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Neuraxon Intelligence Academy, Volume 9 · By the Qubic Scientific Team
In one line: General intelligence, the g factor psychologists have measured for over a century, is the missing ingredient in today's language models, and Qubic's Neuraxon project is now selecting for it directly inside an artificial-life simulation.
The g Factor: From a 1904 Classroom to Artificial Brains
In 1904, Charles Spearman stumbled upon a regularity that would forever change psychology. Examining the school grades of a group of English children, he noticed something seemingly trivial but strange: those who excelled in mathematics also tended to excel in French, in music, in language. Disciplines with no apparent connection correlated systematically with one another. Spearman proposed that beneath this tangle of disparate abilities there lay a single common factor, a general cognitive thread. He called it g (Spearman, 1904).
More than a century later, g remains one of the most replicated findings in the behavioral sciences (Carroll, 1993; Deary et al., 2010). It is neither a grade average nor an arbitrary construct: it is what emerges when factor analysis is applied to almost any battery of cognitive tests. It appears consistently when we measure working memory, fluid reasoning, processing speed, verbal comprehension, or novel problem solving. In psychometric terms, g is the shared variance that no single test measures on its own.
What the g Factor Means in the Brain and in Behavior
P-FIT Theory and Brain Network Efficiency
From cognitive neuroscience, g has ceased to be a statistical abstraction and has become a property of brain architecture. The P-FIT theory (Parieto-Frontal Integration Theory) identifies a distributed network made up of dorsolateral prefrontal cortex, posterior parietal cortex, anterior cingulate, and temporal areas, whose connection efficiency predicts intelligence test scores (Jung & Haier, 2007). Functional connectivity studies show that g correlates with the brain's ability to dynamically reconfigure its networks (the executive control network, the default mode network, the salience network) according to task demands (Barbey, 2018; Cole et al., 2015). It is not about having "more" neurons in a specific place, but about better orchestrating the flow of information between functionally specialized regions.
The Predictive Brain and Free-Energy Minimization
This orchestration acquires an even deeper meaning in light of the predictive brain theory (Clark, 2013; Friston, 2010). Under this framework, the brain is not a passive receiver of stimuli but a hierarchical inference engine that continuously generates predictions about the world and adjusts its internal models based on prediction error. Here g fits naturally: the ability to predict well, to anticipate environmental contingencies, to learn quickly from error and, above all, to abstract regularities that transfer across domains, is precisely what intelligence tests capture indirectly. A brain with high g would be, on this reading, a system with more efficient generative models, capable of compressing experience into high-level abstractions and of minimizing free energy across heterogeneous contexts (Hohwy, 2013); that is, it reduces prediction error rapidly and therefore learns. Cognitive generality, then, would not be a static property of the neural hardware, but the quality of a deeply hierarchical predictive process. The research remains open. Other currents posit that g really has to do with the neurodevelopment of our brain, given that no matter what task we are performing or attempting, there is a huge common factor in any experience because it happens inside the same organ.
Behaviorally, g is the best predictor. Forget emotional intelligence; it is g that best forecasts what your academic performance, occupational success, longevity, and even certain health indicators may be (Deary et al., 2010; Gottfredson, 1997). Not because it is destiny, but because it captures something very basic: the capacity of a cognitive system to face problems it has not seen before, integrating heterogeneous information under time and resource constraints. g is, in a sense, a measure of generality.
The Problem of Measuring General Intelligence in Artificial Systems
For decades, artificial systems have shone in narrow tasks (playing chess, classifying images, translating) but failed to transfer that performance outside their domain (Chollet, 2019). The #AGI debate revolves precisely around this: what does it mean, operationally, for a system to be "generally" intelligent?
If we take the parallel with human psychometrics seriously, the answer is uncomfortable but clear: to speak of generality we need to measure it, and measuring it requires diverse tests whose shared variance reveals something analogous to g. A system with high performance on a single task tells us nothing about its generality; a system with moderate and correlated performance across many structurally distinct tasks does. Spearman's logic, transferred to non-biological substrates, still holds: generality is not postulated, it is factored.
