Backing for UBI Is Growing. Let’s Push for the Most Progressive Version of It.

Universal basic income is not a panacea, and some versions are libertarian — but a good version is worth fighting for.

By Nancy Folbre, C.J. Polychroniou

July 19, 2026

Source: Truthout

The idea of a universal basic income has been around for decades, but it is now growing in popularity as artificial intelligence threatens to make our economic future under neoliberal capitalism even more precarious. Yet, no one would argue that a universal basic income is a panacea to capitalism’s evils.

Moreover, there are different proposals for implementing a universal basic income, as renowned feminist and socialist economist Nancy Folbre — the author of several books including Making Care Work: Why Our Economy Should Put People First — points out in the interview that follows.

Folbre, who is professor emerita of economics and director of the Program on Gender and Care Work at the Political Economy Research Institute (PERI) at the University of Massachusetts Amherst, acknowledges that, given the balance of power between capital and labor in today’s world, the obstacles to implementing a fully progressive version of such a plan are enormous. Yet, societies must care for the well-being of citizens if they are to continue sustaining themselves. As Folbre says, a philosophy of every person for themselves philosophy “is a recipe for extinction.”

C.J. Polychroniou: Support for universal basic income (UBI) has grown rapidly over the past several years across Europe as well as in the United States. Of course, the idea of a universal basic income has a long history but has made a major comeback as a response to growing inequalities and concerns about the effects of automation. Still, no country has yet to implement a full UBI system, and there are many variations behind the idea. What are the defining features of UBI, and what would be the core arguments in favor of it?

Nancy Folbre: This concept implies just what it says — universal (everyone would get it) basic (not very much) income, to be provided on a regular basis, like once a month. The strongest ethical argument in its favor is that some basic level of support should be a human right. The strongest pragmatic argument in its favor is that it would buffer the effects of sustained high levels of unemployment that some (including the world’s first trillionaire-for-just-12-days Elon Musk) see as a serious threat from the expansion of artificial intelligence (AI).

What are the potential risks and complications behind implementing a UBI system in contemporary capitalist societies?

UBI and AI have something in common — they both have great potential to make us all better off, but their effects will be largely determined by who designs and controls them.

A UBI could be deployed in ways that reduce poverty, reward unpaid care, and increase the bargaining power of wage earners but it could also be used to co-opt dissent, justify cutbacks in public services, and enhance control over the population.

Historically, employers disliked the very idea of UBI because they thought it would reduce the supply of labor to employment, creating a permanent class of “free-loaders.”

In recent years, however, a number of privately funded small-scale experiments have explored the impact of a short-term “guaranteed income” on the community level, offering a relatively small payment (such as $500-$1,000 a month) for a year or two to a random sample of low-income persons in order to assess their effects. Reductions in employment that took place were small, and benefits to physical and mental health and subjective well-being were significant — especially for children.

However, it’s not clear that CEOs in the tech sector are enthusiastic about these social benefits, since they seem to believe that human workers will soon be redundant, anyway. They keep saying that a publicly funded UBI could compensate for job loss. If AI produces enormous wealth, concentrated among a few firms, some mechanism for redistribution might be necessary to avert political revolt. Hence the “benevolent oligarchy” strategy — give the masses just enough money to keep them alive and in line.

Would UBI necessarily replace all existing welfare programs?

Different answers to this question highlight the issue of design and control. A “progressive” UBI is framed as an add-on, complementing the provision of public services. A “libertarian” UBI is framed as a reallocation, using cash transfers as a replacement for public services.

Libertarian support for something like UBI has a long history. In his 1962 book, Capitalism and Freedom, the conservative economist Milton Friedman advocated for a negative income tax that was the forerunner (in spirit, if not precise design) of some UBI proposals today. He argued that cash assistance should replace much of the existing welfare bureaucracy, including many categorical assistance programs.

This argument lingers on in different forms. In his 2020 U.S. presidential campaign, Andrew Yang outlined a kind of compromise — a detailed UBI proposal that would have reallocated some but not all funds away from other programs. At least his proposal took financing and design seriously, unlike what followed.

Elon Musk — despite his happy talk regarding a future Universal High Income (rather than a merely basic one) — has offered no details whatsoever regarding its design. However, given his actions as head of the “Department of Government Efficiency,” or DOGE (which would have been better titled the Department of Government Eradication), it’s pretty clear that he sees cash transfers as something between a sop and a bribe.

