#platformcapitalism — Public Fediverse posts

Fear and Loathing of AI (Part V): The Real Product Isn’t AI — It’s Compliance

By Cliff Potts, CSO, and Editor-in-Chief of WPS News — with OpenAI ChatGPT

Every major technology claims to sell capability.

What it actually sells is behavior.

Artificial intelligence is marketed as intelligence, automation, assistance, and insight. But those are not the real product. They are the wrapper.

The real product is compliance.

From tools to rulebooks

AI systems do not merely help people work. They define how work is allowed to happen.

They decide:

• what is acceptable output,
• what counts as efficiency,
• what language is permitted,
• what pace is required,
• what deviation triggers review.

Once embedded, these systems stop being optional tools and start functioning as invisible rulebooks.

You are not just using AI.
You are being shaped by it.

Normalizing the machine’s priorities

AI systems optimize for what they can measure.

That sounds neutral. It isn’t.

What gets measured becomes what matters:

• speed over care,
• volume over judgment,
• consistency over insight,
• compliance over discretion.

Human nuance becomes noise.
Context becomes friction.

Over time, workers internalize this logic. They adjust themselves to match the system rather than questioning whether the system is correct.

That is not assistance.
That is conditioning.

Consent by exhaustion

Most people do not choose compliance.

They accept it because resisting it is exhausting.

Opting out means:

• losing access,
• losing income,
• losing relevance,
• losing visibility.

So people adapt. Quietly. Incrementally. Rationally.

Each update narrows the corridor.
Each “improvement” reduces discretion.
Each convenience carries a hidden obligation.

Eventually, compliance feels like normal work.

The illusion of neutrality

AI is often described as objective.

But every AI system encodes:

• institutional priorities,
• business incentives,
• legal risk tolerance,
• and managerial worldview.

Those values are not debated by users.
They are imposed through interfaces.

When people are told “the system decided,” accountability dissolves. Authority becomes abstract. Power becomes deniable.

No one is responsible.
Everyone must comply.

Why this matters more than jobs

Job loss is visible.
Compliance is subtle.

A workforce that still exists but no longer questions:

• pacing,
• evaluation,
• fairness,
• or purpose

is easier to manage than one that resists.

The danger is not a future without work.

It is a future where work continues, but autonomy does not.

The quiet trade

AI offers convenience in exchange for conformity.

For many, that trade feels necessary. Sometimes it is.

But it should never be invisible.

Because once compliance is normalized, reclaiming discretion becomes almost impossible.

A line that still exists

AI can be useful without being authoritative.
It can assist without dictating.
It can serve without ruling.

But that only happens when:

• systems remain accountable,
• humans retain override power,
• and institutions are forced to justify decisions.

Without those limits, AI doesn’t just change work.

It trains people to accept less agency as the price of participation.

That is not progress.

That is control, automated.

For more social commentary, please see Occupy 2.5 at https://Occupy25.com

Fear and Loathing of AI (Part V): The Real Product Isn’t AI — It’s Compliance

By Cliff Potts, CSO, and Editor-in-Chief of WPS News — with OpenAI ChatGPT

Every major technology claims to sell capability.

What it actually sells is behavior.

Artificial intelligence is marketed as intelligence, automation, assistance, and insight. But those are not the real product. They are the wrapper.

The real product is compliance.

From tools to rulebooks

AI systems do not merely help people work. They define how work is allowed to happen.

They decide:

• what is acceptable output,
• what counts as efficiency,
• what language is permitted,
• what pace is required,
• what deviation triggers review.

Once embedded, these systems stop being optional tools and start functioning as invisible rulebooks.

You are not just using AI.
You are being shaped by it.

Normalizing the machine’s priorities

AI systems optimize for what they can measure.

That sounds neutral. It isn’t.

What gets measured becomes what matters:

• speed over care,
• volume over judgment,
• consistency over insight,
• compliance over discretion.

Human nuance becomes noise.
Context becomes friction.

