,
- economic insecurity is an individual moral test,
- and survival depends on constant retraining at your own expense.
When the promised jobs didn’t materialize—or paid far less than advertised—the story shifted seamlessly: you didn’t learn the right language, the right framework, the right stack.
Now the phrase has been updated.
“Learn AI.”
Same script. Same pressure. Same outcome.
Skills don’t collapse — markets do
Coding did not fail because people were lazy or incapable. It failed because markets flooded, tools commoditized, and labor lost leverage.
AI will follow the same arc, only faster.
The moment a skill becomes:
- widely accessible,
- easily automated,
- and expected rather than rewarded,
it stops being a path to security and becomes a baseline requirement for staying afloat.
The reward for compliance is not prosperity.
It is continued participation.Training as cost transfer
Here is what “learn AI” really means in practice:
- You pay for the courses.
- You absorb the time cost.
- You shoulder the career risk.
- You adapt repeatedly as tools change.
- You accept lower pay because “AI makes you more efficient.”
None of that is accidental.
It is a system designed to push costs downward while extracting value upward.
The more often you are told to retrain, the clearer it becomes that training itself is the product.
The illusion of agency
People are encouraged to believe that mastery equals control.
But control does not come from skill alone.
It comes from:- ownership,
- bargaining power,
- regulation,
- and collective leverage.
Without those, skill is just labor dressed up as self-improvement.
Learning AI may help you keep your job a little longer.
It will not protect you from the logic of the system deploying it.What learning actually means now
This does not mean you should refuse to learn.
It means you should learn without illusions.
Learn AI the way you learn any tool:
- to reduce friction,
- to save time,
- to extend what you already do.
Do not learn it expecting salvation.
Do not learn it expecting loyalty from platforms.
Do not learn it expecting the market to reward you for effort.Markets reward leverage, not diligence.
The quiet truth
The most dangerous part of “learn AI” is not that it is false.
It is that it is incomplete.
It tells people how to adapt, but never who benefits.
It demands flexibility, but never offers stability.
It promises relevance, but never guarantees dignity.We have seen this cycle before.
And it did not end with freedom.
It ended with exhaustion.
For more social commentary, please see Occupy 2.5 at https://Occupy25.com
#AISkills #ArtificialIntelligence #economicPrecarity #futureOfWork #laborEconomics #learnToCode #Occupy25 #platformCapitalism #technologyHype #workforceRetraining #WPSNews -
Fear and Loathing of AI (Part III): “Learn AI” Is the New “Learn to Code”
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
There is a sentence that shows up in every technological cycle right before the disappointment phase begins.
“Just learn the skill.”
It sounds empowering. It sounds reasonable. It sounds like personal agency.
It is also a lie we have been telling people for decades.
The obedience script
“Learn to code” was never about opportunity.
It was about discipline.It trained people to accept that:
- structural failures are personal problems,
- economic insecurity is an individual moral test,
- and survival depends on constant retraining at your own expense.
When the promised jobs didn’t materialize—or paid far less than advertised—the story shifted seamlessly: you didn’t learn the right language, the right framework, the right stack.
Now the phrase has been updated.
“Learn AI.”
Same script. Same pressure. Same outcome.
Skills don’t collapse — markets do
Coding did not fail because people were lazy or incapable. It failed because markets flooded, tools commoditized, and labor lost leverage.
AI will follow the same arc, only faster.
The moment a skill becomes:
- widely accessible,
- easily automated,
- and expected rather than rewarded,
it stops being a path to security and becomes a baseline requirement for staying afloat.
The reward for compliance is not prosperity.
It is continued participation.Training as cost transfer
Here is what “learn AI” really means in practice:
- You pay for the courses.
- You absorb the time cost.
- You shoulder the career risk.
- You adapt repeatedly as tools change.
- You accept lower pay because “AI makes you more efficient.”
None of that is accidental.
It is a system designed to push costs downward while extracting value upward.
The more often you are told to retrain, the clearer it becomes that training itself is the product.
The illusion of agency
People are encouraged to believe that mastery equals control.
But control does not come from skill alone.
It comes from:- ownership,
- bargaining power,
- regulation,
- and collective leverage.
Without those, skill is just labor dressed up as self-improvement.
Learning AI may help you keep your job a little longer.
