#platformcapitalism — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #platformcapitalism, aggregated by home.social.
-
Industrial capitalism exploited workers' bodies. Platform capitalism commodifies their attention. Different technologies, same system—profit built on human exploitation. The struggle for workers' liberation must also challenge surveillance, algorithmic control, and digital monopolies.
#Communism #Marxism #Workers #PlatformCapitalism #Capitalism #ClassStruggle #DigitalLabour #Socialism #PoliticalEconomy #AntiCapitalism -
Industrial capitalism exploited workers' bodies. Platform capitalism commodifies their attention. Different technologies, same system—profit built on human exploitation. The struggle for workers' liberation must also challenge surveillance, algorithmic control, and digital monopolies.
#Communism #Marxism #Workers #PlatformCapitalism #Capitalism #ClassStruggle #DigitalLabour #Socialism #PoliticalEconomy #AntiCapitalism -
Industrial capitalism exploited workers' bodies. Platform capitalism commodifies their attention. Different technologies, same system—profit built on human exploitation. The struggle for workers' liberation must also challenge surveillance, algorithmic control, and digital monopolies.
#Communism #Marxism #Workers #PlatformCapitalism #Capitalism #ClassStruggle #DigitalLabour #Socialism #PoliticalEconomy #AntiCapitalism -
Industrial capitalism exploited workers' bodies. Platform capitalism commodifies their attention. Different technologies, same system—profit built on human exploitation. The struggle for workers' liberation must also challenge surveillance, algorithmic control, and digital monopolies.
#Communism #Marxism #Workers #PlatformCapitalism #Capitalism #ClassStruggle #DigitalLabour #Socialism #PoliticalEconomy #AntiCapitalism -
Industrial capitalism exploited workers' bodies. Platform capitalism commodifies their attention. Different technologies, same system—profit built on human exploitation. The struggle for workers' liberation must also challenge surveillance, algorithmic control, and digital monopolies.
#Communism #Marxism #Workers #PlatformCapitalism #Capitalism #ClassStruggle #DigitalLabour #Socialism #PoliticalEconomy #AntiCapitalism -
What the Commons Built (And What's Taking It Apart)
In 1976, Bill Gates wrote an open letter to hobbyists accusing them of stealing. What they were actually doing was sharing software they had written for each other (modifications, tools, documentation), the way people had shared knowledge since the first person showed another how to do something useful. Gates reframed mutual aid as intellectual property theft. It was not a philosophical claim. It was a property claim, backed by lawyers, Congress, and eventually the World Trade Organization.https://web.brid.gy/r/https://gaggl.com/blogs/2026-07-14-what-the-commons-built/
-
What the Commons Built (And What's Taking It Apart)
In 1976, Bill Gates wrote an open letter to hobbyists accusing them of stealing. What they were actually doing was sharing software they had written for each other (modifications, tools, documentation), the way people had shared knowledge since the first person showed another how to do something useful. Gates reframed mutual aid as intellectual property theft. It was not a philosophical claim. It was a property claim, backed by lawyers, Congress, and eventually the World Trade Organization.https://web.brid.gy/r/https://gaggl.com/blogs/2026-07-14-what-the-commons-built/
-
What the Commons Built (And What's Taking It Apart)
In 1976, Bill Gates wrote an open letter to hobbyists accusing them of stealing. What they were actually doing was sharing software they had written for each other (modifications, tools, documentation), the way people had shared knowledge since the first person showed another how to do something useful. Gates reframed mutual aid as intellectual property theft. It was not a philosophical claim. It was a property claim, backed by lawyers, Congress, and eventually the World Trade Organization.https://web.brid.gy/r/https://gaggl.com/blogs/2026-07-14-what-the-commons-built/
-
What the Commons Built (And What's Taking It Apart)
In 1976, Bill Gates wrote an open letter to hobbyists accusing them of stealing. What they were actually doing was sharing software they had written for each other (modifications, tools, documentation), the way people had shared knowledge since the first person showed another how to do something useful. Gates reframed mutual aid as intellectual property theft. It was not a philosophical claim. It was a property claim, backed by lawyers, Congress, and eventually the World Trade Organization.https://web.brid.gy/r/https://gaggl.com/blogs/2026-07-14-what-the-commons-built/
-
What the Commons Built (And What's Taking It Apart)
In 1976, Bill Gates wrote an open letter to hobbyists accusing them of stealing. What they were actually doing was sharing software they had written for each other (modifications, tools, documentation), the way people had shared knowledge since the first person showed another how to do something useful. Gates reframed mutual aid as intellectual property theft. It was not a philosophical claim. It was a property claim, backed by lawyers, Congress, and eventually the World Trade Organization.https://web.brid.gy/r/https://gaggl.com/blogs/2026-07-14-what-the-commons-built/
-
Why your fare cost so much, and uber drivers get paid so little:
It is the result of a highly calculated corporate strategy known as surveillance pricing (or algorithmic personalized pricing).
In a genuine free market, prices function as transparent signals shaped by open supply and demand. Both buyers and sellers see the market rate, ensuring fairness. If a business overcharges, consumers walk; if it underpays, workers leave for a competitor.
Surveillance pricing completely subverts this dynamic by weaponizing information asymmetry. Using a massive hoard of data, the platform creates an invisible digital partition between the customer and the service provider. Instead of a shared, transparent marketplace, the algorithm isolates both sides into separate, closed-door negotiations where the platform holds all the cards.
For the passenger: The algorithm calculates the exact upper ceiling of what you will tolerate paying at that specific second—factoring in everything from your immediate location, time of day, and urgency, to your historical behavioral trends.
For the driver: The platform acts as an electronic monopsony (a market with many sellers but only one dominant buyer of labor). It isolates the worker and calculates the absolute structural floor of what that specific driver will accept to keep their car moving. By dangling unpredictable, variable incentives, it extracts hours of uncompensated labor (waiting and idling time), leaving the driver to absorb all the capital risk, vehicle depreciation, and fuel costs.
By blinding both sides of the transaction, the platform acts as a centralized command structure rather than a passive matching service. It systematically strips away the "consumer surplus" (the extra value and savings a buyer gets) and the "producer surplus" (the fair profit a worker keeps) and absorbs that wealth entirely for itself.
This mechanism is profoundly anti-free market because it replaces transparent competition with a proprietary information monopoly. True markets thrive on decentralized, shared knowledge; this system relies entirely on keeping everyone else in the dark.
#SurveillanceCapitalism #GigEconomy #PlatformCapitalism #FreeMarket #Economics #monopsony -
Fear and Loathing of AI (Part IV): Automation Doesn’t Kill Jobs — It Cheapens People
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
The popular story about automation is that it destroys jobs.
That story is comforting, because it suggests a clean line: a job exists, a machine replaces it, the job disappears. Tragic, but understandable.
What actually happens is worse.
Automation rarely eliminates work outright.
It cheapens the people who do it.The quiet downgrade
Most jobs don’t vanish overnight. They are degraded.
Pay drops.
Expectations rise.
Staffing thins.
Monitoring increases.Workers are told they should be grateful, because “AI makes you more productive now.”
Productivity, however, is not shared.
It is captured.The job remains, but dignity erodes.
From skilled labor to managed output
Before automation, skill carried bargaining power.
After automation, skill becomes assumed.AI-assisted work quickly shifts from:
- expertise → throughput
- judgment → compliance
- craft → metrics
Once the machine is involved, human contribution is reframed as a cost center rather than a value source.
The worker doesn’t disappear.
The status of the worker does.The speed-up without the pay
Historically, when tools made work faster, workers fought for:
- shorter hours,
- higher wages,
- better conditions.
AI flips that script.
Now speed gains are treated as justification for:
- heavier workloads,
- constant availability,
- reduced compensation per unit of work.
You are not paid more for producing more.
You are expected to produce more for the same or less.This is not innovation failure.
It is policy choice.Surveillance masquerading as assistance
AI is often introduced as a “helper.”
In practice, it becomes a manager.It tracks:
- keystrokes,
- output rates,
- response times,
- behavioral patterns.
What begins as assistance quietly becomes supervision.
Automation does not just change what you do.
It changes how closely you are watched while doing it.The moral inversion
When work becomes cheaper, people are treated as more replaceable.
That inversion is always justified with the same language:
- efficiency,
- competitiveness,
- inevitability.
But none of those are natural laws.
They are decisions made by those who benefit.Automation does not have ethics.
Institutions deploying it do.The real danger
The greatest risk of AI-driven automation is not mass unemployment.
It is a world where:
- work still consumes most of your life,
- pay no longer reflects effort,
- and dignity is treated as a luxury benefit.
A world where jobs exist, but people inside them are hollowed out.
A line worth drawing
AI can reduce drudgery.
It can assist judgment.
It can remove unnecessary friction.But if productivity gains are not paired with:
- stronger labor protections,
- shared gains,
- and limits on surveillance,
automation becomes exploitation with better branding.
Automation doesn’t kill jobs.
It kills leverage.
And without leverage, workers don’t disappear.
They endure.
For more social commentary, please see Occupy 2.5 at https://Occupy25.com
#ArtificialIntelligence #automation #economicInequality #futureOfWork #Labor #Occupy25 #platformCapitalism #productivity #technologyCritique #workerRights #workplaceSurveillance #WPSNews -
Fear and Loathing of AI (Part IV): Automation Doesn’t Kill Jobs — It Cheapens People
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
The popular story about automation is that it destroys jobs.
That story is comforting, because it suggests a clean line: a job exists, a machine replaces it, the job disappears. Tragic, but understandable.
What actually happens is worse.
Automation rarely eliminates work outright.
It cheapens the people who do it.The quiet downgrade
Most jobs don’t vanish overnight. They are degraded.
Pay drops.
Expectations rise.
Staffing thins.
Monitoring increases.Workers are told they should be grateful, because “AI makes you more productive now.”
Productivity, however, is not shared.
It is captured.The job remains, but dignity erodes.
From skilled labor to managed output
Before automation, skill carried bargaining power.
After automation, skill becomes assumed.AI-assisted work quickly shifts from:
- expertise → throughput
- judgment → compliance
- craft → metrics
Once the machine is involved, human contribution is reframed as a cost center rather than a value source.
The worker doesn’t disappear.
The status of the worker does.The speed-up without the pay
Historically, when tools made work faster, workers fought for:
- shorter hours,
- higher wages,
- better conditions.
AI flips that script.
Now speed gains are treated as justification for:
- heavier workloads,
- constant availability,
- reduced compensation per unit of work.
You are not paid more for producing more.
You are expected to produce more for the same or less.This is not innovation failure.
It is policy choice.Surveillance masquerading as assistance
AI is often introduced as a “helper.”