Over time, workers internalize this logic. They adjust themselves to match the system rather than questioning whether the system is correct.

That is not assistance.
That is conditioning.

Consent by exhaustion

Most people do not choose compliance.

They accept it because resisting it is exhausting.

Opting out means:

• losing access,
• losing income,
• losing relevance,
• losing visibility.

So people adapt. Quietly. Incrementally. Rationally.

Each update narrows the corridor.
Each “improvement” reduces discretion.
Each convenience carries a hidden obligation.

Eventually, compliance feels like normal work.

The illusion of neutrality

AI is often described as objective.

But every AI system encodes:

• institutional priorities,
• business incentives,
• legal risk tolerance,
• and managerial worldview.

Those values are not debated by users.
They are imposed through interfaces.

When people are told “the system decided,” accountability dissolves. Authority becomes abstract. Power becomes deniable.

No one is responsible.
Everyone must comply.

Why this matters more than jobs

Job loss is visible.
Compliance is subtle.

A workforce that still exists but no longer questions:

• pacing,
• evaluation,
• fairness,
• or purpose

is easier to manage than one that resists.

The danger is not a future without work.

It is a future where work continues, but autonomy does not.

The quiet trade

AI offers convenience in exchange for conformity.

For many, that trade feels necessary. Sometimes it is.

But it should never be invisible.

Because once compliance is normalized, reclaiming discretion becomes almost impossible.

A line that still exists

AI can be useful without being authoritative.
It can assist without dictating.
It can serve without ruling.

But that only happens when:

• systems remain accountable,
• humans retain override power,
• and institutions are forced to justify decisions.

Without those limits, AI doesn’t just change work.

It trains people to accept less agency as the price of participation.

That is not progress.

That is control, automated.

For more social commentary, please see Occupy 2.5 at https://Occupy25.com

Fear and Loathing of AI (Part V): The Real Product Isn’t AI — It’s Compliance

By Cliff Potts, CSO, and Editor-in-Chief of WPS News — with OpenAI ChatGPT

Every major technology claims to sell capability.

What it actually sells is behavior.

Artificial intelligence is marketed as intelligence, automation, assistance, and insight. But those are not the real product. They are the wrapper.

The real product is compliance.

From tools to rulebooks

AI systems do not merely help people work. They define how work is allowed to happen.

They decide:

• what is acceptable output,
• what counts as efficiency,
• what language is permitted,
• what pace is required,
• what deviation triggers review.

Once embedded, these systems stop being optional tools and start functioning as invisible rulebooks.

You are not just using AI.
You are being shaped by it.

Normalizing the machine’s priorities

AI systems optimize for what they can measure.

That sounds neutral. It isn’t.

What gets measured becomes what matters:

• speed over care,
• volume over judgment,
• consistency over insight,
• compliance over discretion.

Human nuance becomes noise.
Context becomes friction.

Over time, workers internalize this logic. They adjust themselves to match the system rather than questioning whether the system is correct.

That is not assistance.
That is conditioning.

Consent by exhaustion

Most people do not choose compliance.

They accept it because resisting it is exhausting.

Opting out means:

• losing access,
• losing income,
• losing relevance,
• losing visibility.

So people adapt. Quietly. Incrementally. Rationally.

Each update narrows the corridor.
Each “improvement” reduces discretion.
Each convenience carries a hidden obligation.

Eventually, compliance feels like normal work.

The illusion of neutrality

AI is often described as objective.

But every AI system encodes:

• institutional priorities,
• business incentives,
• legal risk tolerance,
• and managerial worldview.

Those values are not debated by users.
They are imposed through interfaces.

When people are told “the system decided,” accountability dissolves. Authority becomes abstract. Power becomes deniable.

No one is responsible.
Everyone must comply.

Why this matters more than jobs

Job loss is visible.
Compliance is subtle.

A workforce that still exists but no longer questions:

• pacing,
• evaluation,
• fairness,
• or purpose

is easier to manage than one that resists.

The danger is not a future without work.

It is a future where work continues, but autonomy does not.

