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The new class war over artificial intelligence

Published

6 days ago

July 1, 2025

Two hundred years ago, Karl Marx looked at the industrial revolution and tried to decode the deeper logic behind the smoke, engines and factories. He didn’t just see machinery, he saw power relations. “The history of all hitherto existing society,” he wrote, “is the history of class struggles.” And at the heart of that struggle: control over the means of production.

Once it was land. Then, machines. Today, it’s code. In the 21st century, the means of production are no longer steam engines or assembly lines, but mathematical models written in open-source code and powered by silicon processors. They reside in the cloud, run on green energy and are fed by data we generate, freely. We are not just laboring for the algorithm; we are also its raw material.

Enter Elon Musk. Musk is no ordinary tycoon. He doesn’t merely manufacture cars or launch rockets; he aims to rebuild the human condition. He openly speaks of brain-machine fusion (Neuralink), interplanetary colonization and programming languages as universal modes of communication. In a sense, Musk is returning to Marx’s question, but offering a new kind of answer. Control over the means of production is no longer just economic; it is cognitive, neurological, metaphysical.

But if Marx feared factory owners, who should we fear today? Perhaps, corporations that control not only the means of production, but also the means of understanding.

History teaches us that infrastructure creates hierarchies. In ancient Rome, roads were instruments of imperial control. In feudal Europe, it was land. In the 20th century, it was electricity, gas, transport and water. Today, the deepest infrastructure is often the most invisible.

Algorithms that decide for us. Interfaces that mediate reality. AI that learns us faster than we learn ourselves.

Take ChatGPT, for example, a system perceived as “neutral,” “intelligent,” “informational.” In practice, it rests on vast data sets, embedded value hierarchies, cultural filters and corporate decisions that determine who gets access to knowledge, and what qualifies as knowledge at all. The algorithm is replacing the philosopher, the teacher, the experienced elder. And in all this, we are becoming workers who don’t know who their boss is.

If Marx were alive today, he wouldn’t go to a factory; he’d go to a GPT server or AWS. He wouldn’t talk about “exploitation” in the old sense, but about the loss of control over the most essential human tool: the ability to understand, choose, process and assign meaning to the world.

And this raises the real question: Is the era of artificial intelligence a dream of liberation, or a return to feudalism?

In the feudal age, the lord of the land didn’t just control the fields. He controlled language, law, religion and education. He dictated what could be thought. Today, the owners of code control language, information flows and attention spans. Elon Musk, Mark Zuckerberg, Sam Altman—these aren’t just “entrepreneurs.” They represent a new form of governance. They don’t enforce laws. They write the boundaries of the human imagination.

Meanwhile, the general public exists at the bottom of the information pyramid: consuming, liking, voting, but not controlling. Most of us don’t understand how AI works. We know how to use it, but not how to comprehend it. The difference is vast, like that between a farmer who can use a plough and one who can write a constitution. But is there an alternative?

Historically, structural change occurred when technology became accessible. The printing press turned monks into mass communicators. Electricity turned homes into micro-factories. The internet turned individuals into broadcasters. And AI, at least in theory, can also be democratized. There are open-source models, developer communities and cross-border collaborations. The question is whether we can preserve that openness, or whether capitalism will, once again, fence off the mind.

Perhaps the real battle for humanity’s future will not be waged between nations, but between models. Between open code and monopolies. Between transparent systems and those masquerading as neutral. Between tools that serve communities and tools that harvest their data.

Marx believed history moved toward liberation. He didn’t predict the detours. But he was right about one thing: Whoever controls the means of production, controls the future.

In the 21st century, the means of production are no longer machines. They are the thoughts we don’t know were implanted.

And the next revolution? It won’t begin in the streets. It will begin in code. Which means the question is no longer just “Who controls the technology?” But rather: Who controls the language in which we think about control?

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Radiomics-Based Artificial Intelligence and Machine Learning Approach for the Diagnosis and Prognosis of Idiopathic Pulmonary Fibrosis: A Systematic Review – Cureus

Published

1 hour ago

July 7, 2025

The Editors

Radiomics-Based Artificial Intelligence and Machine Learning Approach for the Diagnosis and Prognosis of Idiopathic Pulmonary Fibrosis: A Systematic Review Cureus

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A Real-Time Look at How AI Is Reshaping Work : Information Sciences Institute

Published

2 hours ago

July 7, 2025

The Editors

Artificial intelligence may take over some tasks and transform others, but one thing is certain: it’s reshaping the job market. Researchers at USC’s Information Sciences Institute (ISI) analyzed LinkedIn job postings and AI-related patent filings to measure which jobs are most exposed, and where those changes are happening first.