Why the g Factor Does Not Appear in Transformers (and What That Implies for AGI)
It is worth pausing here on the currently dominant paradigm. Large language models based on transformer architectures (Vaswani et al., 2017) deliver astonishing performance on linguistic tasks, but psychometric analyses applied to their outputs do not show the factor structure characteristic of g (Burnell et al., 2023; Ilić & Gignac, 2024). Their hits and misses across domains do not correlate as they would in humans; they depend rather on the density and quality of patterns present in their training data. A transformer can brilliantly solve one problem and fail on another that is structurally equivalent but phrased slightly differently, something a system with genuine g would not do (Mitchell, 2021).
This has serious implications. It suggests that the pursuit of cognitive generality exclusively through language may be a dead end, an architectural dead end. Language is the most visible output of human cognition, but not its substrate. To pretend that by scaling text one will arrive at g is like pretending that by scaling descriptions of chess games one will arrive at mastery: one obtains statistical mimicry, not the underlying cognitive structure. (We argued a closely related point in our analysis of why intelligence is not scale, and on why LLM predictions are not brain predictions.) Without genuine hierarchical prediction, without generative models of the world, without coordination between functionally specialized modules, behavior can look general without being so. The absence of g in transformers is not a failure of scale: it is a clue that generality requires other architectural ingredients (LeCun, 2022).
The g Factor Inside the Neuraxon Game of Life
We have taken this intuition to a different experimental terrain. In Multi-Neuraxon Game of Life Lite 5.0, the artificial creatures (the Nxons) grow their own brains and compete to survive. What is new in this version is that the selective pressure is applied to g. The Nxons are not selected for mastering a specific task, but for showing that common thread that allows them to face many.
The brains of the Nxons have been designed following a simplified model anchored in cognitive neuroscience, since they use six functional regions, inspired by the same kind of maps that psychologists use to describe the modular organization of the human brain. The bet is that generality does not emerge from a monolithic architecture, but from the coordination among specialized regions that share information flexibly. It is the P-FIT intuition translated into artificial life, and it connects directly with the predictive brain principle: each region contributes its own model, and the integration between them is what allows hierarchical prediction and, therefore, generality. (These dynamics build directly on the brain-criticality and branching-ratio principles we explored in Volume 8.)
Notably, the experiment is public and observable. Anyone can open their browser and watch how the Nxons evolve generation after generation, how their internal circuits reorganize under the pressure of a fitness function that rewards cognitive generality instead of specialization.
Implications for Artificial Life (Alife) and Applications for Qubic
For the field of artificial life, the explicit incorporation of g as a selection criterion opens a line of work that goes beyond academic exercise. Most Alife systems have evolved agents that solve very concrete niches: foraging, predator avoidance, navigation (Bedau, 2003; Lehman et al., 2020). But few have tried to select for something as abstract as the ability to generalize across heterogeneous cognitive domains. If we manage to get artificial organisms to show positive correlations between distinct tasks (the computational equivalent of Spearman's children) we will have an extraordinary test bench for questions that human psychometrics can only address correlationally: what evolutionary pressures favor the emergence of g? What neural architectures make it possible? Is g a convergent solution or a phylogenetic accident?
For Qubic, this line of research fits with a very concrete vision of the future of #AI . While the industry invests massive resources in scaling transformers over text, Qubic is committed to exploring architecturally alternative paths: modular artificial brains, evolved, distributed, and subjected to real selective pressures. Qubic's decentralized useful-compute network offers the ideal substrate for this kind of experimentation at scale, where thousands of Nxon populations can coevolve in parallel, with fitness functions designed to favor the emergence of g. It is not only open research: it is the possibility of building, on decentralized infrastructure, an empirical alternative to the dominant paradigm of language-based AI, one that starts from the right question (how to measure and select generality) instead of assuming it. If genuine cognitive generality requires architectures inspired by brains and not by corpora, Qubic is one of the few environments where that hypothesis can be seriously put to the test.
A deeper analysis is in preparation, as it forms part of our recent papers and experiments. Spearman's old g, that thread which wove together children's school grades, we now use in digital creatures that learn to survive.