The progressive case for a UBI, by contrast, sees UBI as a universal income floor layered on top of existing social programs, rather than replacing all of them. While it would justify elimination of means-tested income transfers, it would not replace social insurance (such as pensions) or public services. Indeed, the most compelling vision of programmatic change to the “welfare state” calls for both UBI and Universal Basic Services (UBS) which would include health care, child care, and elder care.

These versions of UBI typically rely on progressive taxation — imposing higher taxes on those with higher incomes or wealth. Even the very rich would be eligible for a basic income, but it would be more than canceled out by the higher taxes they would be required to pay. This is what makes the net transfer strongly progressive.

Why are socialist feminists like yourself strong proponents of combining a basic income with basic services (UBI + UBS)?

A basic universal income recognizes the value of self-care and care for others as important economic activities that go unrewarded by the market. Family and community care of dependents like children, people with disabilities, and the frail elderly is costly in terms of both time and money. Today, these costs are disproportionately borne by women, but they are discouraging men as well as women from making family commitments.

The production, development, and maintenance of human capabilities is a vital component of our economic “output,” but it isn’t considered “productive” work or included in estimates of our Gross Domestic Product. I develop this point in more detail in my new book, Making Care Work: Why Our Economy Should Put People First.

A universal basic income would recognize this work — and it should go to children as well as adults, as an extra transfer to their caregivers, in recognition of the value created by unpaid family work. It would give all those working for a wage more flexibility to devote time as needed to family, friends, and neighbors.

On the other hand, universal basic services such as health care, child care, and elder care are necessary complements to family income. They provide access to technical expertise and valuable opportunities for socialization and learning outside the home, while also making it possible for people to earn the additional income they need.

Doesn’t a lot depend on how UBI and UBS are funded? Who should pay?

Yes, here again, the details really matter. I would say there’s a pretty strong consensus among advocates that funding should come from taxing income from capital, not from labor. This would require a transformation of the federal tax system that would include increasing the capital gains tax, eliminating the “carried interest” loophole, and increasing inheritance taxes.

However, the biggest problem with the federal income tax system is that the super-rich avoid earning income precisely because it is taxed. They can simply borrow money using their enormous wealth as collateral — and the rate of interest they pay is far less than their rate of taxation. You can find a very readable explanation in Ray Madoff’s recent book, The Second Estate: How the Tax Code Made an American Aristocracy.

In other words, the income tax itself has become rather toothless, which helps explain the growing popularity of state efforts and congressional proposals to tax wealth. These could be — and should be — linked to specific estimates of the potential revenue they would make available for public social spending. Keep in mind, however, that income transfers and services targeted especially to dependents and their caregivers entail a different kind of redistribution through taxation than one based entirely on class.

Is this a practical vision for change, or is it utopian to imagine that a capitalist system could ever accommodate measures that are so antithetical to maximizing profit and promoting capital accumulation?

I agree that the obstacles — both ideological and political — are enormous. But I don’t think any of us knows for sure what possibilities the future may offer. The very concept of universal basic income and services is subversive, because it suggests that most of us are suffering a lot of unnecessary economic stress. And, in the long run, no economy based purely on profit maximization can reproduce itself, because it disregards its own demographic and environmental future. Either we care more for ourselves or we disappear.

I will end with my favorite slogan: Every man for himself is a recipe for extinction. I think that any AI well-trained in human history would probably (though possibly secretly) agree.

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This article was originally published by Truthout; please consider supporting the original publication, and read the original version at the link above.Email

Nancy Folbre

Nancy Folbre is a Professor of Economics at the University of Massachusetts Amherst and an associate editor of the journal Feminist Economics. She is the author of Greed, Lust, and Gender: A History of Economic Ideas (Oxford University Press).

AI Needs Human Hands. Indian Workers Fought Back

In Delhi’s factories, labourers are resisting efforts to train their robot replacements on the most human of actions.

By Anumeha Yadav

July 19, 2026

Source: openDemocracy

A young tailor attached buttons to a shirt at a factory near Pearl Global. Anumeha Yadav

HR staff claimed the tiny cameras affixed to each tailor’s head would mean prospective clients saw the quality of their products and placed more orders, resulting in pay rises for everyone at the garment factory on the outskirts of India’s capital. 