It will not protect you from the logic of the system deploying it.What learning actually means now
This does not mean you should refuse to learn.
It means you should learn without illusions.
Learn AI the way you learn any tool:
- to reduce friction,
- to save time,
- to extend what you already do.
Do not learn it expecting salvation.
Do not learn it expecting loyalty from platforms.
Do not learn it expecting the market to reward you for effort.Markets reward leverage, not diligence.
The quiet truth
The most dangerous part of “learn AI” is not that it is false.
It is that it is incomplete.
It tells people how to adapt, but never who benefits.
It demands flexibility, but never offers stability.
It promises relevance, but never guarantees dignity.We have seen this cycle before.
And it did not end with freedom.
It ended with exhaustion.
For more social commentary, please see Occupy 2.5 at https://Occupy25.com
#AISkills #ArtificialIntelligence #economicPrecarity #futureOfWork #laborEconomics #learnToCode #Occupy25 #platformCapitalism #technologyHype #workforceRetraining #WPSNews -
TVs get cheaper every year. So does fast fashion. But rent, healthcare, and college tuition keep climbing. Baumol explained this in 1966. We're still living it. by @daylightatheism.bsky.social
https://onlys.ky/cost-disease/
#Economics #CostOfLiving #Inequality #LaborEconomics #PublicPolicy
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TVs get cheaper every year. So does fast fashion. But rent, healthcare, and college tuition keep climbing. Baumol explained this in 1966. We're still living it. by @daylightatheism.bsky.social
https://onlys.ky/cost-disease/
#Economics #CostOfLiving #Inequality #LaborEconomics #PublicPolicy
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TVs get cheaper every year. So does fast fashion. But rent, healthcare, and college tuition keep climbing. Baumol explained this in 1966. We're still living it. by @daylightatheism.bsky.social
https://onlys.ky/cost-disease/
#Economics #CostOfLiving #Inequality #LaborEconomics #PublicPolicy
-
TVs get cheaper every year. So does fast fashion. But rent, healthcare, and college tuition keep climbing. Baumol explained this in 1966. We're still living it. by @daylightatheism.bsky.social
https://onlys.ky/cost-disease/
#Economics #CostOfLiving #Inequality #LaborEconomics #PublicPolicy
-
TVs get cheaper every year. So does fast fashion. But rent, healthcare, and college tuition keep climbing. Baumol explained this in 1966. We're still living it. by @daylightatheism.bsky.social
https://onlys.ky/cost-disease/
#Economics #CostOfLiving #Inequality #LaborEconomics #PublicPolicy
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Choice Architecture in Occupational Choices
http://repec.business.uzh.ch/RePEc/iso/leadinghouse/0255_lhwpaper.pdf
This study uses a Swiss job board to analyze how rank order and design influence high-stakes occupational choices. Higher rankings increased applications, especially for high-paying and gender-congruent occupations. Users interpreted rank to justify choices aligning with identity, providing field evidence for motivated reasoning. An interactive, visually enriched interface redesign boosted applications and watch list usage. Results show that reducing cognitive load expands the variety of options individuals consider and remember.
#choicearchitecture #motivatedreasoning #laborEconomics #jobtech #ExperimentalEcon
#BoundedRationality -
Choice Architecture in Occupational Choices
http://repec.business.uzh.ch/RePEc/iso/leadinghouse/0255_lhwpaper.pdf
This study uses a Swiss job board to analyze how rank order and design influence high-stakes occupational choices. Higher rankings increased applications, especially for high-paying and gender-congruent occupations. Users interpreted rank to justify choices aligning with identity, providing field evidence for motivated reasoning. An interactive, visually enriched interface redesign boosted applications and watch list usage. Results show that reducing cognitive load expands the variety of options individuals consider and remember.
#choicearchitecture #motivatedreasoning #laborEconomics #jobtech #ExperimentalEcon
#BoundedRationality -
Choice Architecture in Occupational Choices
http://repec.business.uzh.ch/RePEc/iso/leadinghouse/0255_lhwpaper.pdf
This study uses a Swiss job board to analyze how rank order and design influence high-stakes occupational choices. Higher rankings increased applications, especially for high-paying and gender-congruent occupations. Users interpreted rank to justify choices aligning with identity, providing field evidence for motivated reasoning. An interactive, visually enriched interface redesign boosted applications and watch list usage. Results show that reducing cognitive load expands the variety of options individuals consider and remember.
