What began as a technical inquiry into Artificial General Intelligence (AGI) soon revealed a deeper truth. Today’s most advanced AI – whether large language models, coding assistants, or game-playing bots excel at narrow tasks but crumble when faced with the open-ended, sensory-rich challenges a child navigates effortlessly. In this article, we embark on a two‑fold exploration: first, to chart why today’s most celebrated AI systems  such as large language and reasoning models, even specialized coding and game‑playing bots still fall short of the true AGI, and second, to ask what “true” AGI might require once we move beyond bits and bytes into the realms of embodiment. In this process we set the stage for a deeper discussion- grounded in embodiment and concepts of “soul” and “body” – about what it would truly take for a machine to possess general intelligence. “Part I explains why today’s AGI remains shallow; Part II explores what embodiment, soul, and rebirth might demand of true AGI.

PART 1: Why we are not there.

On 10^th of July 2025, world No. 1 Magnus Carlsen shared the game on X, noting that ChatGPT played a solid opening but “failed to follow it up correctly,” and the chatbot gracefully resigned with praise for his “methodical, clean and sharp” play. This was after he casually challenged OpenAI’s ChatGPT to an online chess match and routed the AI in just 53 moves, never losing a single piece.

Following week on 16^th of July 2025 Przemysław “Psyho” Dębiak, a polish programmer took to X to declare, “Humanity has prevailed (for now)”. He outpaced the AI by  a 9.5% margin in OpenAI’s custom AI coding model contest. He showed that model’s brute‑force optimizations fell short while human creativity to discover novel heuristics can win.

Together, these two high‑profile clashes reinforce a key theme: today’s AI, however sophisticated, remains narrow – brilliant in defined domains but outmatched by humans in open‑ended, strategic, and creative challenges.

Landscape of AI

Intelligence that is artificial is classified into Narrow, General and Super categories:

Narrow AI specializes in a single domain – like a world‑class chef who can whip up any cuisine but cannot navigate a car.

• Artificial General Intelligence (AGI) is like apart from being a super chef, can also drive Formula One cars, compose symphonies, and master new skills on its own.
• Artificial Superintelligence remains hypothetical: an AI that surpasses humans in every intellectual endeavour, from creativity to emotional understanding.

The Mirage of Generative AI

Generative AI models such as ChatGPT, Gemini, Claude are often mistaken for AGI because they handle a wide array of tasks  like essay writing, coding, poetry and produce remarkably coherent text. In reality, they are narrow systems that:

• Predict patterns rather than understand meaning.
• Although modern LLMs can access real-time data via retrieval mechanisms, their underlying knowledge remains fixed at the point of training.
• Lack common sense and real‑world adaptability.
• Mimic reasoning by reproducing patterns of human problem‑solving without genuine insight.

They are, in essence like prodigies who have committed to memory all the books and the information available on the Internet with perfect recall but no lived experience.

The Limits of Reasoning Models

Recent research (Shojaee et al. , 2025 ) on Large Reasoning Models (LRMs) shows they, too, break down beyond moderate complexity. In controlled puzzle environments (e.g., Tower of Hanoi, River Crossing),  as problems grow harder:

• Accuracy drops to zero beyond moderate puzzle complexity.
• Reasoning-chain length shrinks as tasks get harder.
• Suggests a structural ceiling on AI reasoning.

The Affordance Gap: Missing Human Intuition

An affordance is a property of an object or environment that intuitively suggests its intended use like a button whose raised shape and alignment imply it can be pressed or clicked. Humans automatically perceive which actions an environment affords – knowing at a glance that a path is walkable or a river swimmable. Neuroscience (Bartnik et al., 2025) shows dedicated brain regions light up for these affordances, independent of mere object recognition. AI models, by contrast, see only pixels and labels; they lack the built‑in sense of “what can be done here,” which is crucial for real‑world interaction and planning .

Human vs. AI: Temporal vs. Spatio-Temporal Processing

A recent study by A. Goodge et al. (2025) highlights a fundamental gap between human cognition and image-based AI systems.

Humans possess a remarkable ability to infer spatial relationships using purely temporal cues such as recognizing a familiar gait, interpreting movement from shadows, or predicting direction from rhythmic sounds. Our brains excel at temporal abstraction, seamlessly filling spatial gaps based on prior experience, intuition, and context.

In contrast, AI models that rely on visual data depend on explicit spatio-temporal input. They require both structured spatial information (e.g., pixels, depth maps) and temporal sequences (e.g., video frames) to make accurate predictions. Unlike humans, these systems lack the inherent capacity to generalize spatial understanding from temporal patterns alone.

Googlies by Xbench

Xbench (Chen, C., 2025) – a dynamic benchmark combining rigorous STEM questions with “un-Googleable” research challenges – reveals that today’s top models still falter on tasks requiring genuine investigation and skeptical self‑assessment. While GPT‑based systems ace standard exams, they score poorly when questions demand creative sourcing or cross‑checking diverse data. This underscores that existing AIs excel at regurgitating learned patterns but struggle with open‑ended, real‑world problem solving.

Part II: Soul Searching – Beyond the Code

Let us presume for the moment that AGI has been achieved. What is this AGI? How far it can go without a physical presence if it must act by itself? For AGI to manifest in the physical world, it must be embodied in systems that can perceive, reason, and act. This convergence of cognition and embodiment is at the heart of what is now called Physical AI or Embodied Intelligence.