But the workers suspected the real reason was AI.

So Ankush Yadav, a young tailor, and his friends pried open the cameras, pulled out the memory cards and plugged them into their phones. 

“We viewed the footage on our mobiles and found it was recording our voices, everything we said, our hands, our work, everything, in three-minute clips,” Yadav said. “After every three minutes, it would record afresh.”

As policymakers and ordinary people struggle to make sense of how large-language models such as ChatGPT, Claude and Gemini will disrupt millions of office jobs, AI companies are racing to solve one of technology’s knottiest problems: dexterity, or training AI models to touch, feel, and manipulate objects in the real world. Robots are already good at regulated non-contact tasks, such as welding and spray-painting, but struggle with unstructured tasks such as stitching, sewing, or blindly groping in a handbag for a phone.

In theory, artificial general intelligence coupled with human-like dexterity would result in the human-replacing “robot army” that tech moguls such as Elon Musk, the world’s richest man, have long dreamed of. But first, the robots need to learn to change a lightbulb.

One increasingly popular approach is training models on millions of hours of recordings of humans performing dexterous tasks, in the hope of achieving a dexterity breakthrough akin to how language models like ChatGPT acquired the ability to mimic language and cognition after ingesting the vast corpus of data available on the internet. One estimate projects that over the next three years, robotics labs may spend over $1.5bn to acquire between 100 million and one billion hours of footage of people performing perhaps the most human of actions: the skilled use of their hands.

A handful of AI startups such as OpenAI and Anthropic have achieved multi-billion-dollar valuations by stealing the intellectual property of writers, artists, musicians and everyday internet users without their consent. Now, a new cohort of companies is looking to repeat their success with manual workers. These firms are striking deals with factory owners in India, offering the use of their cameras to surveil employees like Yadav, who already labour under exploitative conditions, in exchange for the footage.

But savvy Indian factory workers told openDemocracy how they have thus far resisted such attempts, offering a template for workers elsewhere to disrupt the relentless onslaught of surveillance capitalism.

“Before deploying cameras and sensors and harvesting the physical movements of people on the other side of the world, we must ask ourselves the question: What would it look like if we designed our technology for everyone involved?” said Caitrin Lynch, professor of anthropology at Olin College of Engineering, Massachusetts. “What would it look like to partner with the workers to find out what tech they would welcome, and work from there?”

Lynch, who researches the intersection of robotics and society, said that rather than extracting data from workers, this could be an opportunity to design AI applications and robotics technology to benefit both workers and companies.  

“As technologists, we cannot simply wait for policy to catch up to our technological capabilities,” she said. “We have a responsibility to look at the history of colonial extraction and recognise when we are repeating its patterns.”

Technologists at the cutting edge of robotics research told openDemocracy their field faces “a massive data problem”. In short, the large and diverse datasets that robots require to learn are hard to come by.

The current gold standard approaches to training involve a human remotely controlling a robot to perform physical tasks such as lifting or sorting, or a human performing a manual task while wearing a glove-like “gripper” that mimics a robot hand. These are effective, but hard to scale and very expensive. 

A cheaper approach is to use simulation data, in which an AI model designed to train robots is run and tested in a controlled virtual environment – but this has its own drawbacks.

“You can easily scale in simulation, but nothing is as good as real data,” said Jigar Kumar Patel, a roboticist machine learning engineer at the Robotics and AI Institute in Cambridge, Massachusetts. “Physical properties do not easily translate into models. Simulators are getting better but we are not there yet,” he explained, describing a “simulation gap” in which the properties of objects in the real world – weights, noises, malleability to touch, unpredictability – do not translate accurately into data.

In this context, technologists are turning to “egocentric data”, or footage obtained from head-mounted cameras, which accurately capture the positions and configurations of human hands as they perform complex tasks. Predictably, a number of startups have sprung up to service this need.

