What Is Not AGI And Why It Matters

Clearing up the myths and misconceptions about Artificial General Intelligence (AGI)

May 03, 2025

I am sure you have heard the term Artificial General Intelligence (AGI) before, as it is one of the most discussed yet heavily misunderstood topics in the AI space. At the rate of AI advancements, it is very reasonable to think AGI is already here or just around the corner. But before even thinking of the availability of AGI, we need to understand what exactly AGI is and, more importantly, what it isn’t. Let’s dig a little deeper and look at the true state of AI, the myths surrounding AGI, and when we might actually see it in real life.

AGI vs. Today’s AI: What’s the Difference?

Essentially, AGI is an artificial intelligence system that is capable of understanding, learning, and applying the knowledge autonomously across various activities at the level of thinking and reasoning of human-level flexibility. By comparison, the AI we have today is called Artificial Narrow Intelligence (ANI), as they are quite particular about their individual capabilities, such as translating languages, playing games, writing code, or identifying faces, but only within certain, predetermined limits managed by humans.

Key characteristics that set AGI apart from ANI:

Generalization: Unlike ANI, which can only perform tasks it was specially designed and trained for, AGI has the ability to apply knowledge and abilities learned in one area to solve issues in totally other domains, just like a human would. For example, ANI, which was trained to identify faces in photographs, won’t be able to drive a car or write codes. On the other hand, AGI that learns to play a strategy game like chess may also use its strategic capabilities to write code or translate voices to text without retraining the model for every new assignment.
Adaptability: By design, AGI can solve or manage new, unexpected challenges on its own just like a human, while ANI can only address the particular issues it was trained for. We must retrain or reprogram ANI to handle tasks beyond its original design.
Minimal Human Supervision: With little or no human supervision, AGI can learn and develop on its own. Unlike ANI, what we have today depends mostly on human input and specified datasets to operate and grow, but AGI doesn't require continual direction, labeled data, or retraining for every new task.
No Need for Sentience: To think and operate like a human, AGI does not have to be conscious, self-aware, or even emotional. Its main objectives are grasping, reasoning, learning across multiple domains, and solving challenging problems without having the ability to feel or have subjective experiences. In other words, AGI can be very clever without being sentient.

What Is Not AGI? Common Misconceptions

Powerful Chatbots and Image Generators
The leading Large Language Models (LLMs), such as GPT-4 or Google Gemini along with image generation technologies are not AGI but ANI. While they are capable of understanding and performing specific tasks creatively, they remain unable to generalize or reason outside their training dataset.
Superhuman Performance in Specific Tasks
As of today, ANI excels at human-level performance in use cases such as Go, chess, medical image diagnosis, etc. But AI's proficiency in a single task does not qualify as AGI, regardless of its exceptional ability.
AI That Requires Frequent Human Input
Any AI system that requires continuous updates or manual adjustments to address new scenarios or situations is not AGI.
Systems Without Embodied Intelligence
The physical interaction capabilities found in modern robotics are sometimes considered AGI but lack real-world sensory and motor functions despite ongoing significant improvements.
Emotionless or Non-Conscious Systems
As humans, we often assume that AGI must be sentient. But AGI is about competence, not consciousness.

What Comes After AGI? The Shift Toward Superintelligence

Well, there is a goal beyond AGI as well. In a bold blog post, OpenAI CEO Sam Altman recently mentioned that

"We are now confident we know how to build AGI as we have traditionally understood it. We believe that in 2025 we may see the first AI agents join the workforce and materially change the output of companies."

This signals a major shift: not only is AGI viewed as achievable soon, but OpenAI is also turning its attention to Artificial Superintelligence (ASI). ASI would surpass human intelligence across every domain—a leap with profound implications for science, society, and safety.

Barriers to AGI (and ASI): Compute, Cost, and Energy

Despite the rapid progress of the AI landscape, we’re not quite there yet to achieve the status of AGI or ASI due to the various challenges, but primarily:

Compute power
Cost of scaling large models
Energy and infrastructure requirements

While breakthroughs are remarkable, making AGI accessible and affordable is a totally different challenge. Today, only a few tech giants have the resources and capability to build and deploy these systems.

Why the Distinction Matters

Confusing ANI with AGI leads to unnecessary hype, fear, and unrealistic expectations. Even in the worst case, this confusion may derail us from addressing real issues of AGI or beyond:

Fair access and equity
Safety, bias, and control
Transparency and regulatory readiness

As we previously discussed the challenges of achieving true AGI, we also need to understand the possibility of an average person getting access to AG as well. Otherwise, AGI would be another tool of the elite unless democratized intentionally.

So, When Will We See True AGI?

Even though there is no reliable or confirmed timeline, a few prominent individuals and companies made a few predictions, but they vary:

OpenAI (Sam Altman): AGI agents may enter the workforce by 2025
Anthropic (Dario Amodei): AGI capabilities likely by 2027–2030
Google DeepMind (Demis Hassabis): AGI could emerge by 2029–2035
General Academic: 2040–2060+, depending on definitions and breakthroughs

In fact, these predicted timelines depend on how we define true AGI and how fast we could overcome technical challenges like generalization, common sense, autonomous learning, etc. But one thing we could agree on now is that AGI is not here yet, regardless of how impressive the current platforms and tools are , but they do not meet the true AGI standards.

Final Thoughts

AGI is not what we have today, such as chatbots, image generators, or data wranglers. Also, it is not a tool that writes codes or tells jokes. The AGI is something far more powerful and capable. While we may be approaching AGI sooner than expected, we are certainly not there yet. In fact, we are still trying to define what true AGI is and understand what "there" really means to overall human beings.

In the meantime, understanding what AGI isn’t is as important as imagining what it could be. As the AI landscape continues to evolve rapidly, it’s categorically essential we stay informed, curious, and cautious by keeping both the promises and pitfalls in our view.