The quiet trade

AI offers convenience in exchange for conformity.

For many, that trade feels necessary. Sometimes it is.

But it should never be invisible.

Because once compliance is normalized, reclaiming discretion becomes almost impossible.

A line that still exists

AI can be useful without being authoritative.
It can assist without dictating.
It can serve without ruling.

But that only happens when:

• systems remain accountable,
• humans retain override power,
• and institutions are forced to justify decisions.

Without those limits, AI doesn’t just change work.

It trains people to accept less agency as the price of participation.

That is not progress.

That is control, automated.

For more social commentary, please see Occupy 2.5 at https://Occupy25.com

Fear and Loathing of AI (Part V): The Real Product Isn’t AI — It’s Compliance

By Cliff Potts, CSO, and Editor-in-Chief of WPS News — with OpenAI ChatGPT

Every major technology claims to sell capability.

What it actually sells is behavior.

Artificial intelligence is marketed as intelligence, automation, assistance, and insight. But those are not the real product. They are the wrapper.

The real product is compliance.

From tools to rulebooks

AI systems do not merely help people work. They define how work is allowed to happen.

They decide:

• what is acceptable output,
• what counts as efficiency,
• what language is permitted,
• what pace is required,
• what deviation triggers review.

Once embedded, these systems stop being optional tools and start functioning as invisible rulebooks.

You are not just using AI.
You are being shaped by it.

Normalizing the machine’s priorities

AI systems optimize for what they can measure.

That sounds neutral. It isn’t.

What gets measured becomes what matters:

• speed over care,
• volume over judgment,
• consistency over insight,
• compliance over discretion.

Human nuance becomes noise.
Context becomes friction.

Over time, workers internalize this logic. They adjust themselves to match the system rather than questioning whether the system is correct.

That is not assistance.
That is conditioning.

Consent by exhaustion

Most people do not choose compliance.

They accept it because resisting it is exhausting.

Opting out means:

• losing access,
• losing income,
• losing relevance,
• losing visibility.

So people adapt. Quietly. Incrementally. Rationally.

Each update narrows the corridor.
Each “improvement” reduces discretion.
Each convenience carries a hidden obligation.

Eventually, compliance feels like normal work.

The illusion of neutrality

AI is often described as objective.

But every AI system encodes:

• institutional priorities,
• business incentives,
• legal risk tolerance,
• and managerial worldview.

Those values are not debated by users.
They are imposed through interfaces.

When people are told “the system decided,” accountability dissolves. Authority becomes abstract. Power becomes deniable.

No one is responsible.
Everyone must comply.

Why this matters more than jobs

Job loss is visible.
Compliance is subtle.

A workforce that still exists but no longer questions:

• pacing,
• evaluation,
• fairness,
• or purpose

is easier to manage than one that resists.

The danger is not a future without work.

It is a future where work continues, but autonomy does not.

The quiet trade

AI offers convenience in exchange for conformity.

For many, that trade feels necessary. Sometimes it is.

But it should never be invisible.

Because once compliance is normalized, reclaiming discretion becomes almost impossible.

A line that still exists

AI can be useful without being authoritative.
It can assist without dictating.
It can serve without ruling.

But that only happens when:

• systems remain accountable,
• humans retain override power,
• and institutions are forced to justify decisions.

Without those limits, AI doesn’t just change work.

It trains people to accept less agency as the price of participation.

That is not progress.

That is control, automated.

For more social commentary, please see Occupy 2.5 at https://Occupy25.com

Fear and Loathing of AI (Part V): The Real Product Isn’t AI — It’s Compliance

By Cliff Potts, CSO, and Editor-in-Chief of WPS News — with OpenAI ChatGPT

Every major technology claims to sell capability.

What it actually sells is behavior.

Artificial intelligence is marketed as intelligence, automation, assistance, and insight. But those are not the real product. They are the wrapper.

The real product is compliance.

From tools to rulebooks

AI systems do not merely help people work. They define how work is allowed to happen.