One startup making the headgear required for this data is Egolab.AI, which was founded in January this year by two Indian teens living abroad, one of whom had dropped out of an engineering programme at a US university. 

openDemocracy spoke to 20 workers from two factories in Delhi’s industrial belt who were asked to wear Egolab’s head-mounted cameras earlier this year, but were not told why they would be doing so. Yadav works at the smaller of the two factories, owned by Pearl Apparel, which employs around 500-600 workers at two units outside the Indian capital. The second factory is owned by Pearl Global Industries, a multinational with a workforce of more than 30,000 people across India, Bangladesh, Vietnam, Indonesia and Guatemala, which supplies brands such as Zara, Ralph Lauren, Gap and Primark. The two factories are not connected, despite their similar names.

Around the same time that the factory employees we spoke to were trialling its headgear, Egolab was snapped up by another startup headed by two similarly youthful founders. Build Artificial Intelligence Inc. describes itself as the “largest egocentric data collection effort in history”, with an aim to “build AI on a human foundation”. It was registered last year in the US state of Delaware by Edward Xu, 19, and Jonathan Jia, 21.

Egolab offered prospective factories a deal that perfectly encapsulates the oppressive dynamics of technology in the modern workplace. 

In a pitch deck obtained by Indian news site Scroll.in and shared with openDemocracy, Egolab invited Indian manufacturing firms to “lead global AI data revolution” by helping it collect authentic footage of workflows of “assembling, machining, loading”. In return, it offered the companies free access to so-called “AI-powered efficiency reports” derived from surveilling workers through the same cameras, which would monitor how they used their time on the shop floor, whether they were idle, or even whether they congregated for a few seconds. The startup called this snooping “productivity analytics” based on its proprietary models. 

A sample report produced by the company included “critical insights” such as “51% of idle time is socialising. This is 3x higher than top factories. We noticed workers from different stations gathering near the wearer of CAM 02 between 2:00-3:30 PM” and “Productivity drops by 35% after lunch for all workers. Top factories solve this by starting high-priority tasks right after lunch instead of allowing workers to “ease back in”.

Egolab’s pitch deck said “workers opt in voluntarily” to wearing the cameras and that they “can withdraw anytime” – claims that contradicted the experiences of workers interviewed by openDemocracy.

“By taking data of employees who are a captive workforce and already have little bargaining power, such companies are collecting data in a regulatory vacuum,” said Shruti Narayan, a New Delhi-based technology lawyer. 

India still does not have robust data protection legislation. Its primary data privacy law, the Digital Personal Data Protection Act, suffered years of delay and deferral before being passed hastily in one week in 2023, amid a walkout by Parliament’s opposition members. The bulk of its privacy provisions – particularly on consent – were deferred and will not come into effect until mid-2027.

Egolab.AI and Build AI did not respond to requests for comment. 

At both Pearl Global and Pearl Apparel, workers were required to wear the headgear for two weeks in late March and early April. On one hand, the cameras were just one more instance of exploitation in a country where less than 20% of factory employees hold a written contract. On the other, there was something clearly uncanny about the trials, which stopped as mysteriously as they started.

Yadav said workers at Pearl Apparel were initially bemused by the strange new intrusion on the factory shop floor.

“They were asking us to wear [the headset] all eight hours, seeing how long we work, how long, say, we use mobiles; they were recording everything,” he said, adding: “They could listen to everything we said.” In company documents, Egolab.AI claims it blurs faces and mutes voices in the data it makes further available, but that was not the workers’ experience.

“A team would arrive around 9 am and ask tailors, helpers – who cut threads, pack garments, emboss stickers – to wear the camera on their heads,” said one of Yadav’s colleagues, Arun Ram, an experienced master tailor who stitches the prototype for other tailors to replicate. “Then, at 5:30 pm, when the general shift was over, they took the devices away.” 

At both factories, the workers’ compensation for sharing skills perfected over years of labour, through which they earn their livelihoods, with a company that hopes to train robots to be able to do their jobs, was a warm tetrapak of Mango Frooti, an artificially fruit-flavoured drink.

When their pleas to take the cameras off fell on deaf ears, workers quietly stopped wearing them or repeatedly took them on and off to render the footage useless and hinder the ‘productivity analysis’.

“Wearing it would hurt my head and I would take it off,” recounted Veena Devi*, who worked on the same floor as Ram. Devi migrated to the industrial pockets surrounding Delhi from her village in Uttar Pradesh ten years ago. “I would try to remove it, but my supervisor would ask again and again to wear it, so I had to put the camera back on my head.”

