Current AI Models aren't the Path to AGI
45
Artificial Intelligence
Tech
<strong>Artificial General Intelligence (AGI)</strong> represents the holy grail of artificial intelligence research—a system capable of understanding, learning, and applying knowledge across diverse domains with human-like flexibility. There were many claims in the last year by some of the biggest pioneers and investors in AI, probably fueled by their optimistic thinking or greed for investments, that AI would replace humans in many fields by the end of 2025 and AGI is around the corner.
While today's AI models demonstrate remarkable capabilities in specific tasks and continue to improve, they seem to have plateaued with their performance and capabilities, and several fundamental limitations discussed below suggest they may fall short of achieving true AGI.
<h2><strong><center>What is AGI?</h2></strong></center>
AGI stands for Artificial General Intelligence. An AI model which can reach human-level intelligence. So we should first discuss what intelligence means here. Intelligence, by definition, is <strong>the ability to acquire and apply the knowledge we gain</strong>. Something today's models do quite well is applying knowledge, while we can debate if the "applying" is actual applying or just learned behavior of where to apply what, but where they lack is in acquiring knowledge.
It's not like current models do not acquire knowledge; they are obviously trained on data which they learn, but the <strong>learning is not continuous</strong>. Once the model is trained, it cannot be changed. There are methods like in-context learning and fine-tuning of models, but they are not real-time learning mechanisms. While there are other issues around these methods—like fine-tuning a model again and again can lead to loss of old data and loss of the model's ability to learn new things, and for in-context learning, knowledge gained stays for a very short period of time during the context window.
<h2><strong><center>The Narrow Intelligence Trap</h2></strong></center>
Current AI models, including large language models and advanced neural networks, excel at pattern recognition within their training data. However, they <strong>lack genuine understanding and reasoning capabilities</strong>. These systems operate through statistical correlations rather than understanding the concepts behind them and mapping different aspects of the knowledge they gained to one another, meaning they cannot truly grasp the underlying meaning of the information they process. This fundamental architecture makes it difficult for them to generalize knowledge in the way humans naturally do.
<h2><strong><center>Lack of Common Sense Reasoning</h2></strong></center>
Human intelligence is built upon years of experiences gained by taking different actions and intuitive understanding of the physical world. Current AI models lack this grounding. They cannot reliably perform common sense reasoning that even young children might master effortlessly. For instance, if you don't train an AI model that water flows downhill, it wouldn't be able to tell if asked something which humans understand by observing. Other similar cases can be like objects don't disappear when hidden, or that social situations require emotional intelligence—these concepts remain challenging for AI systems that learn primarily from text or digital data.
<h2><strong><center>AI Scaling Problem: Energy and Computational Constraints</h2></strong></center>
The human brain operates on approximately 20 watts of power, yet current AI models require massive computational resources to achieve far narrower capabilities. <strong>AI fails to scale well with the resources provided to it</strong>, be it energy, compute, or storage. Scaling current architectures to AGI-level performance would demand impractical amounts of energy and hardware. This suggests that simply making existing models larger or more complex may not be a viable path to AGI.
<h2><strong><center>Absence of Continual Learning and Adaptation</h2></strong></center>
While humans continuously learn from minimal examples and adapt to novel situations, AI models typically require extensive retraining to acquire new capabilities. They <strong>cannot easily transfer knowledge between domains</strong> or learn from single experiences the way biological intelligence does. They also lack the capabilities to learn continuously. Current models lack the meta-learning abilities and flexible cognitive architecture necessary for general intelligence.
<h2><strong><center>No Self-Awareness or Consciousness</h2></strong></center>
AGI would presumably require some form of self-awareness and subjective experience. Current AI models, despite their sophisticated outputs, show no evidence of consciousness, intentionality, or genuine understanding of their own existence and limitations. They are sophisticated input-output systems without inner mental states.
<h2><strong><center>What Current Models Get Wrong?</h2></strong></center>
The current models or the researchers working on these models focus on how to acquire as much data as possible and how to make the model big enough to learn all the data. But there are a few drawbacks to this approach. The knowledge available in this world can be quantified as infinite, and there is <strong>no way all that knowledge can be acquired</strong>.
The other drawback is that the <strong>models lack creativity</strong>. You cannot ask these models to come up with something new or solve some question that they haven't been trained on. A better framework that AI research should move forward with is <strong>goal-based self-learning and application of knowledge</strong> by AI. This is something which humans do. If I want to learn some topic in Mathematics, I fetch the knowledge bank for that topic and I try to apply what I learned on the problems I haven't seen before. Basically, you define a goal to learn something and then gain the knowledge required for completing that goal.
<h2><strong><center>Conclusion</h2></strong></center>
Large companies promoting their AI models to be very close to reaching AGI use benchmarks to prove their AI models are "better than humans." Though the one thing LLMs are good at today is in the task of rote learning and not learning through experiences, something which these benchmarks do not take into account. This is something very fundamental to humans and hence should be part of the definition of AGI.
While current AI models represent extraordinary engineering achievements, they may represent a local maximum rather than a path to AGI.
In my opinion, the path forward is with more fundamental research work than trying to scale the number of parameters in LLMs to achieve slightly better results than the last model. The leap from narrow, pattern-matching intelligence to flexible, general intelligence may require fundamentally different architectures, approaches, and perhaps even new theoretical frameworks we have not yet discovered. Until we address these core limitations, AGI may remain beyond the reach of current methodologies.
- Ojas Srivastava, 01:42 AM, 23 Oct, 2025