They decide:

• what is acceptable output,
• what counts as efficiency,
• what language is permitted,
• what pace is required,
• what deviation triggers review.

Once embedded, these systems stop being optional tools and start functioning as invisible rulebooks.

You are not just using AI.
You are being shaped by it.

Normalizing the machine’s priorities

AI systems optimize for what they can measure.

That sounds neutral. It isn’t.

What gets measured becomes what matters:

• speed over care,
• volume over judgment,
• consistency over insight,
• compliance over discretion.

Human nuance becomes noise.
Context becomes friction.

Over time, workers internalize this logic. They adjust themselves to match the system rather than questioning whether the system is correct.

That is not assistance.
That is conditioning.

Consent by exhaustion

Most people do not choose compliance.

They accept it because resisting it is exhausting.

Opting out means:

• losing access,
• losing income,
• losing relevance,
• losing visibility.

So people adapt. Quietly. Incrementally. Rationally.

Each update narrows the corridor.
Each “improvement” reduces discretion.
Each convenience carries a hidden obligation.

Eventually, compliance feels like normal work.

The illusion of neutrality

AI is often described as objective.

But every AI system encodes:

• institutional priorities,
• business incentives,
• legal risk tolerance,
• and managerial worldview.

Those values are not debated by users.
They are imposed through interfaces.

When people are told “the system decided,” accountability dissolves. Authority becomes abstract. Power becomes deniable.

No one is responsible.
Everyone must comply.

Why this matters more than jobs

Job loss is visible.
Compliance is subtle.

A workforce that still exists but no longer questions:

• pacing,
• evaluation,
• fairness,
• or purpose

is easier to manage than one that resists.

The danger is not a future without work.

It is a future where work continues, but autonomy does not.

The quiet trade

AI offers convenience in exchange for conformity.

For many, that trade feels necessary. Sometimes it is.

But it should never be invisible.

Because once compliance is normalized, reclaiming discretion becomes almost impossible.

A line that still exists

AI can be useful without being authoritative.
It can assist without dictating.
It can serve without ruling.

But that only happens when:

• systems remain accountable,
• humans retain override power,
• and institutions are forced to justify decisions.

Without those limits, AI doesn’t just change work.

It trains people to accept less agency as the price of participation.

That is not progress.

That is control, automated.

For more social commentary, please see Occupy 2.5 at https://Occupy25.com

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

The three structural trends shaping the AI crisis in higher education

1. 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.
2. 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.
3. 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.

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

👀 Contemporary networked image cultures are inseparable from platform capitalism.

The international conference «React & Respond» (Zurich, 2–4 Oct 2025) explores the aesthetics, politics, and labour of platform capitalism with scholars and artists across disciplines.

Program: https://arthist.net/archive/50541
⭐️⭐️⭐️⭐️⭐️
#PlatformCapitalism #DigitalCulture #AlgorithmicInfrastructures #MediaStudies #VisualCulture #DigitalArt #CriticalAI @bildoperationen

Why public benefit corporations won’t fix the ethics of platform capitalism

I wrote a couple of months ago about my scepticism that Bluesky will retain its ethical stances in the face of investor pressure. There’s no path to federation they’ve committed to, at a point where they’d be relatively free to do so, making it seem unlikely they’ll gut the commercialisation model at a future point when investors could push back. The obvious retort to this is that Bluesky is a public benefit corporation but, as Catherine Bracy points out in the (excellent) World Eaters, from pg 189:

While PBCs are a positive development in corporate governance, moving away from the misguided concept of shareholder supremacy that has dominated capitalism for the last century, they still have significant shortcomings. The biggest is that they don’t require companies to behave a certain way. They just provide protection for those executives who choose to put mission over profit. The companies that want to enact stricter protocols that mandate certain behavior no matter who is in charge are mostly left to create their own governance structures.