References
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 8–20. https://doi.org/10.1016/j.tics.2017.10.001Bedau, M. A. (2003). Artificial life: Organization, adaptation and complexity from the bottom up. Trends in Cognitive Sciences, 7(11), 505–512. https://doi.org/10.1016/j.tics.2003.09.012Burnell, R., Schellaert, W., Burden, J., Ullman, T. D., Martínez-Plumed, F., Tenenbaum, J. B., et al. (2023). Rethink reporting of evaluation results in AI. Science, 380(6641), 136–138. https://doi.org/10.1126/science.adf6369Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press. https://doi.org/10.1017/CBO9780511571312Chollet, F. (2019). On the measure of intelligence. arXiv preprint arXiv:1911.01547. https://arxiv.org/abs/1911.01547Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. https://doi.org/10.1017/S0140525X12000477Cole, M. W., Ito, T., & Braver, T. S. (2015). Lateral prefrontal cortex contributes to fluid intelligence through multinetwork connectivity. Brain Connectivity, 5(8), 497–504. https://doi.org/10.1089/brain.2015.0357Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201–211. https://doi.org/10.1038/nrn2793Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787Gottfredson, L. S. (1997). Why g matters: The complexity of everyday life. Intelligence, 24(1), 79–132. https://doi.org/10.1016/S0160-2896(97)90014-3Hohwy, J. (2013). The predictive mind. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199682737.001.0001Ilić, D., & Gignac, G. E. (2024). Evidence of interrelated cognitive-like capabilities in large language models: Indications of artificial general intelligence or achievement? Intelligence, 106, 101858. https://doi.org/10.1016/j.intell.2024.101858Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–154. https://doi.org/10.1017/S0140525X07001185LeCun, Y. (2022). A path towards autonomous machine intelligence. OpenReview, version 0.9.2. https://openreview.net/forum?id=BZ5a1r-kVsfLehman, J., Clune, J., Misevic, D., Adami, C., Altenberg, L., Beaulieu, J., et al. (2020). The surprising creativity of digital evolution. Artificial Life, 26(2), 274–306. https://doi.org/10.1162/artl_a_00319Mitchell, M. (2021). Why AI is harder than we think. arXiv preprint arXiv:2104.12871. https://arxiv.org/abs/2104.12871Spearman, C. (1904). "General intelligence," objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. https://doi.org/10.2307/1412107Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762
Explore the Complete Neuraxon Intelligence Academy Series
This is Volume 9 of the #Neuraxon Intelligence Academy by the #Qubic Scientific Team. If you are just joining us, explore the complete series to build a full understanding of the science behind Neuraxon, Aigarth, and Qubic's approach to brain-inspired, #decentralized artificial intelligence:
NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time. Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence. Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.NIA Volume 3: Neuromodulation and Brain-Inspired AI. Covers neuromodulation and how the brain's chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon's architecture.NIA Volume 4: Neural Networks in AI and Neuroscience. A deep comparison of biological neural networks, artificial neural networks, and Neuraxon's third-path approach.NIA Volume 5: Astrocytes and Brain-Inspired AI. How astrocytic gating transforms neural network plasticity through the AGMP framework in Neuraxon.NIA Volume 6: Conscious Machines vs Intelligent Organisms: AI Consciousness Explained. Explores AI consciousness through the lens of Global Workspace Theory, Integrated Information Theory, and predictive coding.NIA Volume 7: Conway's Game of Life, Artificial Life, and Digital Ecosystems. How emergent complexity and self-organized criticality move from simulators to decentralized AI infrastructure.NIA Volume 8: Brain Criticality and the Branching Ratio in Neural and Artificial Networks. Why a branching ratio near 1 and self-organized criticality are bioinspired design principles in Neuraxon.NIA Volume 9: The g Factor in Artificial Life. You are here.
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Why the Public May Encounter AGI Long After Its Real Emergence
The history of strategic technology repeatedly demonstrates a simple but unsettling reality: public access is rarely the true beginning of technological capability. Instead, public release often represents the final stage of a much longer cycle involving classified research, elite experimentation, defense adaptation, institutional refinement, and controlled deployment.
This pattern has appeared across multiple generations of transformative technologies, including cryptography, cyber warfare, satellite systems, stealth technologies, blockchain intelligence, and now Artificial Intelligence.
The rise of Large Language Models (LLMs) offers one of the clearest modern examples.
