Artificial Intelligence Could Get a New Name Under Trump’s Proposal

The phrase “artificial intelligence” has become so embedded in technology, business and everyday language that changing it might seem almost impossible.

Yet in September 2026, President Donald Trump proposed doing exactly that, arguing that the word “artificial” makes intelligence sound fake. He initially offered Americans three alternatives: Superior Intelligence, Extreme Intelligence and Supreme Intelligence.

Days later, at the United Nations General Assembly, Trump said the technology would henceforth be referred to by the new name “super intelligence.”

The unusual proposal opens a broader conversation about how technology is framed. Names do not change what a machine can do, but they influence how people understand its purpose, potential and risks. Trump’s suggested alternatives were designed to emphasize capability rather than artificiality.

“Superior Intelligence” implies intelligence that exceeds conventional human performance, while “Extreme Intelligence” emphasizes scale and power. “Supreme Intelligence,” meanwhile, carries the strongest suggestion of technological supremacy.

None of the three is an established technical category in computer science.  The history of the existing term is itself a reminder that technology names are not inevitable. “Artificial intelligence” emerged from the academic work surrounding the 1956 Dartmouth conference.

Where computer scientists including John McCarthy, Marvin Minsky, Claude Shannon and Nathaniel Rochester helped establish the field. Before the terminology became standardized, researchers used descriptions including cybernetics, automata studies and complex information processing.

That history makes Trump’s proposal less about inventing an entirely new concept and more about changing the language surrounding an existing technological revolution.

The proposed alternatives also reveal how dramatically the public conversation around AI has changed. When the term was popularized in the 1950s, computers were primitive compared with today’s systems.

Modern models can generate software, analyze enormous datasets, produce images and video, interact conversationally and perform increasingly sophisticated professional tasks. For Trump, describing these systems as merely artificial understates their capabilities.

At the UN, he said the word made intelligence sound fake and announced that U.S. government documents would use super intelligence instead. There is, however, a practical distinction between political language and technical terminology.

A president can direct terminology used within government communications, but artificial intelligence is not a legally protected name that can simply be erased from global usage. Technology companies, researchers, universities and international institutions have spent decades using AI as the standard description.

The naming debate arrives during a much larger argument over the direction of AI policy. Trump has simultaneously announced plans for an AI Force and a new AI czar, while emphasizing rapid technological development and U.S. competition with China.

Whether the technology is called artificial intelligence, superior intelligence, extreme intelligence or super intelligence does not alter its underlying capabilities. The more consequential question is what governments, companies and societies do with those capabilities.

The terminology may evolve, but the economic, regulatory and technological consequences of increasingly powerful AI will remain the central issue.

How Banks Can Measure the Business Value of Artificial Intelligence

Artificial intelligence is moving from the margins of financial services toward the center of how institutions operate, compete and manage risk.

Banks, insurers, asset managers and fintech companies have spent years experimenting with machine learning, generative AI and automated decision systems. Yet experimentation is proving easier than transformation.

The difficult question is no longer whether financial institutions can use AI, but whether they can deploy it at scale without compromising trust, security or financial discipline. The opportunity is substantial.

AI can process enormous volumes of financial information, identify patterns that humans may overlook and automate repetitive work. In banking, this can mean faster fraud detection, more sophisticated credit assessment, personalized customer services and automated compliance processes.

Asset managers can use AI to analyze market data, corporate disclosures and alternative datasets. Insurers can apply similar technologies to underwriting, claims processing and risk assessment.

Generative AI has expanded the opportunity further by making sophisticated analytical tools accessible through natural language. Employees can potentially summarize documents, generate reports, search internal knowledge and interact with complex datasets without relying entirely on specialized technical teams.

This could reduce administrative costs while allowing professionals to devote more time to decisions requiring judgment. But financial institutions face a fundamental scaling problem. A successful pilot does not automatically become a reliable enterprise system.

An AI model that performs well in a controlled environment can encounter very different conditions when connected to millions of customers, legacy technology and constantly changing financial data.

Institutions therefore need infrastructure capable of supporting AI securely and consistently across business units. Data is central to this challenge.

Financial AI depends on high-quality, accessible and appropriately governed information. Fragmented databases, inconsistent definitions and outdated technology can undermine even the most sophisticated model.

Building a scalable AI strategy consequently requires investment in data architecture, cloud infrastructure, cybersecurity and application programming interfaces alongside investment in the models themselves. The economics of AI demand greater discipline.

Financial executives cannot simply count the number of AI projects launched. They need measurable outcomes. Does an application reduce processing time? Does it lower fraud losses? Does it improve customer retention? Does it increase employee productivity without creating additional operational risk?

These questions turn AI from a technology experiment into an investment decision. Risk management becomes equally important as deployment expands. AI systems can produce inaccurate outputs, inherit biases from training data, expose confidential information or become vulnerable to manipulation.

In highly regulated financial markets, an institution must also be able to explain how important automated decisions are made and establish accountability when systems fail.

This means governance cannot be treated as an obstacle to innovation. Clear human oversight, model validation, access controls, audit trails and continuous monitoring can become part of the infrastructure that makes large-scale adoption possible.

The objective is not necessarily to eliminate human involvement, but to determine where humans remain essential and where machines can safely perform routine tasks.

The competitive landscape is likely to reward institutions that combine technological ambition with organizational discipline. AI adoption will increasingly involve partnerships among executives, engineers, data scientists, compliance professionals and frontline employees.

Institutions that treat AI solely as an IT project may struggle to capture its broader economic value. The transformation of financial services through AI will not be determined by who adopts the most advanced model first.

It will depend on who can integrate AI into real business processes while maintaining reliable data, measurable economics, strong governance and customer trust. The next phase is therefore less about experimentation and more about execution.

AI’s lasting impact on finance will emerge when institutions turn promising demonstrations into dependable infrastructure.