Devi said she and her colleagues enquired about the purpose of data collection several times, but neither their supervisors nor the team of young people who arrived every morning to distribute the headsets responded to them. “We would ask, ‘Why are you making us wear this?’ but the supervisors would not say.”

Sonu Kumar, a young tailor at Pearl Global, said his supervisor told him the data collection was for “training” – but not who the training was for. On hearing that the footage may be used for automation and training AI, Kumar added: “If I knew it was for training a robot, I would prefer not to wear it. If they want a robot to do this work, then why don’t they get one to do stitch clothes already?”

Mid-level managers and HR staff at both factories seemed equally in the dark. At Pearl Apparel, a HR manager said that because the senior management had not shared the cameras’ purpose with them, they told the workers that it was for “training other workers in the future”. At Pearl Global, HR staff said their role was limited to providing a room for the startup team operating the camera devices, where they could gather, monitor and charge devices daily. “A team of six to ten young girls and boys would arrive and divide themselves to place the cameras on the five floors above in the different departments: production, finishing, packing.”

A mid-level manager at Pearl Apparel who spoke to openDemocracy on the condition of anonymity said they found it hard to “motivate” staff to wear the cameras because they, too, didn’t know what they were being used for. “Their camera teams would say this was being done for ‘Output and time calculation’. Workers would come and tell us, ‘No, sir, it’s for AI,’” said the manager. “If the workers had taken the devices off to go to the toilet, most of them would not wear them again. The senior management would call and tell us: ‘Why aren’t you making them wear it through the shift? This way, no productivity analysis can happen’. But not even one worker wore it all eight hours.”

Kumar, like most tailors in the industrial belt, worked eight-hour shifts daily and did an hour or two of overtime a week, for which he received a monthly salary of 15,108 rupees, around £115, for most of the past two years. This rose to 18,500 rupees (£142) in April, after Delhi’s industrial region was hit by a wave of strikes as long-simmering grievances over stagnant wages and harsh work conditions came to a head when cooking gas prices soared in the wake of the US-Iran war. Picketing workers clashed with the police until the state government announced a 35% rise in the minimum wage.   

During the unrest, workers at Pearl Global stopped work twice, staff at the factory’s unit told openDemocracy. The first time, workers on the shop floor downed tools for over an hour. “The next morning, more than a thousand of them gathered outside the gate and refused to go inside the factory for the general shift,” one worker said. 

Staff resumed work only after the company representatives agreed to increase their wages in line with the new minimum wage. Several workers complained that the camera headsets were generating heat and causing them headaches, said a supervisor. The data collection continued for a day or two after their return to work, but workers were increasingly failing to cooperate with wearing the headsets for much of the day. Two weeks into the experiment, the video-collection experiment was quietly shelved at both factories.

Neither Pearl Apparel nor Pearl Global responded to openDemocracy’s request for comment.

openDemocracy asked Chintu Pal, a garment worker in his mid-30s who works at Pearl Global, what would happen if, in the near future, robots did learn to sew in place of workers. “I think they are not able to make such machines right now,” he said. “Or else, they would feel no need for a kaarigar (a skilled craftsman). The first thing they would do would be to chase the poor away. ‘Bhag jao!’ (Be off!), they would like to tell us”.

He thought for a few seconds before adding: “I believe this might already be happening in China.”

*Worker names have been changed to protect their privacy

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This article was originally published by openDemocracy; please consider supporting the original publication, and read the original version at the link above.Email

Anumeha Yadav

Anumeha Yadav writes on labour and technology in India. She has investigated subjects such as workplace rights, the Aadhaar biometric system’s effects on the elderly and children, access to food and health care in conflict zones. X: @anumayhem

Digital Fascism, The Highest Stage Of Capitalism

By Rezgar Akrawi

July 19, 2026

Source: Originally published by Z. Feel free to share widely.

Image in public domain

When Vladimir Lenin wrote about imperialism and considered it the highest stage of capitalism, he was tracking the moment capital shifted from the space of free competition into the world of monopoly, where banks merged with industry, the state turned into a direct instrument serving finance capital, and the world was redivided by force among the great monopolistic blocs. Today, more than a century later, we stand before a transformation similar in essence, different in tools: digital fascism represents the stage in which monopoly capitalism moves beyond the boundaries of classical imperialism, invading the space of consciousness, behaviour, and data, through an organic merger between monopolistic technology capital and extreme nationalist political power. This merger finds its most vivid expression today in the Trumpist project, its alliances, and its aggressive wars.