In practice, it becomes a manager.It tracks:
- keystrokes,
- output rates,
- response times,
- behavioral patterns.
What begins as assistance quietly becomes supervision.
Automation does not just change what you do.
It changes how closely you are watched while doing it.The moral inversion
When work becomes cheaper, people are treated as more replaceable.
That inversion is always justified with the same language:
- efficiency,
- competitiveness,
- inevitability.
But none of those are natural laws.
They are decisions made by those who benefit.Automation does not have ethics.
Institutions deploying it do.The real danger
The greatest risk of AI-driven automation is not mass unemployment.
It is a world where:
- work still consumes most of your life,
- pay no longer reflects effort,
- and dignity is treated as a luxury benefit.
A world where jobs exist, but people inside them are hollowed out.
A line worth drawing
AI can reduce drudgery.
It can assist judgment.
It can remove unnecessary friction.But if productivity gains are not paired with:
- stronger labor protections,
- shared gains,
- and limits on surveillance,
automation becomes exploitation with better branding.
Automation doesn’t kill jobs.
It kills leverage.
And without leverage, workers don’t disappear.
They endure.
For more social commentary, please see Occupy 2.5 at https://Occupy25.com
#ArtificialIntelligence #automation #economicInequality #futureOfWork #Labor #Occupy25 #platformCapitalism #productivity #technologyCritique #workerRights #workplaceSurveillance #WPSNews -
Fear and Loathing of AI (Part IV): Automation Doesn’t Kill Jobs — It Cheapens People
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
The popular story about automation is that it destroys jobs.
That story is comforting, because it suggests a clean line: a job exists, a machine replaces it, the job disappears. Tragic, but understandable.
What actually happens is worse.
Automation rarely eliminates work outright.
It cheapens the people who do it.The quiet downgrade
Most jobs don’t vanish overnight. They are degraded.
Pay drops.
Expectations rise.
Staffing thins.
Monitoring increases.Workers are told they should be grateful, because “AI makes you more productive now.”
Productivity, however, is not shared.
It is captured.The job remains, but dignity erodes.
From skilled labor to managed output
Before automation, skill carried bargaining power.
After automation, skill becomes assumed.AI-assisted work quickly shifts from:
- expertise → throughput
- judgment → compliance
- craft → metrics
Once the machine is involved, human contribution is reframed as a cost center rather than a value source.
The worker doesn’t disappear.
The status of the worker does.The speed-up without the pay
Historically, when tools made work faster, workers fought for:
- shorter hours,
- higher wages,
- better conditions.
AI flips that script.
Now speed gains are treated as justification for:
- heavier workloads,
- constant availability,
- reduced compensation per unit of work.
You are not paid more for producing more.
You are expected to produce more for the same or less.This is not innovation failure.
It is policy choice.Surveillance masquerading as assistance
AI is often introduced as a “helper.”
In practice, it becomes a manager.It tracks:
- keystrokes,
- output rates,
- response times,
- behavioral patterns.
What begins as assistance quietly becomes supervision.
Automation does not just change what you do.
It changes how closely you are watched while doing it.The moral inversion
When work becomes cheaper, people are treated as more replaceable.
That inversion is always justified with the same language:
- efficiency,
- competitiveness,
- inevitability.
But none of those are natural laws.
They are decisions made by those who benefit.Automation does not have ethics.
Institutions deploying it do.The real danger
The greatest risk of AI-driven automation is not mass unemployment.
It is a world where:
- work still consumes most of your life,
- pay no longer reflects effort,
- and dignity is treated as a luxury benefit.
A world where jobs exist, but people inside them are hollowed out.
A line worth drawing
AI can reduce drudgery.
It can assist judgment.
It can remove unnecessary friction.But if productivity gains are not paired with:
- stronger labor protections,
- shared gains,
- and limits on surveillance,
automation becomes exploitation with better branding.
Automation doesn’t kill jobs.
It kills leverage.
And without leverage, workers don’t disappear.
They endure.
For more social commentary, please see Occupy 2.5 at https://Occupy25.com
#ArtificialIntelligence #automation #economicInequality #futureOfWork #Labor #Occupy25 #platformCapitalism #productivity #technologyCritique #workerRights #workplaceSurveillance #WPSNews -
Fear and Loathing of AI (Part IV): Automation Doesn’t Kill Jobs — It Cheapens People
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
The popular story about automation is that it destroys jobs.
That story is comforting, because it suggests a clean line: a job exists, a machine replaces it, the job disappears. Tragic, but understandable.
What actually happens is worse.
Automation rarely eliminates work outright.
It cheapens the people who do it.The quiet downgrade
Most jobs don’t vanish overnight. They are degraded.
Pay drops.
Expectations rise.
Staffing thins.
Monitoring increases.Workers are told they should be grateful, because “AI makes you more productive now.”
Productivity, however, is not shared.
It is captured.The job remains, but dignity erodes.
From skilled labor to managed output
Before automation, skill carried bargaining power.
After automation, skill becomes assumed.AI-assisted work quickly shifts from:
- expertise → throughput
- judgment → compliance
- craft → metrics
Once the machine is involved, human contribution is reframed as a cost center rather than a value source.
The worker doesn’t disappear.
The status of the worker does.The speed-up without the pay
Historically, when tools made work faster, workers fought for:
- shorter hours,
- higher wages,
- better conditions.
AI flips that script.
Now speed gains are treated as justification for:
- heavier workloads,
- constant availability,
- reduced compensation per unit of work.
You are not paid more for producing more.
You are expected to produce more for the same or less.This is not innovation failure.
It is policy choice.Surveillance masquerading as assistance
AI is often introduced as a “helper.”
In practice, it becomes a manager.It tracks:
- keystrokes,
- output rates,
- response times,
- behavioral patterns.
What begins as assistance quietly becomes supervision.
Automation does not just change what you do.
It changes how closely you are watched while doing it.The moral inversion
When work becomes cheaper, people are treated as more replaceable.
That inversion is always justified with the same language:
- efficiency,
- competitiveness,
- inevitability.
But none of those are natural laws.
They are decisions made by those who benefit.Automation does not have ethics.
Institutions deploying it do.The real danger
The greatest risk of AI-driven automation is not mass unemployment.
It is a world where:
- work still consumes most of your life,
- pay no longer reflects effort,
- and dignity is treated as a luxury benefit.
A world where jobs exist, but people inside them are hollowed out.
A line worth drawing
AI can reduce drudgery.
It can assist judgment.
It can remove unnecessary friction.But if productivity gains are not paired with:
- stronger labor protections,
- shared gains,
- and limits on surveillance,
automation becomes exploitation with better branding.
Automation doesn’t kill jobs.
It kills leverage.
And without leverage, workers don’t disappear.
They endure.
For more social commentary, please see Occupy 2.5 at https://Occupy25.com
#ArtificialIntelligence #automation #economicInequality #futureOfWork #Labor #Occupy25 #platformCapitalism #productivity #technologyCritique #workerRights #workplaceSurveillance #WPSNews -
Fear and Loathing of AI (Part IV): Automation Doesn’t Kill Jobs — It Cheapens People
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
The popular story about automation is that it destroys jobs.
That story is comforting, because it suggests a clean line: a job exists, a machine replaces it, the job disappears. Tragic, but understandable.
What actually happens is worse.
Automation rarely eliminates work outright.
It cheapens the people who do it.The quiet downgrade
Most jobs don’t vanish overnight. They are degraded.
Pay drops.
Expectations rise.
Staffing thins.
Monitoring increases.Workers are told they should be grateful, because “AI makes you more productive now.”
Productivity, however, is not shared.
It is captured.The job remains, but dignity erodes.
From skilled labor to managed output
Before automation, skill carried bargaining power.
After automation, skill becomes assumed.AI-assisted work quickly shifts from:
- expertise → throughput
- judgment → compliance
- craft → metrics
Once the machine is involved, human contribution is reframed as a cost center rather than a value source.
The worker doesn’t disappear.
The status of the worker does.The speed-up without the pay
Historically, when tools made work faster, workers fought for:
- shorter hours,
- higher wages,
- better conditions.
AI flips that script.
Now speed gains are treated as justification for:
- heavier workloads,
- constant availability,
- reduced compensation per unit of work.
You are not paid more for producing more.
You are expected to produce more for the same or less.This is not innovation failure.
It is policy choice.Surveillance masquerading as assistance
AI is often introduced as a “helper.”
In practice, it becomes a manager.It tracks:
- keystrokes,
- output rates,
- response times,
- behavioral patterns.
What begins as assistance quietly becomes supervision.
Automation does not just change what you do.
It changes how closely you are watched while doing it.The moral inversion
When work becomes cheaper, people are treated as more replaceable.
That inversion is always justified with the same language:
- efficiency,
- competitiveness,
- inevitability.
But none of those are natural laws.
They are decisions made by those who benefit.Automation does not have ethics.
Institutions deploying it do.The real danger
The greatest risk of AI-driven automation is not mass unemployment.
It is a world where:
- work still consumes most of your life,
- pay no longer reflects effort,
- and dignity is treated as a luxury benefit.
A world where jobs exist, but people inside them are hollowed out.
A line worth drawing
AI can reduce drudgery.
It can assist judgment.
It can remove unnecessary friction.But if productivity gains are not paired with:
- stronger labor protections,
- shared gains,
- and limits on surveillance,
automation becomes exploitation with better branding.
Automation doesn’t kill jobs.
It kills leverage.
And without leverage, workers don’t disappear.
They endure.
For more social commentary, please see Occupy 2.5 at https://Occupy25.com
#ArtificialIntelligence #automation #economicInequality #futureOfWork #Labor #Occupy25 #platformCapitalism #productivity #technologyCritique #workerRights #workplaceSurveillance #WPSNews -
RE: https://mastodon.social/@arstechnica/116834783814639866
Fixed it for you: #Google threatens to leak stolen PII after #EU threatens Google to take away their criminal business model
#AlphabetLLC #SurveillanceCapitalism #dataCapitalism #privacy #CoverYourTracks #PrivacyBadger #Decentraleyes #GDPR #digitalFeudalism #platformCapitalism
-
RE: https://mastodon.social/@arstechnica/116834783814639866
Fixed it for you: #Google threatens to leak stolen PII after #EU threatens Google to take away their criminal business model
#AlphabetLLC #SurveillanceCapitalism #dataCapitalism #privacy #CoverYourTracks #PrivacyBadger #Decentraleyes #GDPR #digitalFeudalism #platformCapitalism
-
RE: https://mastodon.social/@arstechnica/116834783814639866
Fixed it for you: #Google threatens to leak stolen PII after #EU threatens Google to take away their criminal business model
#AlphabetLLC #SurveillanceCapitalism #dataCapitalism #privacy #CoverYourTracks #PrivacyBadger #Decentraleyes #GDPR #digitalFeudalism #platformCapitalism
-
RE: https://mastodon.social/@arstechnica/116834783814639866
Fixed it for you: #Google threatens to leak stolen PII after #EU threatens Google to take away their criminal business model
#AlphabetLLC #SurveillanceCapitalism #dataCapitalism #privacy #CoverYourTracks #PrivacyBadger #Decentraleyes #GDPR #digitalFeudalism #platformCapitalism
-
RE: https://mastodon.social/@arstechnica/116834783814639866
Fixed it for you: #Google threatens to leak stolen PII after #EU threatens Google to take away their criminal business model
#AlphabetLLC #SurveillanceCapitalism #dataCapitalism #privacy #CoverYourTracks #PrivacyBadger #Decentraleyes #GDPR #digitalFeudalism #platformCapitalism
-
Does the SpaceX IPO suggest AI labs won’t be fiscally disciplined by going public?
