The AGI Hype and Its Promises

In the media and at tech conferences, one term dominates the discussion about the future of artificial intelligence: AGI – Artificial General Intelligence. The vision of a superintelligent machine that can handle any intellectual task a human can do fascinates AI users and investors. Billions are flowing into AGI research and the provision of the necessary computing capacities. In companies, people are wondering how to prepare for Day X when AGI is achieved, and how and if they can still jump on this bandwagon and how to bear the costs.

In the effort to achieve artificial superintelligence soon, one critical question remains largely unanswered: Do we even need human-like AGI in businesses?

The short answer: No. In this article, I would like to highlight why an AGI aligned with human performance is an overrated goal for broad AI application and how the fixation on it distracts us from the true potentials of today’s AI technologies.

What is AGI and Why is it So Tempting?

Definition and Delimitation

Artificial General Intelligence (AGI) refers to hypothetical AI systems that are capable of understanding, learning, and performing any intellectual task – similar to a human. In contrast, there is Narrow AI (also called Weak AI), which is specialized in specific tasks.

Characteristics of AGI are:

Broad applicability across various domains
Human-like understanding and context capture
Autonomous learning and development

Characteristics of Narrow AI are:

Specialization in defined task areas
Excellent performance in specific domains
Scalable and practical to use
Already economically successful today

The Promises of AGI Proponents

Prominent voices from Silicon Valley promise a revolutionary future through AGI:

Solving complex global problems (climate change, diseases, poverty)
Explosive economic growth
Scientific breakthroughs in all disciplines
A new era of human history

The positive visions are tempting – but are they realistic? And above all: Are they necessary for economic progress?

The Fundamental Problem with AGI

A fundamental problem with the AGI discussion is the lack of agreement on what AGI actually is. There is no clear definition of when a system should be considered ‘generally intelligent.’ The goalposts for AGI are constantly shifting: what was considered AGI yesterday is dismissed as ‘mere’ Narrow AI today. This makes AGI a moving target that may even remain unattainable.

Current AI systems, particularly Large Language Models, are based on statistical correlations in vast datasets. They recognize patterns but do not understand causal relationships.

A common argument among AGI proponents is: “If we only invest more data and more computing power, we will achieve AGI.” This assumption – often referred to as Scaling Laws – is increasingly being criticized.

A study published in Nature in 2025 concludes: Scaling alone does not lead to AGI. It requires fundamentally new architectures – which may not even exist.¹

Why the AGI Fixation is Problematic

Distraction from Practical Solutions

The obsession with AGI distracts resources, attention, and talent from technologies that already work today and create economic value.

An article in Foreign Affairs (September 2025) criticizes the pursuit of AGI as “The Cost of the AGI Delusion” – a dangerous illusion that causes economic and strategic misjudgments.²