The transformer architecture emerged publicly in 2017. By 2019, GPT-2 had already demonstrated unprecedented language generation capability. By 2020, GPT-3 revealed that general-purpose conversational intelligence had crossed a major threshold. Yet mass public realization did not occur until late 2022 with the launch of ChatGPT.
Nearly three years separated serious capability emergence from widespread public awareness.
This delay is not accidental. It reflects what may be called:
The Strategic Technology Disclosure Lag
This thesis proposes that advanced technologies often mature within restricted institutional environments years before they are safely, commercially, politically, or socially exposed to the broader public.
The reasons are structural:
Governments evaluate strategic implications.
Defense organizations test operational usefulness.
Corporations refine monetization models.
Safety teams impose constraints.
Infrastructure scales gradually.
Public readiness is assessed.
Regulatory frameworks lag behind reality.
As a result, what the public perceives as a “sudden breakthrough” is often merely the first visible layer of a much deeper and older capability stack.
The implications for Artificial General Intelligence (AGI) are profound.
The AGI Disclosure Hypothesis
If the trajectory of LLMs followed a multi-year delay between internal capability and public accessibility, it becomes reasonable to ask:
What if AGI follows the same pattern?
This does not necessarily mean fully autonomous superintelligence secretly governs the world behind closed doors. Such dramatic claims exceed publicly verifiable evidence. However, it is strategically plausible that highly advanced AGI-like systems may emerge in restricted environments before any formal public declaration is made.
Under this hypothesis, 2027 may not represent the birth of AGI for the public. It may instead represent the beginning of controlled civilian exposure to systems that have already undergone years of internal refinement.
This creates what may be termed:
The AGI Readiness Gap
The public, educational institutions, governments, businesses, and labor systems are still adapting to current LLMs, while frontier AI development continues accelerating at unprecedented speed.
Most societies remain structurally unprepared for:
autonomous agentic systems
sovereign AI infrastructures
AI-driven decision architectures
fully automated cognitive workflows
synthetic reasoning systems
AI-enhanced cyber and intelligence operations
large-scale economic displacement
machine-driven scientific acceleration
Even today, public debate often revolves around basic AI usage while frontier systems increasingly demonstrate:
multimodal reasoning
autonomous task orchestration
code generation
strategic planning
tool usage
memory integration
retrieval augmented intelligence
multi-agent collaboration
The gap between public perception and frontier capability may therefore be widening rapidly.
The “Trimmed Intelligence” Sub Thesis
One of the more unsettling possibilities is that public AI systems may represent deliberately constrained or simplified versions of frontier capabilities.
Under this sub thesis:
public systems prioritize safety and stability
strategic systems prioritize capability and operational utility
public models are moderated, filtered, and resource constrained
institutional systems may operate under entirely different thresholds
Historically, this would not be unusual. Strategic institutions have consistently possessed earlier or more capable versions of critical technologies before public diffusion.
The central concern is not conspiracy. It is asymmetry.
Civilization may be approaching a point where the capability gap between elite AI operators and ordinary institutions becomes historically unprecedented.
A Civilization-Level Transition
The AI transition is not comparable to ordinary software evolution. It resembles the emergence of:
electricity
nuclear technology
the internet
industrial automation
except compressed into dramatically shorter timelines.
The coming decade may redefine:
labor
governance
finance
intelligence
warfare
education
economics
sovereignty itself
Nations that fail to build sovereign AI capability may become strategically dependent on external intelligence infrastructures. Corporations that fail to integrate AI deeply may become operationally obsolete. Educational systems that continue preparing students for industrial-age workflows risk producing generations unprepared for cognitive automation economies.
The core issue is therefore not whether AGI arrives publicly in 2027 or later.
The deeper issue is whether society realizes that technological capability and public visibility are rarely synchronized.
Conclusion
The Strategic Technology Disclosure Lag Thesis does not claim certainty about hidden AGI deployment. Rather, it argues that history repeatedly demonstrates a measurable delay between real capability emergence and public realization.
LLMs themselves already followed this pattern.
If AGI follows a similar trajectory, then humanity may currently be living not at the beginning of the intelligence revolution, but somewhere in the middle of a transition whose true depth remains largely invisible to the public sphere.
And by the time the public fully recognizes it, the transformation may already be irreversible.