In other words it provides internal cover for sustaining commitment to a mission but it’s still dependent on motivated actors, who are operating within a system of incentives which makes it difficult to sustain a mission beyond growth and profitability. It doesn’t ‘lock in’ the mission, only ensures that it remains formally on the agenda in a discursive sense. Consider OpenAI’s hybrid structure which is arguably closer to a ‘lock in’ than being a public benefit corporation. From pg 189 of the same book:

There are a few notable examples of these bespoke structures in tech, most famously the one employed by OpenAI, which puts the for-profit entity that develops and markets ChatGPT under the control of a nonprofit whose mission is to “ensure that artificial general intelligence benefits all of humanity.” The company also places a cap on the amount of returns that investors in the for-profit entity can make, an interesting indicator that it understands just how much investor returns can influence product and business model decisions.

And Anthropic’s even more onerous hybrid structure, from pg 190:

One of OpenAI’s main competitors, Anthropic AI (which was founded by a breakaway faction of OpenAI employees who were even more concerned about AI safety risks), also has constructed a bespoke governance model with the intention of protecting the company’s mission from the vagaries of investor demands. Anthropic’s model is a hybrid. They are incorporated as a Public Benefit Corporation in Delaware, but they have also created what they call a Long-Term Benefit Trust (LTBT) that, by 2027, will have the authority to select a majority of the company’s board members. The trustees who oversee the LTBT are selected based on their commitment to and expertise around the safe deployment of artificial intelligence and will have no financial stake in the company. The terms of the trust arrangement also require the company to report to the trustees “actions that could significantly alter the corporation or its business.”

We’ve already seen Altman begin to dismantle OpenAI’s governance structure, supported by a workforce who, Bracy suggests, rallied around him after the sacking due to concerns about the value of their stock options. I think Altman’s motives have as much to do with power, particularly vis-a-vis the board, as profit in driving this dismantling of governance structures he played a significant role in designing. But fund raising will generically play a role in driving resistance to these governance structures, as Bracy notes on pg 192:

The ability to raise money while adopting an alternative structure also reflects an enormous amount of privilege on the part of these companies’ founders. The vast majority of entrepreneurs are not able to drive the kind of bargain Altman and the Anthropic team did with their investors, even in times when VCs have more money to invest than they know what to do with. Even Altman found it difficult, telling me, “It was very hard to raise under this structure. Most investors looked at it and said ‘absolutely not, I’m not capping my profits.’ ” Creating a system in which any founder can do what Altman and his cofounders did will require much deeper structural change.

While I hope Anthropic’s governance structure remains intact, not least of all because I think a reactionary Claude would be the most dangerous of the frontier models, the idea that public benefit corporations and complex governance mechanisms (consider Meta’s oversight board as well) will be sufficient to produce ethical outcomes is self-evidently implausible. The problem, as Bracy argues, in a really incisive book arises from, the incentive structure of the innovation ecosystem itself. From pg 169:

That process, of continuously raising more venture capital in order to demonstrate value to future-round funders rather than focusing on building a solid business with strong fundamentals, is what creates bubbles. It is, more than any inherent risk associated with investing in startups, why Silicon Valley is such a boom-bust sector. Given what’s at stake for venture capitalists, it is extremely difficult for founders to find off-ramps that might allow them to retain control of their companies and operate in accordance with what’s best for customers, employees, and the long-term sustainability of the business instead of what will create the highest valuation in the venture capital marketplace.

What she’s talking about her could be frame in terms of the interplay of the micro-social (founders, VC partners and key staff seeking fame and fortune) and the meso-social (the organisational dynamics of growing a firm under these conditions) within a very specific structure of incentives provided by the innovation ecosystem and the political, legal and economic climate of late neoliberalism. The turn towards public benefit corporations and ethical governance is a welcome shift but it does nothing to change the overarching context, nor does it produce fundamentally different types of firms.

#AI #anthropic #artificialIntelligence #BlueSKy #business #CatherineBracy #finance #investment #investors #openAI #platformCapitalism #politicalEconomy #publicBenefitCorporation #samAltman

Why specifically #TikTok is a problem?