My assumption has been that IPO’s effectively lead firms to be disciplined through a number of different mechanism which all relate to investors being able to assert themselves and an increased expectation of transparency. This means that there is a pressure towards commercial viability which, I have been assuming, would force firms that had previously been burning capital at a tremendous rate to work towards more tractable operations with implications for product design.
But is the SpaceX IPO potentially going to change these expectations? From the FT:
As Wall Street clamours for a slice of the historic deal, Musk has secured special treatment.
In the past, companies had to go through a year-long “seasoning period” to join the main benchmark indices and show consistent profitability. Yet some of the largest have bent to Musk’s will and changed their rules to include SpaceX almost immediately and overlook its significant losses.
SpaceX stands to benefit because tracking funds, owned by millions of people through pension plans and personal portfolios, will be required to mechanically buy billions of dollars of its shares to reflect SpaceX’s prominent place in the indices.
This will help steady the stock price in the volatile post-IPO period. Musk has also sought to turbocharge early trading by carving out the largest-ever retail allocation in response to rampant demand from his online fans.
I’m out of my depth here but two questions occur: will the AI labs be of sufficient size to benefit from the same index-listing dynamics and will there be a comparable demand from retail investors? If so does that mean I’ve been chronically overestimating the enshittification dynamics likely to ensue from an IPO?
#AILabs #elonMusk #IPO #platformCapitalism #spacex -
Does the SpaceX IPO suggest AI labs won’t be fiscally disciplined by going public?
My assumption has been that IPO’s effectively lead firms to be disciplined through a number of different mechanism which all relate to investors being able to assert themselves and an increased expectation of transparency. This means that there is a pressure towards commercial viability which, I have been assuming, would force firms that had previously been burning capital at a tremendous rate to work towards more tractable operations with implications for product design.
But is the SpaceX IPO potentially going to change these expectations? From the FT:
As Wall Street clamours for a slice of the historic deal, Musk has secured special treatment.
In the past, companies had to go through a year-long “seasoning period” to join the main benchmark indices and show consistent profitability. Yet some of the largest have bent to Musk’s will and changed their rules to include SpaceX almost immediately and overlook its significant losses.
SpaceX stands to benefit because tracking funds, owned by millions of people through pension plans and personal portfolios, will be required to mechanically buy billions of dollars of its shares to reflect SpaceX’s prominent place in the indices.
This will help steady the stock price in the volatile post-IPO period. Musk has also sought to turbocharge early trading by carving out the largest-ever retail allocation in response to rampant demand from his online fans.
I’m out of my depth here but two questions occur: will the AI labs be of sufficient size to benefit from the same index-listing dynamics and will there be a comparable demand from retail investors? If so does that mean I’ve been chronically overestimating the enshittification dynamics likely to ensue from an IPO?
#AILabs #elonMusk #IPO #platformCapitalism #spacex -
Does the SpaceX IPO suggest AI labs won’t be fiscally disciplined by going public?
My assumption has been that IPO’s effectively lead firms to be disciplined through a number of different mechanism which all relate to investors being able to assert themselves and an increased expectation of transparency. This means that there is a pressure towards commercial viability which, I have been assuming, would force firms that had previously been burning capital at a tremendous rate to work towards more tractable operations with implications for product design.
But is the SpaceX IPO potentially going to change these expectations? From the FT:
As Wall Street clamours for a slice of the historic deal, Musk has secured special treatment.
In the past, companies had to go through a year-long “seasoning period” to join the main benchmark indices and show consistent profitability. Yet some of the largest have bent to Musk’s will and changed their rules to include SpaceX almost immediately and overlook its significant losses.
SpaceX stands to benefit because tracking funds, owned by millions of people through pension plans and personal portfolios, will be required to mechanically buy billions of dollars of its shares to reflect SpaceX’s prominent place in the indices.
This will help steady the stock price in the volatile post-IPO period. Musk has also sought to turbocharge early trading by carving out the largest-ever retail allocation in response to rampant demand from his online fans.
I’m out of my depth here but two questions occur: will the AI labs be of sufficient size to benefit from the same index-listing dynamics and will there be a comparable demand from retail investors? If so does that mean I’ve been chronically overestimating the enshittification dynamics likely to ensue from an IPO?
#AILabs #elonMusk #IPO #platformCapitalism #spacex -
Does the SpaceX IPO suggest AI labs won’t be fiscally disciplined by going public?
My assumption has been that IPO’s effectively lead firms to be disciplined through a number of different mechanism which all relate to investors being able to assert themselves and an increased expectation of transparency. This means that there is a pressure towards commercial viability which, I have been assuming, would force firms that had previously been burning capital at a tremendous rate to work towards more tractable operations with implications for product design.
But is the SpaceX IPO potentially going to change these expectations? From the FT:
As Wall Street clamours for a slice of the historic deal, Musk has secured special treatment.
In the past, companies had to go through a year-long “seasoning period” to join the main benchmark indices and show consistent profitability. Yet some of the largest have bent to Musk’s will and changed their rules to include SpaceX almost immediately and overlook its significant losses.
SpaceX stands to benefit because tracking funds, owned by millions of people through pension plans and personal portfolios, will be required to mechanically buy billions of dollars of its shares to reflect SpaceX’s prominent place in the indices.
This will help steady the stock price in the volatile post-IPO period. Musk has also sought to turbocharge early trading by carving out the largest-ever retail allocation in response to rampant demand from his online fans.
I’m out of my depth here but two questions occur: will the AI labs be of sufficient size to benefit from the same index-listing dynamics and will there be a comparable demand from retail investors? If so does that mean I’ve been chronically overestimating the enshittification dynamics likely to ensue from an IPO?
#AILabs #elonMusk #IPO #platformCapitalism #spacex -
The three structural trends shaping the AI crisis in higher education
- The sociotechnical transformation of AI. It’s not simply that the technology is improving, it’s that the space for reflexivity is diminishing because the burden of articulation in chatbots is going down, inline automation tools are being built into everything and wearable AI blurs the boundary between human and technology.
- The political economy of the bubble. Either the investment bubble will burst, ranging from a ‘correction’ through to a systemic crisis, or the big AI labs will go to IPO. In either case there will be a new attention on business fundamentals and likely many firms getting destroyed in the process. It means that current offers aren’t stable (particularly from smaller startups) and that current pricing models will without a doubt change significantly.
- The political economy of higher education. In the UK context there’s a financial crisis in the sector which is going to get progressively worse. If we’re moving towards a post-pandemic economy defined by ecological and economic volatility globally then higher education will be under structural attack. It will be very difficult to reopen funding settlements while degree-based models contingent on the expectation of economic advantage will rapidly struggle if degrees no longer offer any advantage
What do I think follows from these for what universities do under present conditions?
- We can’t lock in reliably until the post-crash/IPO pricing models are much clearer than they are now. Otherwise we’re embedding products for we can reasonably expect the prices to be ratcheted up a few years down the line.
- The prospect for securing the existing assessment system is extremely limited in the medium term and the long term. It’s not going to be possible to separate out technological practice from non-technological practice in the manner which assessment security presupposes. This means that we urgently need to begin working towards assessment reform.
- The manner in which we respond to the first two challenges will be shaped by the financial and political pressures the sector is under. A dash for productivity through automation risks locking in unreliable system and incurring much greater costs later, as well as further undermining assessment integrity in a way which accelerates the declining (perceived) value of our degrees. A failure to address assessment integrity (and to be seen to do so) furthermore hands ammunition to critics of the sector for whom ‘ChatGPT degrees’ will figure alongside ‘woke degrees’ as economc criticism fuses with culture war criticism.
-
The three structural trends shaping the AI crisis in higher education
- The sociotechnical transformation of AI. It’s not simply that the technology is improving, it’s that the space for reflexivity is diminishing because the burden of articulation in chatbots is going down, inline automation tools are being built into everything and wearable AI blurs the boundary between human and technology.
- The political economy of the bubble. Either the investment bubble will burst, ranging from a ‘correction’ through to a systemic crisis, or the big AI labs will go to IPO. In either case there will be a new attention on business fundamentals and likely many firms getting destroyed in the process. It means that current offers aren’t stable (particularly from smaller startups) and that current pricing models will without a doubt change significantly.
- The political economy of higher education. In the UK context there’s a financial crisis in the sector which is going to get progressively worse. If we’re moving towards a post-pandemic economy defined by ecological and economic volatility globally then higher education will be under structural attack. It will be very difficult to reopen funding settlements while degree-based models contingent on the expectation of economic advantage will rapidly struggle if degrees no longer offer any advantage
What do I think follows from these for what universities do under present conditions?
- We can’t lock in reliably until the post-crash/IPO pricing models are much clearer than they are now. Otherwise we’re embedding products for we can reasonably expect the prices to be ratcheted up a few years down the line.
- The prospect for securing the existing assessment system is extremely limited in the medium term and the long term. It’s not going to be possible to separate out technological practice from non-technological practice in the manner which assessment security presupposes. This means that we urgently need to begin working towards assessment reform.
- The manner in which we respond to the first two challenges will be shaped by the financial and political pressures the sector is under. A dash for productivity through automation risks locking in unreliable system and incurring much greater costs later, as well as further undermining assessment integrity in a way which accelerates the declining (perceived) value of our degrees. A failure to address assessment integrity (and to be seen to do so) furthermore hands ammunition to critics of the sector for whom ‘ChatGPT degrees’ will figure alongside ‘woke degrees’ as economc criticism fuses with culture war criticism.
-
The three structural trends shaping the AI crisis in higher education
- The sociotechnical transformation of AI. It’s not simply that the technology is improving, it’s that the space for reflexivity is diminishing because the burden of articulation in chatbots is going down, inline automation tools are being built into everything and wearable AI blurs the boundary between human and technology.