#choicearchitecture #motivatedreasoning #laborEconomics #jobtech #ExperimentalEcon
#BoundedRationality -
Choice Architecture in Occupational Choices
http://repec.business.uzh.ch/RePEc/iso/leadinghouse/0255_lhwpaper.pdf
This study uses a Swiss job board to analyze how rank order and design influence high-stakes occupational choices. Higher rankings increased applications, especially for high-paying and gender-congruent occupations. Users interpreted rank to justify choices aligning with identity, providing field evidence for motivated reasoning. An interactive, visually enriched interface redesign boosted applications and watch list usage. Results show that reducing cognitive load expands the variety of options individuals consider and remember.
#choicearchitecture #motivatedreasoning #laborEconomics #jobtech #ExperimentalEcon
#BoundedRationality -
Choice Architecture in Occupational Choices
http://repec.business.uzh.ch/RePEc/iso/leadinghouse/0255_lhwpaper.pdf
This study uses a Swiss job board to analyze how rank order and design influence high-stakes occupational choices. Higher rankings increased applications, especially for high-paying and gender-congruent occupations. Users interpreted rank to justify choices aligning with identity, providing field evidence for motivated reasoning. An interactive, visually enriched interface redesign boosted applications and watch list usage. Results show that reducing cognitive load expands the variety of options individuals consider and remember.
#choicearchitecture #motivatedreasoning #laborEconomics #jobtech #ExperimentalEcon
#BoundedRationality -
Extending Working Lives: A Systematic Review of Motivations, Determinants, and Institutional Contexts
https://cris.maastrichtuniversity.nl/ws/files/307829408/ROA_TR_2026_2_Extending_Working_Lives.pdf
This review of 103 studies examines determinants of labor participation beyond #retirement age across diverse institutional contexts. Causal evidence indicates that pension reforms and tax incentives yield only modest impacts on extending working lives. Instead, employer practices and workplace flexibility act as primary determinants for feasible post-retirement employment. Findings reveal that financial necessity drives liberal systems, whereas intrinsic motives characterize social-democratic contexts. Success requires aligning macro-level policy with firm-level adaptations to support older workers.…with generous references to
Working beyond retirement age in Germany: The employee’s perspective https://economicscience.net/publications/2011-les/
…individuals with lower income or wealth are more likely to continue working past the statutory retirement age
…financial security enables full labor market withdrawal
… functional capacity predicts the ability to continue working but not necessarily the intention to do so
… work motivation predicts willingness to remain employed beyond retirement age in Germany, partly through its positive association with self-reported work ability and openness to further education. Job rewards also increase willingness to continue working. -
Extending Working Lives: A Systematic Review of Motivations, Determinants, and Institutional Contexts
https://cris.maastrichtuniversity.nl/ws/files/307829408/ROA_TR_2026_2_Extending_Working_Lives.pdf
This review of 103 studies examines determinants of labor participation beyond #retirement age across diverse institutional contexts. Causal evidence indicates that pension reforms and tax incentives yield only modest impacts on extending working lives. Instead, employer practices and workplace flexibility act as primary determinants for feasible post-retirement employment. Findings reveal that financial necessity drives liberal systems, whereas intrinsic motives characterize social-democratic contexts. Success requires aligning macro-level policy with firm-level adaptations to support older workers.…with generous references to
Working beyond retirement age in Germany: The employee’s perspective https://economicscience.net/publications/2011-les/
…individuals with lower income or wealth are more likely to continue working past the statutory retirement age
…financial security enables full labor market withdrawal
… functional capacity predicts the ability to continue working but not necessarily the intention to do so
… work motivation predicts willingness to remain employed beyond retirement age in Germany, partly through its positive association with self-reported work ability and openness to further education. Job rewards also increase willingness to continue working. -
Extending Working Lives: A Systematic Review of Motivations, Determinants, and Institutional Contexts
https://cris.maastrichtuniversity.nl/ws/files/307829408/ROA_TR_2026_2_Extending_Working_Lives.pdf