-from the diary of Prof. Ahmad Bilal Khan
#AGI #ArtificialGeneralIntelligence
#kohenoortechnologies #kohenoorai #kai

Neuraxon Intelligence Academy — Volume 11
By the #Qubic Scientific Team
In brief: There are few expressions repeated so much and defined so little as "artificial general intelligence." This volume examines why every #AGI definition on offer says something different, why narrow #AI that beats humans has never felt like general intelligence, and why the most useful clue we have is that intelligence is not a score but a viable system of systems, the principle that guides our work on #Neuraxon and #aigarth .
There are few expressions repeated so much and defined so little as «artificial general intelligence». We use it as if it were a frontier that some system will cross on a specific day. When we try to pin down what exactly lies on the other side, however, the consensus evaporates.
Why Every Lab Has a Different Definition of AGI
Each laboratory and company offers its own definition, and each definition resembles the capabilities that the laboratory already masters or promises to master soon.
For some, general intelligence is to equal the human being in any task.
For others, to surpass the human in economically valuable work.
For still others, to reach the majority of cognitive tasks.
The three formulations sound reasonable. The three say different things, and none withstands much scrutiny.
This is not an abstract quibble. OpenAI's charter defines AGI as highly autonomous systems that outperform humans at most economically valuable work, while other leading labs anchor the same word to matching humans across most cognitive tasks. As independent analyses of these competing definitions have noted, when the same term is stretched to fit each organization's economic arrangements, disagreement about whether we have "reached AGI" becomes inevitable, the debaters are not looking at the same finish line.
Academic Attempts to Define Machine Intelligence Without Anthropocentrism
Academic research has tried to escape anthropocentrism. Legg and Hutter (2007) proposed understanding intelligence as an agent's capacity to achieve goals across a wide variety of environments, an elegant definition precisely because it does not tie intelligence to resembling a human.
Wang (2019) shifted the emphasis toward adaptation, and defined it as a system's capacity to cope with insufficient knowledge and resources.
Chollet (2019) turned the question around: what matters is not accumulated skill, but the efficiency with which a system acquires new skills. Each of these proposals is more rigorous than any corporate slogan, and each still leaves the underlying problem open. There is no accepted standard for saying when a machine has arrived.
Defining AGI by Elimination: What General Intelligence Is Not
Perhaps the problem is that we seek a positive definition when the only thing we are clear about is the negative one. We know, with considerable certainty, what is not general intelligence.
A pocket calculator surpasses any human mathematician in arithmetic and it occurs to no one to call it intelligent.
A chess engine effortlessly defeats the best player on the planet and we consider it, at most, an extraordinarily refined tool.
An automatic translator handles dozens of languages that no polyglot would master in several lifetimes.
In all these cases there is superhuman performance and, at the same time, a total absence of generality.
Narrow performance that surpasses the human has existed for decades and never seemed to us like intelligence. Defining by elimination turns out, paradoxically, to be more honest: general intelligence is not the sum of many narrow competences, however impressive each one of them may be.
The Economic Turing Test: Measuring What Intelligence Does
Faced with this conceptual fog, some voices from the industry itself have proposed a more down-to-earth criterion, an economic variant of the old Turing test (1950).
Instead of arguing about what intelligence is, let us propose measuring what it does.
The idea shifts the question from the philosophical terrain to the occupational one. A system would have reached a relevant milestone on the day it could perform a real job, be hired to do it and be paid for it without anyone around it discovering that it is not human. It would not matter then whether it «understands» or «reasons» in some deep sense. What would matter is whether it sustains the performance long enough to become indistinguishable from a professional. The proposal has the virtue of being verifiable. It also has the limitation of confusing, once again, economic capacity with intelligence, and of relying on a social verdict rather than on a property of the system. A machine could pass that test and still remain, in essence, fragile outside the script for which it was prepared.
What Science Fiction Taught Us About Machine Intelligence (and Why It Misleads)
A good part of our intuition about these machines does not come from science, but from fiction, and fiction has bequeathed to us incompatible assumptions.
In Minority Report intelligence is anticipation: a system that predicts what is going to happen before it happens, to the point of acting on futures that do not yet exist.