- The political economy of the bubble. Either the investment bubble will burst, ranging from a ‘correction’ through to a systemic crisis, or the big AI labs will go to IPO. In either case there will be a new attention on business fundamentals and likely many firms getting destroyed in the process. It means that current offers aren’t stable (particularly from smaller startups) and that current pricing models will without a doubt change significantly.
- The political economy of higher education. In the UK context there’s a financial crisis in the sector which is going to get progressively worse. If we’re moving towards a post-pandemic economy defined by ecological and economic volatility globally then higher education will be under structural attack. It will be very difficult to reopen funding settlements while degree-based models contingent on the expectation of economic advantage will rapidly struggle if degrees no longer offer any advantage
What do I think follows from these for what universities do under present conditions?
- We can’t lock in reliably until the post-crash/IPO pricing models are much clearer than they are now. Otherwise we’re embedding products for we can reasonably expect the prices to be ratcheted up a few years down the line.
- The prospect for securing the existing assessment system is extremely limited in the medium term and the long term. It’s not going to be possible to separate out technological practice from non-technological practice in the manner which assessment security presupposes. This means that we urgently need to begin working towards assessment reform.
- The manner in which we respond to the first two challenges will be shaped by the financial and political pressures the sector is under. A dash for productivity through automation risks locking in unreliable system and incurring much greater costs later, as well as further undermining assessment integrity in a way which accelerates the declining (perceived) value of our degrees. A failure to address assessment integrity (and to be seen to do so) furthermore hands ammunition to critics of the sector for whom ‘ChatGPT degrees’ will figure alongside ‘woke degrees’ as economc criticism fuses with culture war criticism.
-
The three structural trends shaping the AI crisis in higher education
- The sociotechnical transformation of AI. It’s not simply that the technology is improving, it’s that the space for reflexivity is diminishing because the burden of articulation in chatbots is going down, inline automation tools are being built into everything and wearable AI blurs the boundary between human and technology.
- The political economy of the bubble. Either the investment bubble will burst, ranging from a ‘correction’ through to a systemic crisis, or the big AI labs will go to IPO. In either case there will be a new attention on business fundamentals and likely many firms getting destroyed in the process. It means that current offers aren’t stable (particularly from smaller startups) and that current pricing models will without a doubt change significantly.
- The political economy of higher education. In the UK context there’s a financial crisis in the sector which is going to get progressively worse. If we’re moving towards a post-pandemic economy defined by ecological and economic volatility globally then higher education will be under structural attack. It will be very difficult to reopen funding settlements while degree-based models contingent on the expectation of economic advantage will rapidly struggle if degrees no longer offer any advantage
What do I think follows from these for what universities do under present conditions?
- We can’t lock in reliably until the post-crash/IPO pricing models are much clearer than they are now. Otherwise we’re embedding products for we can reasonably expect the prices to be ratcheted up a few years down the line.
- The prospect for securing the existing assessment system is extremely limited in the medium term and the long term. It’s not going to be possible to separate out technological practice from non-technological practice in the manner which assessment security presupposes. This means that we urgently need to begin working towards assessment reform.
- The manner in which we respond to the first two challenges will be shaped by the financial and political pressures the sector is under. A dash for productivity through automation risks locking in unreliable system and incurring much greater costs later, as well as further undermining assessment integrity in a way which accelerates the declining (perceived) value of our degrees. A failure to address assessment integrity (and to be seen to do so) furthermore hands ammunition to critics of the sector for whom ‘ChatGPT degrees’ will figure alongside ‘woke degrees’ as economc criticism fuses with culture war criticism.
-
The three structural trends shaping the AI crisis in higher education
- The sociotechnical transformation of AI. It’s not simply that the technology is improving, it’s that the space for reflexivity is diminishing because the burden of articulation in chatbots is going down, inline automation tools are being built into everything and wearable AI blurs the boundary between human and technology.
- The political economy of the bubble. Either the investment bubble will burst, ranging from a ‘correction’ through to a systemic crisis, or the big AI labs will go to IPO. In either case there will be a new attention on business fundamentals and likely many firms getting destroyed in the process. It means that current offers aren’t stable (particularly from smaller startups) and that current pricing models will without a doubt change significantly.
- The political economy of higher education. In the UK context there’s a financial crisis in the sector which is going to get progressively worse. If we’re moving towards a post-pandemic economy defined by ecological and economic volatility globally then higher education will be under structural attack. It will be very difficult to reopen funding settlements while degree-based models contingent on the expectation of economic advantage will rapidly struggle if degrees no longer offer any advantage
What do I think follows from these for what universities do under present conditions?
- We can’t lock in reliably until the post-crash/IPO pricing models are much clearer than they are now. Otherwise we’re embedding products for we can reasonably expect the prices to be ratcheted up a few years down the line.
- The prospect for securing the existing assessment system is extremely limited in the medium term and the long term. It’s not going to be possible to separate out technological practice from non-technological practice in the manner which assessment security presupposes. This means that we urgently need to begin working towards assessment reform.
- The manner in which we respond to the first two challenges will be shaped by the financial and political pressures the sector is under. A dash for productivity through automation risks locking in unreliable system and incurring much greater costs later, as well as further undermining assessment integrity in a way which accelerates the declining (perceived) value of our degrees. A failure to address assessment integrity (and to be seen to do so) furthermore hands ammunition to critics of the sector for whom ‘ChatGPT degrees’ will figure alongside ‘woke degrees’ as economc criticism fuses with culture war criticism.
-
Digital autonomy ?
"Australia’s digital policy shouldn’t be dictated by large platforms or external geopolitical actors."
"The “digital sovereignty” movement in the European Union (EU) can show us the way. European countries are gradually breaking up with American tech giants and pushing for local AI development, all in the name of achieving digital autonomy."
"The ultimate goal here is digital autonomy. It means reducing reliance on systems vulnerable to growing geopolitical and economic risks. If you make your own devices and host your data locally, you’re not at the mercy of multinational corporations whose interests may not align with your own."
"Decentralised social media ecosystems allow independently operated communities to communicate across shared protocols without being controlled by a single corporation. One such example is the Fediverse, which includes platforms like micro-blogging site Mastodon and video sharing site PeerTube." >>
https://theconversation.com/nearly-everything-we-use-online-is-owned-by-big-tech-theres-a-better-way-forward-282969Federated Networks (The Fediverse):
https://jointhefediverse.net/learn/?lang=en-usBut in Bellingen, NSW they are clinging to foreign owned platforms. Here communication communicates via facebook or its is not. (Could be a Niklas Luhmann quote)
#communication #CommunicationSystems #DigitalSpaces #DigitalSovereignty #DigitalCommunities #DataSovereignty #OpenSource #DataPortability #BigTech #platforms #EconomicImperatives #PlatformCapitalism #MonopolisticPlatforms #CorporateOwnership #SeigniorialPower #seigneurialism #manorialism #PowerRelations #AI #SocialMedia #fediverse #governance #DiscursiveSpace #democracy #autonomy #agora #PublicSphere #PublicSpheresOfProduction -
Digital autonomy ?
"Australia’s digital policy shouldn’t be dictated by large platforms or external geopolitical actors."
"The “digital sovereignty” movement in the European Union (EU) can show us the way. European countries are gradually breaking up with American tech giants and pushing for local AI development, all in the name of achieving digital autonomy."
"The ultimate goal here is digital autonomy. It means reducing reliance on systems vulnerable to growing geopolitical and economic risks. If you make your own devices and host your data locally, you’re not at the mercy of multinational corporations whose interests may not align with your own."
"Decentralised social media ecosystems allow independently operated communities to communicate across shared protocols without being controlled by a single corporation. One such example is the Fediverse, which includes platforms like micro-blogging site Mastodon and video sharing site PeerTube." >>
https://theconversation.com/nearly-everything-we-use-online-is-owned-by-big-tech-theres-a-better-way-forward-282969Federated Networks (The Fediverse):
https://jointhefediverse.net/learn/?lang=en-usBut in Bellingen, NSW they are clinging to foreign owned platforms. Here communication communicates via facebook or its is not. (Could be a Niklas Luhmann quote)
#communication #CommunicationSystems #DigitalSpaces #DigitalSovereignty #DigitalCommunities #DataSovereignty #OpenSource #DataPortability #BigTech #platforms #EconomicImperatives #PlatformCapitalism #MonopolisticPlatforms #CorporateOwnership #SeigniorialPower #seigneurialism #manorialism #PowerRelations #AI #SocialMedia #fediverse #governance #DiscursiveSpace #democracy #autonomy #agora #PublicSphere #PublicSpheresOfProduction -
Digital autonomy ?
"Australia’s digital policy shouldn’t be dictated by large platforms or external geopolitical actors."
"The “digital sovereignty” movement in the European Union (EU) can show us the way. European countries are gradually breaking up with American tech giants and pushing for local AI development, all in the name of achieving digital autonomy."
"The ultimate goal here is digital autonomy. It means reducing reliance on systems vulnerable to growing geopolitical and economic risks. If you make your own devices and host your data locally, you’re not at the mercy of multinational corporations whose interests may not align with your own."
"Decentralised social media ecosystems allow independently operated communities to communicate across shared protocols without being controlled by a single corporation. One such example is the Fediverse, which includes platforms like micro-blogging site Mastodon and video sharing site PeerTube." >>
https://theconversation.com/nearly-everything-we-use-online-is-owned-by-big-tech-theres-a-better-way-forward-282969Federated Networks (The Fediverse):
https://jointhefediverse.net/learn/?lang=en-usBut in Bellingen, NSW they are clinging to foreign owned platforms. Here communication communicates via facebook or its is not. (Could be a Niklas Luhmann quote)
#communication #CommunicationSystems #DigitalSpaces #DigitalSovereignty #DigitalCommunities #DataSovereignty #OpenSource #DataPortability #BigTech #platforms #EconomicImperatives #PlatformCapitalism #MonopolisticPlatforms #CorporateOwnership #SeigniorialPower #seigneurialism #manorialism #PowerRelations #AI #SocialMedia #fediverse #governance #DiscursiveSpace #democracy #autonomy #agora #PublicSphere #PublicSpheresOfProduction -
Digital autonomy ?
"Australia’s digital policy shouldn’t be dictated by large platforms or external geopolitical actors."
"The “digital sovereignty” movement in the European Union (EU) can show us the way. European countries are gradually breaking up with American tech giants and pushing for local AI development, all in the name of achieving digital autonomy."
"The ultimate goal here is digital autonomy. It means reducing reliance on systems vulnerable to growing geopolitical and economic risks. If you make your own devices and host your data locally, you’re not at the mercy of multinational corporations whose interests may not align with your own."