This review of 103 studies examines determinants of labor participation beyond #retirement age across diverse institutional contexts. Causal evidence indicates that pension reforms and tax incentives yield only modest impacts on extending working lives. Instead, employer practices and workplace flexibility act as primary determinants for feasible post-retirement employment. Findings reveal that financial necessity drives liberal systems, whereas intrinsic motives characterize social-democratic contexts. Success requires aligning macro-level policy with firm-level adaptations to support older workers.…with generous references to
Working beyond retirement age in Germany: The employee’s perspective https://economicscience.net/publications/2011-les/
…individuals with lower income or wealth are more likely to continue working past the statutory retirement age
…financial security enables full labor market withdrawal
… functional capacity predicts the ability to continue working but not necessarily the intention to do so
… work motivation predicts willingness to remain employed beyond retirement age in Germany, partly through its positive association with self-reported work ability and openness to further education. Job rewards also increase willingness to continue working. -
Extending Working Lives: A Systematic Review of Motivations, Determinants, and Institutional Contexts
https://cris.maastrichtuniversity.nl/ws/files/307829408/ROA_TR_2026_2_Extending_Working_Lives.pdf
This review of 103 studies examines determinants of labor participation beyond #retirement age across diverse institutional contexts. Causal evidence indicates that pension reforms and tax incentives yield only modest impacts on extending working lives. Instead, employer practices and workplace flexibility act as primary determinants for feasible post-retirement employment. Findings reveal that financial necessity drives liberal systems, whereas intrinsic motives characterize social-democratic contexts. Success requires aligning macro-level policy with firm-level adaptations to support older workers.…with generous references to
Working beyond retirement age in Germany: The employee’s perspective https://economicscience.net/publications/2011-les/
…individuals with lower income or wealth are more likely to continue working past the statutory retirement age
…financial security enables full labor market withdrawal
… functional capacity predicts the ability to continue working but not necessarily the intention to do so
… work motivation predicts willingness to remain employed beyond retirement age in Germany, partly through its positive association with self-reported work ability and openness to further education. Job rewards also increase willingness to continue working. -
Extending Working Lives: A Systematic Review of Motivations, Determinants, and Institutional Contexts
https://cris.maastrichtuniversity.nl/ws/files/307829408/ROA_TR_2026_2_Extending_Working_Lives.pdf
This review of 103 studies examines determinants of labor participation beyond #retirement age across diverse institutional contexts. Causal evidence indicates that pension reforms and tax incentives yield only modest impacts on extending working lives. Instead, employer practices and workplace flexibility act as primary determinants for feasible post-retirement employment. Findings reveal that financial necessity drives liberal systems, whereas intrinsic motives characterize social-democratic contexts. Success requires aligning macro-level policy with firm-level adaptations to support older workers.…with generous references to
Working beyond retirement age in Germany: The employee’s perspective https://economicscience.net/publications/2011-les/
…individuals with lower income or wealth are more likely to continue working past the statutory retirement age
…financial security enables full labor market withdrawal
… functional capacity predicts the ability to continue working but not necessarily the intention to do so
… work motivation predicts willingness to remain employed beyond retirement age in Germany, partly through its positive association with self-reported work ability and openness to further education. Job rewards also increase willingness to continue working. -
Do Firms Share their Profits Equally with Women and Men? The Role of Human Capital, Managerial Positions and Unions https://docs.iza.org/dp18388.pdf
"… wage-profit elasticity is estimated at 2.8% and is not statistically different for women and men. These non-differing elasticities therefore imply a non-significant price effect in the gender wage gap, which is estimated in our analysis at 15.6%
… higher human capital – measured here by education level or tenure – and holding a managerial position increase rent-sharing for both men and women
… Still, rent-sharing seems to fuel the gender wage gap, albeit to a fairly modest extent (at around 5% of the gender wage gap for our benchmark specification) through the channel of segregation (i.e. women are somewhat more concentrated in less profitable firms)"
#gpg #wages #LaborEconomics -
Do Firms Share their Profits Equally with Women and Men? The Role of Human Capital, Managerial Positions and Unions https://docs.iza.org/dp18388.pdf