The project was led by ISI research assistant Eun Cheol Choi, working with students in a graduate-level USC Annenberg data science course taught by USC Viterbi Research Assistant Professor Luca Luceri. The team developed an “AI exposure” score to measure how closely each role is tied to current AI technologies. A high score suggests the job may be affected by automation, new tools, or shifts in how the work is done.

Which Industries Are Most Exposed to AI?

To understand how exposure shifted with new waves of innovation, the researchers compared patent data from before and after a major turning point. “We split the patent dataset into two parts, pre- and post-ChatGPT release, to see how job exposure scores changed in relation to fresh innovations,” Choi said. Released in late 2022, ChatGPT triggered a surge in generative AI development, investment, and patent filings.

Jobs in wholesale trade, transportation and warehousing, information, and manufacturing topped the list in both periods. Retail also showed high exposure early on, while healthcare and social assistance rose sharply after ChatGPT, likely due to new AI tools aimed at diagnostics, medical records, and clinical decision-making.

In contrast, education and real estate consistently showed low exposure, suggesting they are, at least for now, less likely to be reshaped by current AI technologies.

AI’s Reach Depends on the Role

AI exposure doesn’t just vary by industry, it also depends on the specific type of work. Jobs like software engineer and data scientist scored highest, since they involve building or deploying AI systems. Roles in manufacturing and repair, such as maintenance technician, also showed elevated exposure due to increased use of AI in automation and diagnostics.

At the other end of the spectrum, jobs like tax accountant, HR coordinator, and paralegal showed low exposure. They center on work that’s harder for AI to automate: nuanced reasoning, domain expertise, or dealing with people.

AI Exposure and Salary Don’t Always Move Together

The study also examined how AI exposure relates to pay. In general, jobs with higher exposure to current AI technologies were associated with higher salaries, likely reflecting the demand for new AI skills. That trend was strongest in the information sector, where software and data-related roles were both highly exposed and well compensated.

But in sectors like wholesale trade and transportation and warehousing, the opposite was true. Jobs with higher exposure in these industries tended to offer lower salaries, especially at the highest exposure levels. The researchers suggest this may signal the early effects of automation, where AI is starting to replace workers instead of augmenting them.

“In some industries, there may be synergy between workers and AI,” said Choi. “In others, it may point to competition or replacement.”

From Class Project to Ongoing Research

The contrast between industries where AI complements workers and those where it may replace them is something the team plans to investigate further. They hope to build on their framework by distinguishing between different types of impact — automation versus augmentation — and by tracking the emergence of new job categories driven by AI. “This kind of framework is exciting,” said Choi, “because it lets us capture those signals in real time.”

Luceri emphasized the value of hands-on research in the classroom: “It’s important to give students the chance to work on relevant and impactful problems where they can apply the theoretical tools they’ve learned to real-world data and questions,” he said. The paper, Mapping Labor Market Vulnerability in the Age of AI: Evidence from Job Postings and Patent Data, was co-authored by students Qingyu Cao, Qi Guan, Shengzhu Peng, and Po-Yuan Chen, and was presented at the 2025 International AAAI Conference on Web and Social Media (ICWSM), held June 23-26 in Copenhagen, Denmark.

Published on July 7th, 2025

Last updated on July 7th, 2025

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SERAM collaborates on AI-driven clinical decision project

Published

2 hours ago

July 7, 2025

AuntMinnieEurope.com staff writers

The Spanish Society of Medical Radiology (SERAM) has collaborated with six other scientific societies to develop an AI-supported urology clinical decision-making project called Uro-Oncogu(IA)s.

Uro-Oncog(IA)s project team.SERAM

The initiative produced an algorithm that will “reduce time and clinical variability” in the management of urological patients, the society said. SERAM’s collaborators include the Spanish Urology Association (AEU), the Foundation for Research in Urology (FIU), the Spanish Society of Pathological Anatomy (SEAP), the Spanish Society of Hospital Pharmacy (SEFH), the Spanish Society of Nuclear Medicine and Molecular Imaging (SEMNIM), and the Spanish Society of Radiation Oncology (SEOR).

SERAM Secretary General Dr. MaríLuz Parra launched the project in Madrid on 3 July with AEU President Dr. Carmen González.

On behalf of SERAM, the following doctors participated in this initiative:

Prostate cancer guide: Dr. Joan Carles Vilanova, PhD, of the University of Girona,
Upper urinary tract guide: Dr. Richard Mast of University Hospital Vall d’Hebron in Barcelona,
Muscle-invasive bladder cancer guide: Dr. Eloy Vivas of the University of Malaga,
Non-muscle invasive bladder cancer guide: Dr. Paula Pelechano of the Valencian Institute of Oncology in Valencia,
Kidney cancer guide: Dr. Nicolau Molina of the University of Barcelona.

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