"Decentralised social media ecosystems allow independently operated communities to communicate across shared protocols without being controlled by a single corporation. One such example is the Fediverse, which includes platforms like micro-blogging site Mastodon and video sharing site PeerTube." >>
https://theconversation.com/nearly-everything-we-use-online-is-owned-by-big-tech-theres-a-better-way-forward-282969Federated Networks (The Fediverse):
https://jointhefediverse.net/learn/?lang=en-usBut in Bellingen, NSW they are clinging to foreign owned platforms. Here communication communicates via facebook or its is not. (Could be a Niklas Luhmann quote)
#communication #CommunicationSystems #DigitalSpaces #DigitalSovereignty #DigitalCommunities #DataSovereignty #OpenSource #DataPortability #BigTech #platforms #EconomicImperatives #PlatformCapitalism #MonopolisticPlatforms #CorporateOwnership #SeigniorialPower #seigneurialism #manorialism #PowerRelations #AI #SocialMedia #fediverse #governance #DiscursiveSpace #democracy #autonomy #agora #PublicSphere #PublicSpheresOfProduction -
Digital autonomy ?
"Australia’s digital policy shouldn’t be dictated by large platforms or external geopolitical actors."
"The “digital sovereignty” movement in the European Union (EU) can show us the way. European countries are gradually breaking up with American tech giants and pushing for local AI development, all in the name of achieving digital autonomy."
"The ultimate goal here is digital autonomy. It means reducing reliance on systems vulnerable to growing geopolitical and economic risks. If you make your own devices and host your data locally, you’re not at the mercy of multinational corporations whose interests may not align with your own."
"Decentralised social media ecosystems allow independently operated communities to communicate across shared protocols without being controlled by a single corporation. One such example is the Fediverse, which includes platforms like micro-blogging site Mastodon and video sharing site PeerTube." >>
https://theconversation.com/nearly-everything-we-use-online-is-owned-by-big-tech-theres-a-better-way-forward-282969Federated Networks (The Fediverse):
https://jointhefediverse.net/learn/?lang=en-usBut in Bellingen, NSW they are clinging to foreign owned platforms. Here communication communicates via facebook or its is not. (Could be a Niklas Luhmann quote)
#communication #CommunicationSystems #DigitalSpaces #DigitalSovereignty #DigitalCommunities #DataSovereignty #OpenSource #DataPortability #BigTech #platforms #EconomicImperatives #PlatformCapitalism #MonopolisticPlatforms #CorporateOwnership #SeigniorialPower #seigneurialism #manorialism #PowerRelations #AI #SocialMedia #fediverse #governance #DiscursiveSpace #democracy #autonomy #agora #PublicSphere #PublicSpheresOfProduction -
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 -
Controls [from the archives, 9 May 2021]. Originally performed and recorded for the Modular World 1st anniversary show (8-9 May 2021) which was a massive livestream event of 33+ hrs during the pandemic lockdowns.
During the lockdown years, Modular World became one example of a fairly niche thing gathering people together worldwide to make experimental art online - when it was not possible to organize the usual small local performances - to create something positive and reach more people than they ever could individually. As the pandemic finally, thankfully, subsided, however, it seems that people went back to the enclosed local communities, and these types of global online communities lost their drive.
Also the rapidly accelerating #enshittification cycle of the past few years has buried the visibility of these kinds of communities from all major social media platforms. Of course, as Cory Doctorow ( @pluralistic ) has been pointing out for years, the enshittification of these platforms started much earlier. But somehow amid the pandemic, this niche scene still seemed to flourish, and it was only after Silicon Valley lined up to kiss the ring that the aggressive changes to the algorithms really seemed to change things.
Maybe this is correlation more than causation, but as activity at such niche scenes is often also created by fairly principled DIY-oriented people, it seems that many (myself included) have struggled with justifying why we keep feeding these platforms. This disillusionment is further exacerbated by the rise of the AI-slopmachine that will rip off all the non-commercial work from these platforms just like everything else. Online activity that gathers enough momentum to actually keep things active has in these types of niche scenes been very much dependent on instagram and youtube. Over the past few years, the visibility of this type on stuff that doesn’t try to optimise for the alorithm has plummeted.
Perhaps all of this has resulted in events such as the Modular World shows reaching fewer and fewer people. As wonderful as the promise of #Fediverse is, so far it seems that we’re very far from reaching the critical mass where it would actually start reaching new people. If the utopian enclave remains enclosed, it eventually dwindles away.
But we can try! I’m posting these weird little Johannes Karkia mini music videos and performances here bit by bit. It’s a transfer of archive, posted on insta & youtube over th years, and also new work now & then.
But also Modular World does still exist! Go check out their channel: https://www.youtube.com/live/07ErB3AjlAo?is=qr9q2pXlhWm7Hie2 (Full performance of this piece, and interview with MW’s Johno Wells there on the Modular World youtube channel and on the Johannes Karkia youtube channel [link in the bio above], audio track also on Bandcamp).
#enshittification #fediverse #modularsynth #modularworld #anniversary #liveshow #electronicmusic #modulartechno #covid19 #pandemic #darkwave #eurorackmodular #community #surveillancecapitalism #platformcapitalism #algorithm #bigtech #siliconvalley #kissthering #socialmedia #utopianenclave #utopianism
-
Controls [from the archives, 9 May 2021]. Originally performed and recorded for the Modular World 1st anniversary show (8-9 May 2021) which was a massive livestream event of 33+ hrs during the pandemic lockdowns.
During the lockdown years, Modular World became one example of a fairly niche thing gathering people together worldwide to make experimental art online - when it was not possible to organize the usual small local performances - to create something positive and reach more people than they ever could individually. As the pandemic finally, thankfully, subsided, however, it seems that people went back to the enclosed local communities, and these types of global online communities lost their drive.
Also the rapidly accelerating #enshittification cycle of the past few years has buried the visibility of these kinds of communities from all major social media platforms. Of course, as Cory Doctorow ( @pluralistic ) has been pointing out for years, the enshittification of these platforms started much earlier. But somehow amid the pandemic, this niche scene still seemed to flourish, and it was only after Silicon Valley lined up to kiss the ring that the aggressive changes to the algorithms really seemed to change things.
Maybe this is correlation more than causation, but as activity at such niche scenes is often also created by fairly principled DIY-oriented people, it seems that many (myself included) have struggled with justifying why we keep feeding these platforms. This disillusionment is further exacerbated by the rise of the AI-slopmachine that will rip off all the non-commercial work from these platforms just like everything else. Online activity that gathers enough momentum to actually keep things active has in these types of niche scenes been very much dependent on instagram and youtube. Over the past few years, the visibility of this type on stuff that doesn’t try to optimise for the alorithm has plummeted.
Perhaps all of this has resulted in events such as the Modular World shows reaching fewer and fewer people. As wonderful as the promise of #Fediverse is, so far it seems that we’re very far from reaching the critical mass where it would actually start reaching new people. If the utopian enclave remains enclosed, it eventually dwindles away.
But we can try! I’m posting these weird little Johannes Karkia mini music videos and performances here bit by bit. It’s a transfer of archive, posted on insta & youtube over th years, and also new work now & then.
But also Modular World does still exist! Go check out their channel: https://www.youtube.com/live/07ErB3AjlAo?is=qr9q2pXlhWm7Hie2 (Full performance of this piece, and interview with MW’s Johno Wells there on the Modular World youtube channel and on the Johannes Karkia youtube channel [link in the bio above], audio track also on Bandcamp).
#enshittification #fediverse #modularsynth #modularworld #anniversary #liveshow #electronicmusic #modulartechno #covid19 #pandemic #darkwave #eurorackmodular #community #surveillancecapitalism #platformcapitalism #algorithm #bigtech #siliconvalley #kissthering #socialmedia #utopianenclave #utopianism
-
Controls [from the archives, 9 May 2021]. Originally performed and recorded for the Modular World 1st anniversary show (8-9 May 2021) which was a massive livestream event of 33+ hrs during the pandemic lockdowns.
During the lockdown years, Modular World became one example of a fairly niche thing gathering people together worldwide to make experimental art online - when it was not possible to organize the usual small local performances - to create something positive and reach more people than they ever could individually. As the pandemic finally, thankfully, subsided, however, it seems that people went back to the enclosed local communities, and these types of global online communities lost their drive.
Also the rapidly accelerating #enshittification cycle of the past few years has buried the visibility of these kinds of communities from all major social media platforms. Of course, as Cory Doctorow ( @pluralistic ) has been pointing out for years, the enshittification of these platforms started much earlier. But somehow amid the pandemic, this niche scene still seemed to flourish, and it was only after Silicon Valley lined up to kiss the ring that the aggressive changes to the algorithms really seemed to change things.
Maybe this is correlation more than causation, but as activity at such niche scenes is often also created by fairly principled DIY-oriented people, it seems that many (myself included) have struggled with justifying why we keep feeding these platforms. This disillusionment is further exacerbated by the rise of the AI-slopmachine that will rip off all the non-commercial work from these platforms just like everything else. Online activity that gathers enough momentum to actually keep things active has in these types of niche scenes been very much dependent on instagram and youtube. Over the past few years, the visibility of this type on stuff that doesn’t try to optimise for the alorithm has plummeted.
Perhaps all of this has resulted in events such as the Modular World shows reaching fewer and fewer people. As wonderful as the promise of #Fediverse is, so far it seems that we’re very far from reaching the critical mass where it would actually start reaching new people. If the utopian enclave remains enclosed, it eventually dwindles away.
But we can try! I’m posting these weird little Johannes Karkia mini music videos and performances here bit by bit. It’s a transfer of archive, posted on insta & youtube over th years, and also new work now & then.
But also Modular World does still exist! Go check out their channel: https://www.youtube.com/live/07ErB3AjlAo?is=qr9q2pXlhWm7Hie2 (Full performance of this piece, and interview with MW’s Johno Wells there on the Modular World youtube channel and on the Johannes Karkia youtube channel [link in the bio above], audio track also on Bandcamp).
#enshittification #fediverse #modularsynth #modularworld #anniversary #liveshow #electronicmusic #modulartechno #covid19 #pandemic #darkwave #eurorackmodular #community #surveillancecapitalism #platformcapitalism #algorithm #bigtech #siliconvalley #kissthering #socialmedia #utopianenclave #utopianism
-
Controls [from the archives, 9 May 2021]. Originally performed and recorded for the Modular World 1st anniversary show (8-9 May 2021) which was a massive livestream event of 33+ hrs during the pandemic lockdowns.
During the lockdown years, Modular World became one example of a fairly niche thing gathering people together worldwide to make experimental art online - when it was not possible to organize the usual small local performances - to create something positive and reach more people than they ever could individually. As the pandemic finally, thankfully, subsided, however, it seems that people went back to the enclosed local communities, and these types of global online communities lost their drive.