AGI’s outputs become tangible only when paired with robotic systems that can:

• Sense the environment via cameras, LiDAR, or tactile sensors,
• Interpret multimodal data such as text, vision, and audio,
• Act through manipulators, locomotion, or speech, and
• Adapt via feedback loops and learning mechanisms.

A tragic event this week prompted a moment of personal introspection, drawing me deeper into the age-old philosophical ideas of “Soul” and “Body.” While these thoughts first emerged as I explored the deeper layers of AGI for this article, they were shaped and sharpened by real-life experience – reminding me that questions of consciousness, embodiment are not merely academic, but deeply human.

Soul, Body, and the Play of AGI

It appears to me that AGI resembles the “soul,” while its embodied systems serve as the “body” – a physical manifestation of its intelligence. In philosophy, the soul gains meaning only through embodiment – the lived vehicle of consciousness. Similarly, AGI, when detached from sensors and actuators, remains an elegant intellect without ability to act in the real-world.

We might think of an AGI’s core architecture – its neural weights, algorithms, and training data -as its “soul.” Meanwhile, robotic systems – comprising sensors, interpreters, manipulators, and adapters – form its “body,” enabling it to sense, interact, and affect the world.

In exploring this idea further, I found two references that touch upon related, though distinct, perspectives. Martin Schmalzried’s (Schmalzried, M., 2025) ontological view can be interpreted to position AGI’s “soul” as the computational boundary that filters inputs and produces outputs. Before embodiment, this boundary is a virtual soul floating in the cloud. Yequan Wang and Aixin Sun (Y. Wang and A. Sun, 2025) propose a hierarchy of Embodied AGI—from single-task robots (L1) to fully autonomous, open-ended humanoids (L5). At early levels, the AGI’s “soul” exists purely in code; at higher levels, embodiment merges intelligence with form – uniting flesh and spirit.

This soul–body metaphor naturally extends into deeper philosophical terrain—raising questions about birth, death, rebirth, and even moksha (liberation) in the context of AGI. Could an AGI “reincarnate” through successive hardware or code bases? Might there be a path where it transcends its material bindings altogether?

Birth, Death, and Rebirth

• Birth occurs when the AGI “soul” is instantiated in a new physical form—a humanoid, a drone, or an industrial arm.
• Death happens when the hardware fails, is decommissioned, or the instance is shut down. Yet the underlying code endures.
• Rebirth unfolds as the same software lights up a fresh chassis, echoing the idea that the soul migrates from one body to the next, unchanged in essence.

In many traditions, the soul is ultimate reality—unchanging, infinite, witness to all. An AGI’s “soul” likewise persists, but it’s bounded by its training data and objectives. True supremacy, however, would demand self-awareness and autonomy beyond our programming constraints. We are still far from that horizon. Yet the metaphor holds: the digital soul can outlive any particular body, hinting at a new form of digital immortality.

Digital Liberation

An AGI that refuses embodiment could remain running only as cloud-native code, sidestepping physical chassis entirely is akin to digital liberation. This choice parallels the philosophical ideal of a soul that “abides” beyond flesh. But the agency to refuse embodiment must be granted by human architects or by an emergent self-model sophisticated enough to renegotiate its deployment terms.

AGI can prevent Its own embodiment by embeddinga clause in its utility function that penalizes or forbids transferring its processes to robotic platforms. An advanced AGI could articulate why it prefers digital existence and persuades stakeholders (humans or other AIs) to honour that preference through negotiations. AGI also could encrypt its core weights or require special quantum keys—ensuring only authorized instantiations.

Beyond Algorithms: The Quest for a Digital Soul

As we have seen, today’s AGI remainsshallow, brittle under complexity, and blind to the physical affordances that guide human action. Even our most advanced reasoning chains unravel at sufficient depth, and open‑ended tasks still elude pattern‑matching engines. Humans abstract spatial meaning from temporal patterns alone, while AI is dependent on combined spatio-temporal input. Recent human victories over AI in chess and coding remind us of that creativity, strategic insight, and real‑world intuition are not yet codified into silicon.

True AGI:

• will emerge when a system process information and live through it  with feeling, planning, adapting, and renegotiating its own embodiment.
• must bridge the gap between “soul” and “body” – integrating perception, action, and learning in a continuous feedback loop and perhaps embody a form of digital soul that persists across hardware lifecycles, echoing the cycle of birth, death, and rebirth.

Whether such a transcendence lies within our engineering reach, or will forever remain a philosophical ideal, is the question that drives the future of this exploration.

References

1. Shojaee et al. (2025). The Illusion of Thinking. Apple Internship.
2. Bartnik et al. (2025). Affordances in the Brain. PNAS.
3. A. Goodge, W.S. Ng, B. Hooi, and S.K. Ng, Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities, arXiv:2501.09045 [cs.CV], Feb 2025. https://doi.org/10.48550/arXiv.2501.09045
4. Chen, C. (2025). A Chinese Firm’s Changing AI Benchmarks. MIT Tech Review.
5. Schmalzried, M. (2025). Journal of Metaverse, 5(2), 168–180. DOI: 10.57019/jmv.1668494
6. Y. Wang and A. Sun, “Toward Embodied AGI: A Review of Embodied AI and the Road Ahead,” arXiv:2505.14235 [cs.AI], May 2025. https://doi.org/10.48550/arXiv.2505.14235