Between Trump's 'Gifts' and Rumi's Wisdom, Who Holds the Keys to Wealth and Work in the Digital Age? - Press Roundup

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In today's press roundup, we shed light on the future of wealth distribution amid the rise of artificial intelligence and the widening wealth gap, and we also discuss the value of human judgment in the age of automation, in addition to a reading of Jalal al-Din Rumi's thought on contentment and resistance to greed, and what his wisdom can offer in confronting the culture of comparison that the digital age perpetuates.

We begin our tour with the Financial Times, and an article by writer Rana Foroohar, in which she discusses the widening wealth gap and how it has become one of the most prominent political issues in the United States, amid growing public concern over income and wealth distribution, and the rising number of Americans unable to afford basic needs such as housing, food, and healthcare.

She says that the AI economy, whose returns are concentrated in the hands of a few, has increased these concerns, which explains, in her view, the launch of US President Donald Trump's 'Trump Accounts' initiative, which gives every child born between 2025 and 2028 a government contribution of $1,000, aiming to give new generations a chance to build wealth.

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But the author believes that this initiative will not achieve the transformation Trump promises, explaining that investing $1,000 with a real annual return of 5% would only amount to about $2,400 when the child turns 18, which may be helpful but is far from making a radical change in life opportunities.

She notes that Trump's mere adoption of this proposal reflects the widening American debate on how to address economic inequality, between those who call for wealth redistribution by taxing the rich, and those who prefer distributing asset ownership opportunities from the start, so that more citizens own shares in financial markets or real estate rather than relying on later tax redistribution.

The author also believes that the United States has become an asset-based economy more than ever, but the problem lies in that the richest 10% of the population owns about 93% of the wealth invested in stocks, making market gains concentrated in the hands of a few, while their wealth contributes to raising asset prices, especially housing, a phenomenon she expects to worsen with the current AI boom.

She asserts that the most successful policies in reducing inequality were those that transferred ownership of productive assets to broad segments of citizens, citing land reforms in South Korea, Taiwan, and Japan after World War II, housing policies in Singapore, and the Alaska Sovereign Wealth Fund, which distributes a portion of oil revenues to residents.

The author believes that the most important productive asset in the current era is intellectual property, data, and AI technologies, considering that the enormous wealth generated by tech companies is fundamentally based on data produced by users, which justifies, in her view, the growing calls to create technology sovereign funds that give citizens a direct share in AI wealth, rather than just relying on cash support.

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To The Washington Post, and an article by University of Washington political science professor Victor Menaldo, in which he says that growing fears of AI eliminating white-collar jobs are exaggerated, although he acknowledges that the technology has already begun to affect some professions, particularly entry-level jobs.

The writer notes that a number of top executives in technology and industry companies have warned of job shrinkage due to AI, citing predictions by Anthropic CEO Dario Amodei that the technology could eliminate up to half of entry-level white-collar jobs within five years, along with similar predictions from officials at major companies such as JPMorgan, Ford, and Amazon.

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He confirms that these concerns are based on real facts, explaining that the unemployment rate among new graduates has exceeded 5%, and job opportunities have declined in some sectors most exposed to AI, such as software development and customer service, since the emergence of generative AI applications.

But Menaldo believes that the conclusion that automating part of a job's tasks means eliminating the job itself is based on a false assumption, explaining that jobs are not made up of separate tasks that can be easily removed, but rather an interconnected set that includes human judgment, coordination, building trust with clients, and taking responsibility for outcomes.

The writer gives the example of the Industrial Revolution, explaining that machines eliminated some traditional jobs in the textile industry, but in return increased the value of workers capable of operating and managing the new machines efficiently, making their skills more important despite the spread of automation.

He says that AI, despite its great ability to produce texts and analyze information, does not possess real knowledge, but relies on prediction based on vast amounts of data it was trained on. He adds that it cannot assess the bias of its sources, nor explain how it reached its results, and it may present fabricated information or references with the same confidence as correct facts.

Menaldo believes that this shortcoming shifts economic value from information production to the ability to verify it, explaining that institutions will increasingly need people capable of distinguishing between accurate answers and misleading or inaccurate information that AI may produce.

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He adds that the editor who can assess the quality of an argument, the doctor who detects a wrong dosage, and the lawyer who realizes that a cited court case does not exist, will become more important in an environment filled with outputs that seem convincing but may be wrong.

The writer warns that institutions that dispense with employees responsible for reviewing AI outputs may appear more efficient in the short term, but will face significant risks when incorrect information turns into an influential decision, stressing that legal and professional responsibility remains with humans and cannot be assigned to the technical model.