Artificial General Intelligence (AGI) is considered the latest stage of AI. Here’s what it is, how it works, and why it’s more powerful than the human mind.
Artificial General Intelligence (AGI) represents the most important frontier in AI in recent years. Machines are no longer limited to performing tasks taught by humans, but are acquiring the capabilities to learn, understand, and apply their knowledge to any problem.
NVIDIA CEO Jensen Huang recently spoke on the topic, reopening the debate between those curious about how AI will impact our lives and those concerned about its future implications.
In this guide, we’ll explore what Artificial Intelligence is, how it works, what it can already do today, and what its goals are for the coming years.
What is General Artificial Intelligence
General Artificial Intelligence is a system capable of understanding and applying intelligence to any problem. Its main distinguishing feature from other models is transfer learning. What does this mean? That its knowledge acquired in one area can be used to solve problems in completely different fields.
The development of AGI is based on three main foundations. The first is Reasoning, the ability to not only predict the next word but also plan logical steps.
The second is multimodality, which allows it to see, hear, and even touch (in the case of robotics) to understand the physical world.
The third is long-term memory, which allows us to build an identity and a historical experience that can persist from one session to the next.
At the moment, on the scale of artificial intelligence development, we are stuck at the second stage, that of reasoners. These are systems capable of solving complex problems at the level of a human with a doctorate, without having to access external tools.
Level 3 is that of agents, systems that not only speak, but also act in detail and can operate autonomously for several days on behalf of the user.
For the definitive transition from level 2 to level 3, there are some technical obstacles such as hallucinations, long-term planning, and computational cost.
How does General Artificial Intelligence work
How does General Artificial Intelligence work? It doesn’t work through traditional software, but through a hybrid architecture that aims to replicate the human brain.
To work optimally, two different approaches are combined. On the one hand, neural networks, on the other, symbolic reasoning.
The reasoning architecture, on the other hand, is multi-step, with the so-called chain of thought. This means that the system doesn’t generate the output immediately, but creates a series of intermediate and invisible steps, evaluating different hypotheses and correcting any errors before providing the final answer.
The true engine of General Artificial Intelligence is its ability to generalize. Tokenization is universal; each element inserted into the input is treated as a sequence of numerical data. Thanks to this approach, the machine can learn new things without having to be reprogrammed from scratch.
To work autonomously, AGI requires a structured external memory that recalls past information both to make deductions and for completely new situations.
Where Artificial General Intelligence is applied
Today, Artificial General Intelligence is not yet applied because there is no system that possesses consciousness or cognitive capacity equal to that of humans.
However, we are in an advanced transition phase, where some applications are showing signs of behavior similar to what AGI should be expected.
For example, there are systems like Google DeepMind’s GNoME capable of predicting the stability of over 2 million new crystals, as well as platforms like Devin that are starting to behave like digital agents.
The discussion then extends to complex medical diagnostics, with reasoning models such as OpenAI’s o1 series being tested to solve rare clinical cases.
Finally, humanoid robotics with Figure AI or Tesla and strategic and military simulations, where AGI is used to predict geopolitical scenarios and manage complex logistics networks.
Can it become superior to the human mind?
Several experts in the field have already spoken out on this matter, defining Artificial General Intelligence as potentially superior to the human mind. This is only partially true, as it depends on the type of intelligence we’re measuring.
There are some fields in which machines have greater potential. Just consider the fact that AGI can read an entire medical literature in a few minutes and find correlations between multiple studies, or that its memory maintains 100% accuracy even for complex calculations.
And the human mind, where is it unbeatable? A first point in the debate concerns energy efficiency. The human brain consumes about 20 watts to perform operations that AI would require a supercomputer to perform. Learning occurs with little data, while AI needs millions of images. And then there’s emotional and social intelligence, the ability to read emotions and moral nuances without being specified.
The real breakthrough is recursive self-improvement, which is already a fear on one side and a promise on the other. When AGI is able to write its own code better than a human engineer, it will be able to create a more intelligent version of itself in an infinite loop.
This would lead to Artificial Superintelligence, a stage that would surpass human intelligence in every field, including creativity and social strategy.
What the experts say today
There are many experts who have already expressed their views on the topic. Among them is Yann LeCun, Meta’s AI chief, who believes that today’s Artificial General Intelligence lacks biological common sense. Until it has a physical body to interact with the world, he says, it will remain a limited intelligence compared to human intelligence.
Jensen Huang, CEO of NVIDIA, also weighed in on the topic. According to him, AGI could already be among us or within a short time. Appearing on Lex Fridman’s podcast, he explained that AGI is already among us if we consider it as the ability of software to pass medical, legal, or logical tests better than humans.
However, there are current limitations, such as the inability to manage a complex company like NVIDIA alone. To fuel this technology, NVIDIA GTC 2026 predicted total revenue between 2025 and 2027 will exceed $1 trillion.
What’s next? NVIDIA will launch Vera Rubin, an architecture designed for next-generation accelerated computing. General robotics is also expected to explode, with models capable of learning motor and logical skills across the board.
Companies could transform themselves into token factories, where AI is produced on an industrial scale to optimize various processes. Finally, the focus will shift from training to large-scale inference, making AGI affordable and accessible for everyday applications.
Original article published on Money.it Italy. Original title: Cos’รจ l’Intelligenza Artificiale Generale (AGI), l’AI che supera la mente umana