"… wage-profit elasticity is estimated at 2.8% and is not statistically different for women and men. These non-differing elasticities therefore imply a non-significant price effect in the gender wage gap, which is estimated in our analysis at 15.6%
… higher human capital – measured here by education level or tenure – and holding a managerial position increase rent-sharing for both men and women
… Still, rent-sharing seems to fuel the gender wage gap, albeit to a fairly modest extent (at around 5% of the gender wage gap for our benchmark specification) through the channel of segregation (i.e. women are somewhat more concentrated in less profitable firms)"
#gpg #wages #LaborEconomics -
Do Firms Share their Profits Equally with Women and Men? The Role of Human Capital, Managerial Positions and Unions https://docs.iza.org/dp18388.pdf
"… wage-profit elasticity is estimated at 2.8% and is not statistically different for women and men. These non-differing elasticities therefore imply a non-significant price effect in the gender wage gap, which is estimated in our analysis at 15.6%
… higher human capital – measured here by education level or tenure – and holding a managerial position increase rent-sharing for both men and women
… Still, rent-sharing seems to fuel the gender wage gap, albeit to a fairly modest extent (at around 5% of the gender wage gap for our benchmark specification) through the channel of segregation (i.e. women are somewhat more concentrated in less profitable firms)"
#gpg #wages #LaborEconomics -
Do Firms Share their Profits Equally with Women and Men? The Role of Human Capital, Managerial Positions and Unions https://docs.iza.org/dp18388.pdf
"… wage-profit elasticity is estimated at 2.8% and is not statistically different for women and men. These non-differing elasticities therefore imply a non-significant price effect in the gender wage gap, which is estimated in our analysis at 15.6%
… higher human capital – measured here by education level or tenure – and holding a managerial position increase rent-sharing for both men and women
… Still, rent-sharing seems to fuel the gender wage gap, albeit to a fairly modest extent (at around 5% of the gender wage gap for our benchmark specification) through the channel of segregation (i.e. women are somewhat more concentrated in less profitable firms)"
#gpg #wages #LaborEconomics -
Do Firms Share their Profits Equally with Women and Men? The Role of Human Capital, Managerial Positions and Unions https://docs.iza.org/dp18388.pdf
"… wage-profit elasticity is estimated at 2.8% and is not statistically different for women and men. These non-differing elasticities therefore imply a non-significant price effect in the gender wage gap, which is estimated in our analysis at 15.6%
… higher human capital – measured here by education level or tenure – and holding a managerial position increase rent-sharing for both men and women
… Still, rent-sharing seems to fuel the gender wage gap, albeit to a fairly modest extent (at around 5% of the gender wage gap for our benchmark specification) through the channel of segregation (i.e. women are somewhat more concentrated in less profitable firms)"
#gpg #wages #LaborEconomics -
What makes this notable is the split between sentiment and behavior. Public frustration with tipping has risen sharply, yet restaurant gratuity levels remain relatively stable, suggesting social pressure still overrides irritation in many contexts.
#TippingCulture #TipFatigue #LaborEconomics #ConsumerBehavior #Hospitality #news #usa -
https://www.europesays.com/ie/361400/ Phenomenal Cosmic Power, Itty Bitty Labor Force? #AIAgents #ArtificialIntelligence #CareerDevelopment #CosmicPowers #DataScience #DisneyAladdin #Éire #IE #Ireland #Jafar #LaborEconomics #Technology #WageCompression #WallStreet
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Crítica de la economía algorítmica: La paradoja del aumento de carga laboral tras la implementación de sistemas de IA productiva en 2026. 🧠👾 🔗 https://www.glitchmental.com/2026/02/ia-productividad-paradoja-horas-2026.html #LaborEconomics #AI #FutureOfWork #GlitchMentalMX
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Turns Out They Didn’t Really Want You To Bring Your Whole Self To Work
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Turns Out They Didn’t Really Want You To Bring Your Whole Self To Work
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Turns Out They Didn’t Really Want You To Bring Your Whole Self To Work
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Turns Out They Didn’t Really Want You To Bring Your Whole Self To Work
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Turns Out They Didn’t Really Want You To Bring Your Whole Self To Work
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Interesting angle, but letting your best people walk just to avoid paying them more seems like self-sabotage. Sure, you keep “solid” workers at standard wages, but isn’t that how you end up with mediocrity baked in? #WorkCulture #HR #BusinessStrategy #LaborEconomics
Why top firms paradoxically fi...