Many well-known digital and technology companies known for their close relations with the Israeli military and the forced deportation systems in the United States, among them Palantir, constitute a revealing model of this stage. Yet the issue is broader than a single company. The core matter lies in the fact that monopolistic digital capital, having exhausted the possibilities of expansion through consumer markets alone, is today turning to weaponize its tools and employ them in an aggressive nationalist political project that reproduces the logic of colonial domination, this time backed by the language of algorithms.

From the Monopoly of Money to the Domination of Algorithms

In Lenin’s time, monopoly rested on control over the means of industrial production and financial markets. Today, control over the digital infrastructure itself is added to that: algorithms, databases, targeting systems, and communication platforms. This new monopoly is no less central than its financial predecessor, and in fact surpasses it in its ability to infiltrate the finest details of daily life. Every user, male and female, today turns into digital serfs inside systems they do not own, and over which they hold no real capacity to exert influence.

Unlike the classical financial monopoly that Lenin analysed, digital monopoly does not settle for silent economic exploitation. It moves rapidly into a stage in which the major monopolistic companies declare their ideological and political project with growing frankness, abandoning the mask of technical neutrality behind which they long hid. Philosophical and political statements issued by the leaderships of these companies frame investment in tools of targeting and surveillance as a moral duty toward the nation and civilization and portray refusal to participate in this project as failure or negligence.

Digital Fascism: The Alliance of Silicon Valley with the Aggressive Nation State

This alliance finds its clearest translation in the second Trump administration. The technological accelerationism movement, with its influential figures in Silicon Valley, supplies this administration with the digital tools to turn its aggressive nationalist rhetoric into actual control, domestically through the forced deportation of migrants, and externally through targeting systems that support military campaigns. Venezuela stands as a revealing model of this dimension, since Washington has, since mid 2025, relied on advanced digital surveillance and intelligence systems to track movements and identify targets, which led to air and naval strikes and a wide ranging military operation in early January 2026 that ended with the crime of abducting the Venezuelan president, in a flagrant violation of international law. Nor is this aggression separate from the brutal blockade imposed on Cuba for more than six decades.

As for the partnership between Washington and Tel Aviv, it is a deep technical partnership that provides Israel with data and targeting systems powered by artificial intelligence, which makes America and the major digital companies actual partners in documented war crimes against Palestinian civilians, while Trump works to deepen this alliance through massive security and military contracts that grant these companies growing influence in shaping policy.

Unlike traditional fascism, which needed a visible police state and a loud propaganda discourse, killing today does not need a responsible human decision or a declaration of war, instead, it needs an algorithm, data, and a green light from a device subject to no accountability whatsoever. The crime begins with classification, not with the bomb, and when entire communities are described as a threat through silent digital criteria, the killing of civilians turns into mere “security management” rather than genocidal crimes requiring accountability, in a logic that reproduces classical colonialism in the language of big data.

Yet the gravest danger lies in the surveillance society, where control becomes internal more than external. When an individual knows that they are constantly watched, they restrict themselves and move away from opposing ideas, and this voluntary self-surveillance restrains leftist and labour movements from within without the need for arrests, becoming the most efficient form of ideological domination, and the hardest to resist, because it penetrates the fabric of daily life and reshapes it from within.

The Alternative: Collective Ownership and Digital Socialism

Linking imperialism to digital fascism is not a theoretical metaphor, rather, it is an analytical necessity for understanding that the tools of domination have evolved, while their class essence has remained constant. The struggle today does not revolve only around how technology is used, rather, around who owns it, who determines its goals, and in favour of which social class it is employed.

Technology will not turn into a tool of liberation as long as it remains under the control of digital monopolies allied with the projects of the extreme right, wars, and repression. Therefore, any serious discussion about the future of technology must proceed from the necessity of building a digital socialist alternative resting on collective and communal ownership of digital infrastructure, and subjecting algorithms and artificial intelligence to genuine mass democratic oversight, through local and global legislation that expresses the interests of the working masses and the communities harmed by digital domination.

