Artificial General Intelligence (AGI) – a system with intelligence comparable to or superior to that of humans in all areas – continues to be considered the Holy Grail of technology. However, in 2025, an alternative path is emerging more clearly: we are not achieving AGI as a unified system, but rather through an increasingly convincing illusion created by the combination of multiple specialized narrow AIs.

Today's AI excels at specific tasks: Large Language Models (LLMs) handle text, models such as Midjourney or DALL-E create images, AlphaFold analyzes proteins. Although limited individually, when integrated into a coordinated ecosystem, these narrow AIs create the appearance of general intelligence—a “proxy” for AGI.

According to Stanford University's AI Index 2025 report, despite significant advances, AI continues to encounter obstacles in the field of complex reasoning.

The most advanced models solve highly structured problems but show marked limitations when it comes to articulate logical reasoning, sequential planning, and abstract thinking.

In 2025, artificial intelligence is evolving rapidly, transforming from a niche technology to a strategic element of the technological and social landscape, with profound cultural and ethical implications.

This has led to the emergence of agentic AI systems that bring us closer to the horizon of general artificial intelligence.

In multi-agent systems, each agent operates independently, using local data and autonomous decision-making processes without relying on a central controller.

Each agent has a local view, but no agent has a global view of the entire system. This decentralization allows agents to manage tasks individually while contributing to overall goals through interaction.

In 2025, multi-agent systems—where multiple AI agents collaborate to achieve complex goals—are becoming increasingly widespread. These systems can optimize workflows, generate insights, and assist in decision-making processes across various industries.

For example, in customer service, AI agents handle complex requests; in manufacturing, they supervise production lines in real time; in logistics, they coordinate supply chains dynamically.

Despite impressive advances, we are beginning to reach a plateau in traditional computational development. From 1959 to 2012, the amount of energy required to train AI models doubled every two years, following Moore's Law. However, the latest data shows that after 2012, the doubling time has become significantly faster—every 3.4 months—making the current pace more than seven times faster than the previous rate.

This dramatic increase in the computing power required underscores how economically challenging it has become to achieve significant advances in AI.

Quantum computing could overcome this hurdle, offering a paradigm shift in the computational capacity needed for even more sophisticated models. In 2025, quantum computing is emerging as a crucial tool for addressing these challenges, as technology companies embrace alternative energy sources to keep pace with AI's growing energy consumption.

According to a prediction by Arvind Krishna, CEO of IBM, thanks to rapid advances in quantum computing, AI's energy and water consumption could be reduced by up to 99% over the next five years.

This technology promises to unlock currently unimaginable computing capabilities and open new frontiers in scientific research.

A major breakthrough was announced in March 2025 by D-Wave Quantum, which published a peer-reviewed paper titled “Beyond-Classical Computation in Quantum Simulation,” demonstrating that their quantum annealing computer outperformed one of the world's most powerful classical supercomputers in solving complex magnetic material simulation problems.

2025 saw transformative advances in quantum computing, with major breakthroughs in hardware, error correction, integration with AI, and quantum networks. These are redefining the role of quantum computing in sectors such as healthcare, finance, and logistics.

Microsoft claims to have made significant progress in quantum computing with its Majorana 1 chip, introduced in early 2025. This processor features a new Topological Core architecture, built with eight topological qubits that manipulate Majorana particles, quasi-particles that act as “half-electrons” known for their strong resistance to errors.

Google, on the other hand, has developed a different approach with its revolutionary quantum chip called Willow, which solves the traditional problem of increasing error rates as qubits increase—Willow actually becomes more accurate as more qubits are added.

These two different strategies represent fundamentally different approaches to quantum computing, with Microsoft focusing on topology and Google on error optimization.

We will explore this fascinating technological comparison in a future article dedicated exclusively to the implications of quantum computing for artificial intelligence.

In addition to hardware limitations, composite AIs face other fundamental barriers:

Causal understanding: Systems correlate variables but do not isolate true cause-and-effect relationships. AI has made significant progress in many fields, but continues to face limitations in understanding and responding to human emotions, making decisions in crisis situations, and evaluating ethical and moral considerations.

Continuous learning: Neural networks lose accuracy when trained sequentially on different tasks, exhibiting a kind of “catastrophic amnesia.”

Metacognition: AI lacks an internal model of its own cognition, limiting true self-improvement.

The scientific community appears rather divided on the technologies and timeframes needed to achieve the goal of Artificial General Intelligence (AGI), but the debate is bringing to light some interesting new ideas, which are already finding practical application in the search for new AI systems.

2025 could be the year when the first agent systems go into production in companies. While AGI is the most ambitious goal—systems with cognitive abilities comparable to or superior to those of humans, capable of understanding, learning, and applying knowledge across domains—the future is likely to see the emergence of what we might call “AGI by proxy.”

Rather than waiting for a monolithic AGI, the most likely future will see the emergence of what we might call “facade AGI”—systems that appear to possess general intelligence through:

In the AGI debate, we tend to take for granted that humans are endowed with a “consciousness” that machines cannot replicate. But perhaps we should ask a more radical question: Is human consciousness itself real, or is it also an illusion?

Some neuroscientists and philosophers of mind, such as Daniel Dennett, have proposed that what we call “consciousness” may itself be a post-hoc narrative—an interpretation that the brain constructs to make sense of its own operations.

If we consider consciousness not as a mysterious, unitary property, but as a set of interconnected neural processes that generate a convincing illusion of a unified “self,” then the boundary between humans and machines becomes less clear.

In this view, we might consider the differences between emerging AGI and human intelligence to be differences of degree rather than of kind. The illusion of understanding we see in advanced language models may not be so different from the illusion of understanding we experience ourselves—both emerging from complex networks of processes, albeit organized in fundamentally different ways.

This perspective raises a provocative question: if human consciousness is itself a simulation emerging from multiple interconnected cognitive processes, then the “proxy” AGI we are building—a mosaic of specialized systems working together to simulate general understanding—could be surprisingly similar to our own mental architecture.

We would not be trying to replicate a magical and ineffable quality, but rather to reconstruct the convincing illusion that we ourselves experience as consciousness.

This reflection does not diminish the depth of human experience, but invites us to reconsider what we really mean when we talk about “consciousness” and whether this concept is truly an insurmountable obstacle for artificial intelligence, or simply another process that we may one day be able to simulate.

Perhaps we should reconsider our definition of AGI. If human consciousness is an emergent illusion—a narrative the brain constructs to make sense of its own operations—the distinction between human and artificial intelligence becomes blurred.

Experts predict that 2027 could be a pivotal year for AI. At the current pace, models could achieve cognitive generality—the ability to perform any human task—within a few years.

However, this scenario should not be viewed as merely a replication of human intelligence. Rather, it should be seen as the emergence of a new type of intelligence—one that is neither completely human nor completely artificial, but rather something different and potentially complementary.

This approach frees us from trying to replicate something we may not fully understand—human consciousness—and allows us to focus on what artificial intelligence can do on its own terms. The AGI that emerges will not be a single system that "pretends" to be human but rather an integrated technological ecosystem with its own emerging characteristics—a distributed intelligence that may, paradoxically, reflect the fragmented and interconnected nature of our own cognition more than we initially thought.

Thus, the quest for AGI is less an attempt to emulate humans and more a journey of discovery into the nature of intelligence and consciousness, both human and artificial.

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