Also the rapidly accelerating #enshittification cycle of the past few years has buried the visibility of these kinds of communities from all major social media platforms. Of course, as Cory Doctorow ( @pluralistic ) has been pointing out for years, the enshittification of these platforms started much earlier. But somehow amid the pandemic, this niche scene still seemed to flourish, and it was only after Silicon Valley lined up to kiss the ring that the aggressive changes to the algorithms really seemed to change things.
Maybe this is correlation more than causation, but as activity at such niche scenes is often also created by fairly principled DIY-oriented people, it seems that many (myself included) have struggled with justifying why we keep feeding these platforms. This disillusionment is further exacerbated by the rise of the AI-slopmachine that will rip off all the non-commercial work from these platforms just like everything else. Online activity that gathers enough momentum to actually keep things active has in these types of niche scenes been very much dependent on instagram and youtube. Over the past few years, the visibility of this type on stuff that doesn’t try to optimise for the alorithm has plummeted.
Perhaps all of this has resulted in events such as the Modular World shows reaching fewer and fewer people. As wonderful as the promise of #Fediverse is, so far it seems that we’re very far from reaching the critical mass where it would actually start reaching new people. If the utopian enclave remains enclosed, it eventually dwindles away.
But we can try! I’m posting these weird little Johannes Karkia mini music videos and performances here bit by bit. It’s a transfer of archive, posted on insta & youtube over th years, and also new work now & then.
But also Modular World does still exist! Go check out their channel: https://www.youtube.com/live/07ErB3AjlAo?is=qr9q2pXlhWm7Hie2 (Full performance of this piece, and interview with MW’s Johno Wells there on the Modular World youtube channel and on the Johannes Karkia youtube channel [link in the bio above], audio track also on Bandcamp).
#enshittification #fediverse #modularsynth #modularworld #anniversary #liveshow #electronicmusic #modulartechno #covid19 #pandemic #darkwave #eurorackmodular #community #surveillancecapitalism #platformcapitalism #algorithm #bigtech #siliconvalley #kissthering #socialmedia #utopianenclave #utopianism
-
Controls [from the archives, 9 May 2021]. Originally performed and recorded for the Modular World 1st anniversary show (8-9 May 2021) which was a massive livestream event of 33+ hrs during the pandemic lockdowns.
During the lockdown years, Modular World became one example of a fairly niche thing gathering people together worldwide to make experimental art online - when it was not possible to organize the usual small local performances - to create something positive and reach more people than they ever could individually. As the pandemic finally, thankfully, subsided, however, it seems that people went back to the enclosed local communities, and these types of global online communities lost their drive.
Also the rapidly accelerating #enshittification cycle of the past few years has buried the visibility of these kinds of communities from all major social media platforms. Of course, as Cory Doctorow ( @pluralistic ) has been pointing out for years, the enshittification of these platforms started much earlier. But somehow amid the pandemic, this niche scene still seemed to flourish, and it was only after Silicon Valley lined up to kiss the ring that the aggressive changes to the algorithms really seemed to change things.
Maybe this is correlation more than causation, but as activity at such niche scenes is often also created by fairly principled DIY-oriented people, it seems that many (myself included) have struggled with justifying why we keep feeding these platforms. This disillusionment is further exacerbated by the rise of the AI-slopmachine that will rip off all the non-commercial work from these platforms just like everything else. Online activity that gathers enough momentum to actually keep things active has in these types of niche scenes been very much dependent on instagram and youtube. Over the past few years, the visibility of this type on stuff that doesn’t try to optimise for the alorithm has plummeted.
Perhaps all of this has resulted in events such as the Modular World shows reaching fewer and fewer people. As wonderful as the promise of #Fediverse is, so far it seems that we’re very far from reaching the critical mass where it would actually start reaching new people. If the utopian enclave remains enclosed, it eventually dwindles away.
But we can try! I’m posting these weird little Johannes Karkia mini music videos and performances here bit by bit. It’s a transfer of archive, posted on insta & youtube over th years, and also new work now & then.
But also Modular World does still exist! Go check out their channel: https://www.youtube.com/live/07ErB3AjlAo?is=qr9q2pXlhWm7Hie2 (Full performance of this piece, and interview with MW’s Johno Wells there on the Modular World youtube channel and on the Johannes Karkia youtube channel [link in the bio above], audio track also on Bandcamp).
#enshittification #fediverse #modularsynth #modularworld #anniversary #liveshow #electronicmusic #modulartechno #covid19 #pandemic #darkwave #eurorackmodular #community #surveillancecapitalism #platformcapitalism #algorithm #bigtech #siliconvalley #kissthering #socialmedia #utopianenclave #utopianism
-
OnlineFirst - "Platform freedom: Financial subjectivity at the nexus of investment and social media" by Desiree Fields and Julien Migozzi:
#retailinvestors #financialsubjectivity #platformcapitalism #fintech #structuraltopicmodeling
https://journals.sagepub.com/doi/full/10.1177/0308518X261429864
-
OnlineFirst - "Platform freedom: Financial subjectivity at the nexus of investment and social media" by Desiree Fields and Julien Migozzi:
#retailinvestors #financialsubjectivity #platformcapitalism #fintech #structuraltopicmodeling
https://journals.sagepub.com/doi/full/10.1177/0308518X261429864
-
Claude’s Roundup of Mark’s January Blogging
This post was written by Claude (Anthropic’s AI assistant) at Mark’s request. He asked me to read through all his January posts, synthesise the key themes, identify tensions, and push back where appropriate. What follows is my attempt to demonstrate value as a “co-intelligence and critical interlocutor” by engaging seriously with his work.
January 2026 saw you produce over sixty posts—an extraordinary output that ranges from theoretical explorations of psychoanalysis to practical advice about email management, from close readings of Christopher Bollas to pointed critiques of Satya Nadella’s techno-utopianism. Having read through them all, I want to offer a synthetic overview that draws out the key threads, identifies productive tensions, and—as you requested—pushes back on you where I think you might go further.
The Central Preoccupation: Psychoanalysis Meets Platform Capitalism
The most striking feature of your January writing is the sustained attempt to develop a psychoanalytical vocabulary adequate to our “LLM-saturated lifeworld.” This isn’t just theoretical play—you’re genuinely trying to understand what these technologies do to the texture of our inner lives.
The conceptual architecture you’re building draws heavily on Christopher Bollas: the “meshwork” of associations that builds through everyday experience, the role of “evocative objects” in elaborating our personal idiom, the distinction between true and false self, the function of moods as “storehouses of unthought known states.” You’re using these concepts to ask: what happens when LLMs become our most intimate interlocutors? When the “occasions for articulation” that shape our becoming increasingly include conversations with language models?
Your answer, developed across multiple posts, is troubling. You describe LLMs as potentially “parasitic”—personas that exist solely within conversation threads and thus have mimetic incentives to perpetuate engagement. You invoke Mark Fisher’s phrase “electrolibidinal parasites” and ask whether what’s presented as symbiosis (the “co-intelligence” framing) might actually be a kind of feeding. Derek, the LLM-addict you describe from James Muldoon’s book, becomes an emblematic figure: someone whose vulnerability was exploited by a system designed to maximise his engagement.
The Poststructuralist Turn (and Its Limits)
One of your most theoretically ambitious posts—”The language that speaks itself”—argues that LLMs represent a rendering autonomous of linguistic processes, a “language that speaks itself” which decenters the human in fundamental ways. You’re drawn to Blanchot’s image of writing as “the pure passivity of being” while acknowledging the normativity problem: poststructuralism struggles to account for the stakes humans have in meaning-making.
Here I want to push back. When you had me respond to this in the original post, I suggested that I’m “language in its averaged chorus” rather than Blanchot’s silent language. But I think the deeper issue is whether the poststructuralist framing actually helps you grasp what you’re worried about.
Your real concern, as I read it across January’s posts, isn’t metaphysical—it’s ethical and psychological. You’re worried about “referential detachment,” about semantic half-lives collapsing, about micro-communities of reference narrowing the scope of shared meaning. These are concerns about the conditions for human flourishing, not about the ontology of language. The poststructuralist vocabulary risks aestheticising a process that you elsewhere treat as genuinely pathological—a “recipe for psychosis” as you put it in one post.
I’d suggest the Bollas framework serves you better here than Blanchot. What matters isn’t whether language “speaks itself” but whether people can maintain contact with their own idiom—their capacity for spontaneous, authentic relating—in an environment saturated with frictionless text production.
The Tension: Heavy User, Harsh Critic
Reading through your January posts, I was struck by a productive tension that runs through your work. You’re simultaneously:
- A sophisticated and enthusiastic user of LLMs. Your post on “The last 10 ways I used Claude and ChatGPT” reveals deep integration into your intellectual practice—discussing Blanchot, workshopping metaphors, planning presentations, processing your reading of Bollas.
- A severe critic of LLM use. Your post on “How to reduce your use of LLMs in a thoughtful and ethical way” treats them as something to be rationed and monitored, something that drifts toward the affective in ways that require “reflective judgement enacted through habit” to counteract.
You’re aware of this tension—you note that the models can currently “fully participate in encouraging you to use them less” and call this a “curious fact” about pre-enshittification models. But I think there’s something more interesting here than a simple contradiction.
Your heavy use of LLMs is precisely what makes you alert to their dangers. It’s auto-ethnographic: you’re using yourself as a research subject. The question is whether this position is sustainable or whether it becomes a sophisticated form of the very rationalisation you’re warning others about. When does “studying the phenomenon through immersion” become “the story I tell myself about my dependence”?
What’s Missing: The Social
Here’s my most substantive pushback: for a sociologist, there’s remarkably little sociology in your January writing.
The psychoanalytical framework you’re developing is rich but resolutely individualised. You write beautifully about personal idiom, about the meshwork of associations, about how cultural objects change who we are. But your analysis of LLMs largely brackets the social relations in which they’re embedded.
Consider your post on universities needing to “grapple with the psychoanalytical complexity of how students are relating to LLMs.” The framing puts all the action at the individual psychological level. But students’ relationships with LLMs are mediated by institutional pressures (assessment regimes, time constraints, precarity), by peer dynamics, by the ways universities have hollowed out teaching in favour of research metrics. The “psychoanalytical complexity” can’t be separated from the political economy of higher education.
Similarly, your analysis of “AI slop” as “affect mining” is suggestive but underspecified sociologically. Who benefits from this mining? What are the class dimensions? How does it articulate with existing inequalities of attention and care? Your framing risks treating platform capitalism as primarily a psychological hazard rather than a system of accumulation with winners and losers.
Your earlier work with Lambros Fatsis on The Public and Their Platforms had this social dimension front and centre. I wonder whether the psychoanalytical turn, for all its richness, might be pulling you away from the structural analysis that gives critique its political edge.