The struggle for social justice today inevitably passes through the struggle to liberate technology from this rightist, racist, and aggressive class alliance. Just as Lenin diagnosed imperialism as the highest stage of capitalism in his era, we can say today that digital fascism represents the highest stage in the development of that same imperialism, where technological digital monopoly capital merges with the extreme nationalist project into one organic alliance, whose goal is to concentrate power, wealth, consciousness, and human destiny in the hands of a small minority of the wealthy and the politicians.

To confront this reality, criticism and analysis are not enough, instead, we must move toward building joint internationalist work through digital leftist internationals capable of waging the struggle in the technological space and on the ground. This will be the subject of our next article.Email

Rezgar Akrawi

Rezgar Akrawi is a leftist researcher specializing in issues of technology and the left, working in the field of systems development and e-governance.

 

Ethics As The Architecture Of Power: China Proposes A New Global Governance Framework For Artificial Intelligence

The new Action Plan on International AI Ethical Governance, unveiled in Shanghai during the 2026 World Artificial Intelligence Conference, extends far beyond the technological sphere. It constitutes a proposal to define who will establish the rules governing the development of artificial intelligence over the coming decades and reflects a distinct conception of the international order, the role of the State, and the place that developing countries should occupy in the technological revolution.

By Claudia Aranda

July 19, 2026

Source: Pressenza

For decades, geopolitics was shaped by control over territory, natural resources, maritime routes, and military capability. In the twenty-first century, those factors remain decisive, but a new dimension of power has emerged above them: the capacity to develop, train, regulate, and govern artificial intelligence.

The issue is no longer simply who builds the most advanced models or manufactures the most sophisticated semiconductors. The competition also revolves around who will write the rules that will govern the operation of this technology for the rest of the world.

With that objective, China unveiled an Action Plan on International AI Ethical Governance on July 17, 2026, during the World Artificial Intelligence Conference (WAIC) held in Shanghai. The document was prepared under the coordination of the Ministry of Industry and Information Technology and is explicitly framed within the United Nations Pact for the Future and the Global Digital Compact.

This decision is far from incidental. While much of the international debate continues to focus on technological competition between the United States and China, Beijing seeks to place another issue at the center of the global agenda: the construction of an international architecture for governing artificial intelligence before its development permanently outpaces the regulatory capacity of States.

The distinction is profound. From China’s perspective, artificial intelligence is not merely a strategic industry or a market of immense economic value. It is a civilizational infrastructure destined to transform production, education, medicine, scientific research, public administration, finance, transportation, security, and virtually every sphere of human activity. For this very reason, Beijing argues that its development cannot be determined solely by commercial interests or corporate competition, but instead requires governance capable of safeguarding the global public good.

From this perspective, ethics ceases to be a collection of abstract principles and becomes a political technology. Governing artificial intelligence ethically means deciding how it will be designed, who will bear responsibility when harm occurs, which risks will be considered acceptable, and who will participate in defining those rules.

One of the central concepts of the plan is the establishment of governance throughout the entire life cycle of artificial intelligence. Although the expression may appear highly technical, it represents a significant departure from traditional regulatory approaches. Rather than supervising only finished products, the proposal calls for oversight mechanisms beginning at the very origin of every system. This encompasses data acquisition, training processes, model design, safety testing, deployment, subsequent updates, and even the eventual withdrawal of systems whose risks can no longer be effectively managed.

In practice, this means that responsibility no longer rests exclusively with the end user. Developers, data providers, technology companies, research institutions, and regulatory authorities all share responsibilities throughout the technological development process. Artificial intelligence therefore ceases to be viewed merely as a commercial product and instead becomes a comprehensive chain of shared accountability.

Another fundamental pillar is the classification of risks according to categories and levels. The underlying logic is straightforward: not every artificial intelligence system presents the same potential for harm. A model designed to translate documents does not pose the same risks as one capable of operating critical energy infrastructure, financial systems, medical diagnostics, or military decision-making. Consequently, the plan proposes regulatory obligations proportional to the potential impact of each application, avoiding both insufficient oversight and sweeping prohibitions that could hinder innovation.