– Because it's a Chinese app (#racism) and people like to suppress the fact how problematic big American tech companies are (#corpocracy)
– Because right-wing propaganda works and middle and left-wing politicians don't want to admit that they just didn't try to be equally present there

#Corporatocracy #platformCapitalism #predatoryCapitalism #chokepointCapitalism #corporateGreed #dataCapitalism #privacy #privacyMatters #MyPrivacyisNoneofYourBusiness

How long until this next #enshittification "feature":

Premium users can see your cam in the meeting software, non-premium users can't—BUT: You can't either and you can't deactivate your cam.

#ensh11n #platformCapitalism #predatoryCapitalism #chokepointCapitalism #corporateGreed #dataCapitalism

It must surely burst at some point, but it’s interesting reading this New Statesman piece from early August suggesting that the sharp dip in July could turn out to be a parallel to the dot com crash:

he dot-com crash began on a Friday – 10 March 2000 – but it wasn’t named as such until some time later. A week later, the New York Times declared “technology-heavy Nasdaq bounces back” as part of a new “surge” in stock prices. Internet companies were still attracting huge valuations without making any profit. Almost everyone believed the boom was still under way, but it had already become a crash: by October 2002, tech stocks had declined by almost 80 per cent from their peak.

It may be that 24 July 2024 comes to be remembered in similar terms. The Nasdaq-100 – an index of 100 publicly traded companies which includes Apple, Intel, Nvidia, Microsoft, Alphabet and other Big Tech names – lost a trillion dollars in market value as investors looked at a new round of company reports and asked when exactly the world-changing AI revolution was going to show up as earnings. On 2 August, the investors moving their money out of the tech-heavy American stock market was “becoming a stampede”, Bloomberg News reported, as signs of a slowing US economy sent money flowing away from riskier investments.

This was the end of a period of spectacular growth that has in recent years been largely based on the AI narrative. From November 2022 to July 2024 the market value of Nvidia, which makes chips used for running large language models such as ChatGPT, increased by nearly $2.5trn – hundreds of billions more than the value of the entire FTSE 100 index of Britain’s largest companies. By March of this year, tech stocks were priced as confidently (relative to their sales) as they had been at the height of the dot-com boom.

https://www.newstatesman.com/science-tech/2024/08/when-the-ai-bubble-bursts

I’m completely out of my comfort zone here, but this appears to me like a much more individualised trajectory for the big tech firms whose fates are most tied up in GAI:

In terms of the integration of GAI into organisation, the bubble bursting would probably be a good thing. It could be useful to ground ourselves in the realisation this is just software: it’s extremely unusual software, with a remarkably range of capabilities, but integrating it into organisational processes isn’t something that should be done in a rush or out of a fear of being left behind.

If I understand the argument Varoufakis has made about technofeudalism accurately, we shouldn’t assume that disenchantment with the technology will necessarily lead to the bubble bursting. This implies that the real economy and stock markets are still cleaved together, whereas that’s exactly what has changed. If there are any economists reading this who have ideas about what I should read to better understand the AI bubble, I’m totally open to suggestions.

https://markcarrigan.net/2024/09/15/when-will-the-ai-bubble-burst-what-will-be-left-behind/

#AI #bigTech #bubble #generativeAI #hypeCycle #investment #platformCapitalism

It should be mandatory to display the revenue of tracking and selling personal identifiable data as the cost of a free plan.

#privacy #privacyMatters #platformCapitalism #predatoryCapitalism #chokepointCapitalism #corporateGreed #dataCapitalism

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Wow, this link preview makes you so excited! This sure boosts usage of this site 👏🏻

#enshittification #ensh11n #platformCapitalism #predatoryCapitalism #chokepointCapitalism

Social Quitting

https://craphound.com/news/2023/01/22/social-quitting/

#platformcapitalism #enshittification #networkeffects #switchingcosts #locusmagazine #post-facebook #corydoctorow #post-twitter #socialmedia #spokenword #surpluses #webtheory #Articles #podcasts #Podcast #exodus #audio #locus #News