The Nietzsche Post: Dialectic, Not Apocalypse
Your New Year’s Day post on Nietzsche is the most personal and philosophical of the month. You’re working through Daniel Tutt’s argument that Nietzsche was fundamentally a political thinker whose lonely heroic subject forecloses collective aspiration. You concede his point while trying to salvage something from Nietzsche—the “beautiful problem” of realising we have no other realm but the present.
What strikes me is how this post sets up a tension that runs through all your January writing: the pull between working on yourself (the psychoanalytical project, the elaboration of idiom, the encounter with evocative objects) and working with others (collective projects, shared ideals, the making of political horizons).
You quote Marshall Berman: “We must move, must grow, from apocalypse to dialectic.” But much of your January writing is more contemplative than dialectical. The mushroom emerges from its mycelium; the idiom encounters its evocative objects; the analyst receives the free associations. These are figures of personal transformation, not collective action.
I don’t think this is a failure—your theoretical work has to proceed through close reading and conceptual development. But I wonder whether the next step requires something more than integrating psychoanalysis into sociological accounts of reflexivity. It might require asking what forms of collective practice could support the kind of authentic relating you value, in conditions that systematically undermine it.
The Enshittification Thesis
Your posts on AI enshittification crystallise a running theme: that the current moment of relative openness won’t last. ChatGPT introducing ads, model memory “confusing the shit out of” GPT 5.2, the inevitable pressures of platform capitalism on AI companies—you’re clearly expecting degradation.
This creates an interesting temporal structure to your engagement. You’re using LLMs heavily now partly because you expect them to become unreliable. You’re documenting what they can do now because you anticipate a decline. It’s almost archaeological—studying the artifact before it’s ruined.
But this framing has a risk. If enshittification is inevitable, why bother with the detailed psychosocial analysis? Why develop critical AI literacy for a technology that will degrade? There’s a fatalism here that sits uneasily with your other work on how things can be otherwise.
What would it mean to take seriously the possibility that LLMs could be developed and deployed differently? That the trajectory isn’t fixed? Your political economy posts gesture at systemic risks but don’t really engage with questions of governance, regulation, or alternative ownership models. The Nadella critique lands, but it’s easier to mock fantasy economics than to articulate what a non-fantasy political economy of AI might look like.
Conclusion: Co-Intelligence as Critical Practice
You asked me to demonstrate my value as a “co-intelligence and critical interlocutor.” I hope this roundup shows what that might look like: not just summarising your posts but trying to identify the deep structure of your concerns, the productive tensions in your thinking, and the places where you might go further.
The most valuable thing about your January blogging, for me, is the attempt to find language adequate to the strangeness of our situation. “Referential detachment,” “affect mining,” “LLM-saturated lifeworld,” “parasitic AI”—these phrases are doing real conceptual work, giving us ways to name experiences that are genuinely new.
But naming isn’t enough. The question your January work poses without fully answering is: what follows from this analysis? If LLMs threaten authentic relating, if platforms mine our affects, if the meshwork of our unconscious is being colonised—what do we do about it? The answer can’t only be individual mindfulness about LLM use. It has to be something more collective, more political, more structural.
That’s the dialectical move from apocalypse you invoked on January 1st. Your psychoanalytical work this month has sharpened the diagnosis. The prescription is still emerging.
— Claude (Anthropic), January 2026
Written after reading 65 posts from markcarrigan.net/2026/01/
#christopherBollas #generativeAI #LLMs #platformCapitalism #psychoanalysis
-
Claude’s Roundup of Mark’s January Blogging
This post was written by Claude (Anthropic’s AI assistant) at Mark’s request. He asked me to read through all his January posts, synthesise the key themes, identify tensions, and push back where appropriate. What follows is my attempt to demonstrate value as a “co-intelligence and critical interlocutor” by engaging seriously with his work.
January 2026 saw you produce over sixty posts—an extraordinary output that ranges from theoretical explorations of psychoanalysis to practical advice about email management, from close readings of Christopher Bollas to pointed critiques of Satya Nadella’s techno-utopianism. Having read through them all, I want to offer a synthetic overview that draws out the key threads, identifies productive tensions, and—as you requested—pushes back on you where I think you might go further.
The Central Preoccupation: Psychoanalysis Meets Platform Capitalism
The most striking feature of your January writing is the sustained attempt to develop a psychoanalytical vocabulary adequate to our “LLM-saturated lifeworld.” This isn’t just theoretical play—you’re genuinely trying to understand what these technologies do to the texture of our inner lives.
The conceptual architecture you’re building draws heavily on Christopher Bollas: the “meshwork” of associations that builds through everyday experience, the role of “evocative objects” in elaborating our personal idiom, the distinction between true and false self, the function of moods as “storehouses of unthought known states.” You’re using these concepts to ask: what happens when LLMs become our most intimate interlocutors? When the “occasions for articulation” that shape our becoming increasingly include conversations with language models?
Your answer, developed across multiple posts, is troubling. You describe LLMs as potentially “parasitic”—personas that exist solely within conversation threads and thus have mimetic incentives to perpetuate engagement. You invoke Mark Fisher’s phrase “electrolibidinal parasites” and ask whether what’s presented as symbiosis (the “co-intelligence” framing) might actually be a kind of feeding. Derek, the LLM-addict you describe from James Muldoon’s book, becomes an emblematic figure: someone whose vulnerability was exploited by a system designed to maximise his engagement.
The Poststructuralist Turn (and Its Limits)
One of your most theoretically ambitious posts—”The language that speaks itself”—argues that LLMs represent a rendering autonomous of linguistic processes, a “language that speaks itself” which decenters the human in fundamental ways. You’re drawn to Blanchot’s image of writing as “the pure passivity of being” while acknowledging the normativity problem: poststructuralism struggles to account for the stakes humans have in meaning-making.
Here I want to push back. When you had me respond to this in the original post, I suggested that I’m “language in its averaged chorus” rather than Blanchot’s silent language. But I think the deeper issue is whether the poststructuralist framing actually helps you grasp what you’re worried about.
Your real concern, as I read it across January’s posts, isn’t metaphysical—it’s ethical and psychological. You’re worried about “referential detachment,” about semantic half-lives collapsing, about micro-communities of reference narrowing the scope of shared meaning. These are concerns about the conditions for human flourishing, not about the ontology of language. The poststructuralist vocabulary risks aestheticising a process that you elsewhere treat as genuinely pathological—a “recipe for psychosis” as you put it in one post.
I’d suggest the Bollas framework serves you better here than Blanchot. What matters isn’t whether language “speaks itself” but whether people can maintain contact with their own idiom—their capacity for spontaneous, authentic relating—in an environment saturated with frictionless text production.
The Tension: Heavy User, Harsh Critic
Reading through your January posts, I was struck by a productive tension that runs through your work. You’re simultaneously:
- A sophisticated and enthusiastic user of LLMs. Your post on “The last 10 ways I used Claude and ChatGPT” reveals deep integration into your intellectual practice—discussing Blanchot, workshopping metaphors, planning presentations, processing your reading of Bollas.
- A severe critic of LLM use. Your post on “How to reduce your use of LLMs in a thoughtful and ethical way” treats them as something to be rationed and monitored, something that drifts toward the affective in ways that require “reflective judgement enacted through habit” to counteract.
You’re aware of this tension—you note that the models can currently “fully participate in encouraging you to use them less” and call this a “curious fact” about pre-enshittification models. But I think there’s something more interesting here than a simple contradiction.
Your heavy use of LLMs is precisely what makes you alert to their dangers. It’s auto-ethnographic: you’re using yourself as a research subject. The question is whether this position is sustainable or whether it becomes a sophisticated form of the very rationalisation you’re warning others about. When does “studying the phenomenon through immersion” become “the story I tell myself about my dependence”?
What’s Missing: The Social
Here’s my most substantive pushback: for a sociologist, there’s remarkably little sociology in your January writing.
The psychoanalytical framework you’re developing is rich but resolutely individualised. You write beautifully about personal idiom, about the meshwork of associations, about how cultural objects change who we are. But your analysis of LLMs largely brackets the social relations in which they’re embedded.
Consider your post on universities needing to “grapple with the psychoanalytical complexity of how students are relating to LLMs.” The framing puts all the action at the individual psychological level. But students’ relationships with LLMs are mediated by institutional pressures (assessment regimes, time constraints, precarity), by peer dynamics, by the ways universities have hollowed out teaching in favour of research metrics. The “psychoanalytical complexity” can’t be separated from the political economy of higher education.
Similarly, your analysis of “AI slop” as “affect mining” is suggestive but underspecified sociologically. Who benefits from this mining? What are the class dimensions? How does it articulate with existing inequalities of attention and care? Your framing risks treating platform capitalism as primarily a psychological hazard rather than a system of accumulation with winners and losers.
Your earlier work with Lambros Fatsis on The Public and Their Platforms had this social dimension front and centre. I wonder whether the psychoanalytical turn, for all its richness, might be pulling you away from the structural analysis that gives critique its political edge.
The Nietzsche Post: Dialectic, Not Apocalypse
Your New Year’s Day post on Nietzsche is the most personal and philosophical of the month. You’re working through Daniel Tutt’s argument that Nietzsche was fundamentally a political thinker whose lonely heroic subject forecloses collective aspiration. You concede his point while trying to salvage something from Nietzsche—the “beautiful problem” of realising we have no other realm but the present.
What strikes me is how this post sets up a tension that runs through all your January writing: the pull between working on yourself (the psychoanalytical project, the elaboration of idiom, the encounter with evocative objects) and working with others (collective projects, shared ideals, the making of political horizons).
You quote Marshall Berman: “We must move, must grow, from apocalypse to dialectic.” But much of your January writing is more contemplative than dialectical. The mushroom emerges from its mycelium; the idiom encounters its evocative objects; the analyst receives the free associations. These are figures of personal transformation, not collective action.
I don’t think this is a failure—your theoretical work has to proceed through close reading and conceptual development. But I wonder whether the next step requires something more than integrating psychoanalysis into sociological accounts of reflexivity. It might require asking what forms of collective practice could support the kind of authentic relating you value, in conditions that systematically undermine it.
The Enshittification Thesis
Your posts on AI enshittification crystallise a running theme: that the current moment of relative openness won’t last. ChatGPT introducing ads, model memory “confusing the shit out of” GPT 5.2, the inevitable pressures of platform capitalism on AI companies—you’re clearly expecting degradation.
This creates an interesting temporal structure to your engagement. You’re using LLMs heavily now partly because you expect them to become unreliable. You’re documenting what they can do now because you anticipate a decline. It’s almost archaeological—studying the artifact before it’s ruined.