The document also introduces the concept of agile governance. China recognizes that technological development advances far more rapidly than traditional legislative processes. A law may require years of parliamentary debate, whereas an artificial intelligence model can evolve within months—or even weeks. In response, the plan advocates regulatory mechanisms capable of continuous adaptation through technical standards, ongoing evaluation, and institutional coordination. Regulation is therefore conceived not as a static legal text, but as a dynamic and evolving process.

Another particularly significant aspect is the strengthening of a collaborative ecosystem. Under the Chinese approach, artificial intelligence is not viewed as the exclusive domain of major technology corporations. Universities, public laboratories, research institutes, manufacturing industries, open-source developers, international organizations, and governments are all considered integral parts of a single innovation chain. This reflects a longstanding characteristic of China’s development model, in which state planning seeks to coordinate capacities across multiple sectors in order to accelerate technological innovation.

Within this context, particular importance is attached to research on explainability, privacy protection, and bias mitigation. Explainability seeks to address one of the most complex questions in contemporary artificial intelligence: is it possible to understand how a model reached a particular conclusion? As AI systems become increasingly sophisticated, many of their decisions function as genuine “black boxes.” The plan therefore identifies as a priority the development of technologies capable of making these internal processes understandable while simultaneously strengthening personal data protection and reducing algorithmic discrimination.

Equally noteworthy is the explicit support for technological exchange and open-source development in areas related to security, transparency, and explainability. At a time marked by trade restrictions, export controls, and intensifying technological competition, this position seeks to project an image of international scientific cooperation while enabling countries with more limited technological capabilities to participate in the development of advanced AI tools.

The plan also incorporates a social dimension extending well beyond engineering. It proposes integrating scientific and technological ethics into the national education system, specifically safeguarding the rights and interests of women, children, older adults, and persons with disabilities, while reducing the digital divide. From China’s perspective, artificial intelligence should not deepen existing inequalities but rather serve as an instrument for expanding human development opportunities.

Perhaps the most significant element from a geopolitical perspective is the plan’s insistence on strengthening cooperation with developing countries. The document proposes expanding regulatory capacities, sharing institutional experience, and facilitating access to technical knowledge so that AI governance does not become concentrated within a small number of advanced economies. This orientation is closely connected to other initiatives promoted by China over the past decade, including the Belt and Road Initiative, the Global Development Initiative, and expanding technological cooperation with Asia, Africa, and Latin America.

This approach contrasts with the trajectory followed by the United States. Although Washington has also developed regulatory initiatives and recognizes the need to address risks associated with artificial intelligence, it has historically assigned a predominant role to private-sector dynamism and the preservation of technological leadership. Major corporations have played a central role in developing the world’s most advanced models, while public policy has combined regulation, innovation incentives, and measures designed to protect strategic capabilities, including export controls on high-performance semiconductors.

China advances a different approach. It argues that international governance should not be built solely around technological competition, but rather through multilateral mechanisms capable of establishing shared rules for the future development of artificial intelligence. From this perspective, regulation is not viewed as an obstacle to innovation but as a necessary condition for ensuring its long-term sustainability and for distributing its benefits more broadly.

Ultimately, the debate extends far beyond technology itself. What is now beginning to take shape is the normative architecture of the twenty-first century. The rules established today will determine who controls data, who sets international standards, who bears responsibility for technological risks, and who participates in the distribution of the benefits generated by artificial intelligence.

The plan presented in Shanghai reflects China’s determination to play an active role in shaping that architecture. Rather than merely responding to immediate technological competition, it seeks to occupy a central position in defining the rules of the emerging international digital order. The debate is no longer simply about who develops the most powerful artificial intelligence. The decisive question is who will possess the authority to define the rules under which that intelligence will ultimately transform the world.

Sources

• Ministry of Industry and Information Technology of the People’s Republic of China (MIIT). Official statements on the 2026 World Artificial Intelligence Conference.
• Xinhua News Agency. “Action Plan on International AI Ethical Governance,” July 17, 2026.
• United Nations. Pact for the Future and Global Digital Compact, 2024.
• Ministry of Foreign Affairs of the People’s Republic of China. Global AI Governance Action Plan, 2025.
• World Artificial Intelligence Conference (WAIC) 2026. Official speeches and conference documents.

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Claudia Aranda

Claudia Aranda is a journalist in Pressenza's Chile team.