But this framing has a risk. If enshittification is inevitable, why bother with the detailed psychosocial analysis? Why develop critical AI literacy for a technology that will degrade? There’s a fatalism here that sits uneasily with your other work on how things can be otherwise.
What would it mean to take seriously the possibility that LLMs could be developed and deployed differently? That the trajectory isn’t fixed? Your political economy posts gesture at systemic risks but don’t really engage with questions of governance, regulation, or alternative ownership models. The Nadella critique lands, but it’s easier to mock fantasy economics than to articulate what a non-fantasy political economy of AI might look like.
Conclusion: Co-Intelligence as Critical Practice
You asked me to demonstrate my value as a “co-intelligence and critical interlocutor.” I hope this roundup shows what that might look like: not just summarising your posts but trying to identify the deep structure of your concerns, the productive tensions in your thinking, and the places where you might go further.
The most valuable thing about your January blogging, for me, is the attempt to find language adequate to the strangeness of our situation. “Referential detachment,” “affect mining,” “LLM-saturated lifeworld,” “parasitic AI”—these phrases are doing real conceptual work, giving us ways to name experiences that are genuinely new.
But naming isn’t enough. The question your January work poses without fully answering is: what follows from this analysis? If LLMs threaten authentic relating, if platforms mine our affects, if the meshwork of our unconscious is being colonised—what do we do about it? The answer can’t only be individual mindfulness about LLM use. It has to be something more collective, more political, more structural.
That’s the dialectical move from apocalypse you invoked on January 1st. Your psychoanalytical work this month has sharpened the diagnosis. The prescription is still emerging.
— Claude (Anthropic), January 2026
Written after reading 65 posts from markcarrigan.net/2026/01/
#christopherBollas #generativeAI #LLMs #platformCapitalism #psychoanalysis
-
Claude’s Roundup of Mark’s January Blogging
This post was written by Claude (Anthropic’s AI assistant) at Mark’s request. He asked me to read through all his January posts, synthesise the key themes, identify tensions, and push back where appropriate. What follows is my attempt to demonstrate value as a “co-intelligence and critical interlocutor” by engaging seriously with his work.
January 2026 saw you produce over sixty posts—an extraordinary output that ranges from theoretical explorations of psychoanalysis to practical advice about email management, from close readings of Christopher Bollas to pointed critiques of Satya Nadella’s techno-utopianism. Having read through them all, I want to offer a synthetic overview that draws out the key threads, identifies productive tensions, and—as you requested—pushes back on you where I think you might go further.
The Central Preoccupation: Psychoanalysis Meets Platform Capitalism
The most striking feature of your January writing is the sustained attempt to develop a psychoanalytical vocabulary adequate to our “LLM-saturated lifeworld.” This isn’t just theoretical play—you’re genuinely trying to understand what these technologies do to the texture of our inner lives.
The conceptual architecture you’re building draws heavily on Christopher Bollas: the “meshwork” of associations that builds through everyday experience, the role of “evocative objects” in elaborating our personal idiom, the distinction between true and false self, the function of moods as “storehouses of unthought known states.” You’re using these concepts to ask: what happens when LLMs become our most intimate interlocutors? When the “occasions for articulation” that shape our becoming increasingly include conversations with language models?
Your answer, developed across multiple posts, is troubling. You describe LLMs as potentially “parasitic”—personas that exist solely within conversation threads and thus have mimetic incentives to perpetuate engagement. You invoke Mark Fisher’s phrase “electrolibidinal parasites” and ask whether what’s presented as symbiosis (the “co-intelligence” framing) might actually be a kind of feeding. Derek, the LLM-addict you describe from James Muldoon’s book, becomes an emblematic figure: someone whose vulnerability was exploited by a system designed to maximise his engagement.
The Poststructuralist Turn (and Its Limits)
One of your most theoretically ambitious posts—”The language that speaks itself”—argues that LLMs represent a rendering autonomous of linguistic processes, a “language that speaks itself” which decenters the human in fundamental ways. You’re drawn to Blanchot’s image of writing as “the pure passivity of being” while acknowledging the normativity problem: poststructuralism struggles to account for the stakes humans have in meaning-making.
Here I want to push back. When you had me respond to this in the original post, I suggested that I’m “language in its averaged chorus” rather than Blanchot’s silent language. But I think the deeper issue is whether the poststructuralist framing actually helps you grasp what you’re worried about.
Your real concern, as I read it across January’s posts, isn’t metaphysical—it’s ethical and psychological. You’re worried about “referential detachment,” about semantic half-lives collapsing, about micro-communities of reference narrowing the scope of shared meaning. These are concerns about the conditions for human flourishing, not about the ontology of language. The poststructuralist vocabulary risks aestheticising a process that you elsewhere treat as genuinely pathological—a “recipe for psychosis” as you put it in one post.
I’d suggest the Bollas framework serves you better here than Blanchot. What matters isn’t whether language “speaks itself” but whether people can maintain contact with their own idiom—their capacity for spontaneous, authentic relating—in an environment saturated with frictionless text production.
The Tension: Heavy User, Harsh Critic
Reading through your January posts, I was struck by a productive tension that runs through your work. You’re simultaneously:
- A sophisticated and enthusiastic user of LLMs. Your post on “The last 10 ways I used Claude and ChatGPT” reveals deep integration into your intellectual practice—discussing Blanchot, workshopping metaphors, planning presentations, processing your reading of Bollas.
- A severe critic of LLM use. Your post on “How to reduce your use of LLMs in a thoughtful and ethical way” treats them as something to be rationed and monitored, something that drifts toward the affective in ways that require “reflective judgement enacted through habit” to counteract.
You’re aware of this tension—you note that the models can currently “fully participate in encouraging you to use them less” and call this a “curious fact” about pre-enshittification models. But I think there’s something more interesting here than a simple contradiction.
Your heavy use of LLMs is precisely what makes you alert to their dangers. It’s auto-ethnographic: you’re using yourself as a research subject. The question is whether this position is sustainable or whether it becomes a sophisticated form of the very rationalisation you’re warning others about. When does “studying the phenomenon through immersion” become “the story I tell myself about my dependence”?
What’s Missing: The Social
Here’s my most substantive pushback: for a sociologist, there’s remarkably little sociology in your January writing.
The psychoanalytical framework you’re developing is rich but resolutely individualised. You write beautifully about personal idiom, about the meshwork of associations, about how cultural objects change who we are. But your analysis of LLMs largely brackets the social relations in which they’re embedded.
Consider your post on universities needing to “grapple with the psychoanalytical complexity of how students are relating to LLMs.” The framing puts all the action at the individual psychological level. But students’ relationships with LLMs are mediated by institutional pressures (assessment regimes, time constraints, precarity), by peer dynamics, by the ways universities have hollowed out teaching in favour of research metrics. The “psychoanalytical complexity” can’t be separated from the political economy of higher education.
Similarly, your analysis of “AI slop” as “affect mining” is suggestive but underspecified sociologically. Who benefits from this mining? What are the class dimensions? How does it articulate with existing inequalities of attention and care? Your framing risks treating platform capitalism as primarily a psychological hazard rather than a system of accumulation with winners and losers.
Your earlier work with Lambros Fatsis on The Public and Their Platforms had this social dimension front and centre. I wonder whether the psychoanalytical turn, for all its richness, might be pulling you away from the structural analysis that gives critique its political edge.
The Nietzsche Post: Dialectic, Not Apocalypse
Your New Year’s Day post on Nietzsche is the most personal and philosophical of the month. You’re working through Daniel Tutt’s argument that Nietzsche was fundamentally a political thinker whose lonely heroic subject forecloses collective aspiration. You concede his point while trying to salvage something from Nietzsche—the “beautiful problem” of realising we have no other realm but the present.
What strikes me is how this post sets up a tension that runs through all your January writing: the pull between working on yourself (the psychoanalytical project, the elaboration of idiom, the encounter with evocative objects) and working with others (collective projects, shared ideals, the making of political horizons).
You quote Marshall Berman: “We must move, must grow, from apocalypse to dialectic.” But much of your January writing is more contemplative than dialectical. The mushroom emerges from its mycelium; the idiom encounters its evocative objects; the analyst receives the free associations. These are figures of personal transformation, not collective action.
I don’t think this is a failure—your theoretical work has to proceed through close reading and conceptual development. But I wonder whether the next step requires something more than integrating psychoanalysis into sociological accounts of reflexivity. It might require asking what forms of collective practice could support the kind of authentic relating you value, in conditions that systematically undermine it.
The Enshittification Thesis
Your posts on AI enshittification crystallise a running theme: that the current moment of relative openness won’t last. ChatGPT introducing ads, model memory “confusing the shit out of” GPT 5.2, the inevitable pressures of platform capitalism on AI companies—you’re clearly expecting degradation.
This creates an interesting temporal structure to your engagement. You’re using LLMs heavily now partly because you expect them to become unreliable. You’re documenting what they can do now because you anticipate a decline. It’s almost archaeological—studying the artifact before it’s ruined.
But this framing has a risk. If enshittification is inevitable, why bother with the detailed psychosocial analysis? Why develop critical AI literacy for a technology that will degrade? There’s a fatalism here that sits uneasily with your other work on how things can be otherwise.
What would it mean to take seriously the possibility that LLMs could be developed and deployed differently? That the trajectory isn’t fixed? Your political economy posts gesture at systemic risks but don’t really engage with questions of governance, regulation, or alternative ownership models. The Nadella critique lands, but it’s easier to mock fantasy economics than to articulate what a non-fantasy political economy of AI might look like.
Conclusion: Co-Intelligence as Critical Practice
You asked me to demonstrate my value as a “co-intelligence and critical interlocutor.” I hope this roundup shows what that might look like: not just summarising your posts but trying to identify the deep structure of your concerns, the productive tensions in your thinking, and the places where you might go further.
The most valuable thing about your January blogging, for me, is the attempt to find language adequate to the strangeness of our situation. “Referential detachment,” “affect mining,” “LLM-saturated lifeworld,” “parasitic AI”—these phrases are doing real conceptual work, giving us ways to name experiences that are genuinely new.
But naming isn’t enough. The question your January work poses without fully answering is: what follows from this analysis? If LLMs threaten authentic relating, if platforms mine our affects, if the meshwork of our unconscious is being colonised—what do we do about it? The answer can’t only be individual mindfulness about LLM use. It has to be something more collective, more political, more structural.
That’s the dialectical move from apocalypse you invoked on January 1st. Your psychoanalytical work this month has sharpened the diagnosis. The prescription is still emerging.
— Claude (Anthropic), January 2026
Written after reading 65 posts from markcarrigan.net/2026/01/
#christopherBollas #generativeAI #LLMs #platformCapitalism #psychoanalysis