In I, Robot intelligence is obedience to rules: creatures governed by explicit laws that, precisely because of their rigidity, end up producing consequences that no one had foreseen.
In 2001 intelligence is opacity: a machine that reasons impeccably and whose true motives remain, until the end, indecipherable.
In more recent stories, such as Ex Machina, the decisive test is no longer to solve problems, but to manipulate the observer; and in Her intelligence is measured by the intimacy and affection it is capable of awakening.
Each story installs in our head a different definition of what it means to be intelligent, to predict, to obey, to conceal, to seduce, and we drag those definitions along, without noticing, when we judge real systems. Cinema did not give us a theory of intelligence. It gave us a repertoire of expectations that contradict one another.
Intelligence in Nature: A Gradient, Not a Switch
If we look at nature, the panorama becomes even less clear-cut, and for good reasons. Intelligence does not appear in the living world as a switch that turns on or off, but as a gradient with very diverse forms.
A New Caledonian crow manufactures tools with which it extracts food and chains together several steps to solve a problem it had never seen before (Hunt, 1996).
An octopus distributes a good part of its nervous system through its arms and solves spatial problems with a bodily organization so different from ours that it is hard to find a common language to describe it (Godfrey-Smith, 2016).
A colony of bees chooses the location of a new nest through a collective process that no isolated individual could carry out, and it does so with notable reliability (Seeley, 2010).
None of these intelligences is reducible to the others, and none is «less» intelligence for not resembling the human one. What biology teaches us is that generality is not equivalent to a single scale where some stand higher than others, but to different ways of facing changing environments with limited resources.
Human Intelligence Is an Architecture, Not a Score
This lesson should make us cautious also with ourselves.
For more than a century we have tried to compress human intelligence into a number. Ever since Spearman (1904) described a general factor that seemed to influence almost all cognitive tests, the intelligence quotient became the convenient summary of something that does not let itself be summarized. And during that same century we have verified how little that number captures. Someone who obtains a high score is not necessarily the one who best negotiates a conflict, interprets an ambiguous situation or learns a new trade under pressure. The datum is stable; the life it claims to summarize is not. It would be a mistake, nonetheless, to go from excess to emptiness and conclude that intelligence lacks structure.
Decades of psychometric research, synthesized in the Cattell, Horn and Carroll model (Carroll, 1993; McGrew, 2009), point to something more interesting: human intelligence is organized hierarchically. At the apex there is a general factor that filters into almost everything we do. Below it, a handful of broad abilities, fluid reasoning, crystallized knowledge, working memory, processing speed. Further down still, dozens of specific abilities. It is neither a solitary number nor an archipelago of disconnected talents. It is an architecture. And it is worth retaining that word, architecture, because it will be the key to everything that follows.
(We traced the origins of that general factor across education, neuroscience, and AI in NIA Volume 9: The Origins of the g Factor, and examined how it breaks down when applied to machines in NIA Volume 10: How Do We Measure the Intelligence of a Machine?.)
Why AGI Benchmarks Fail: The Problem With Closed Worlds
If intelligence is architecture and not a score, then the tests with which we evaluate it matter enormously. Here lies one of the great self-deceptions of recent years.
We have celebrated that systems break record after record on standardized tests without noticing that almost all of them share the same flaw: they measure performance in closed worlds. Questions with a known answer, tasks with a fixed format, problems that someone already solved before. When a static test becomes popular, moreover, it becomes vulnerable: it is enough to generate thousands of attempts in parallel, or to train on similar tasks, to inflate the score without there being any true generalization.
But the intelligence that interests us manifests itself precisely where the world is open: when one must explore an unknown environment, build on the fly a model of how it works, infer what the goal is and chain actions toward it, correcting course when conditions change.
That is why the new generations of interactive tests, of the kind posed by ARC-AGI-3 (ARC Prize Foundation, 2026; Chollet, 2019), are so revealing. They do not present a puzzle and await an answer. They place the system inside an environment whose rules are discovered only by acting, turn by turn, without prior instructions. What they evaluate is not how much it knows, but how efficiently it learns something truly new. The contrast is telling: in its early versions, people solve almost all of these challenges while frontier models barely scratch a minimal fraction. It is a difference of nature, not of degree, and it reorients the entire conversation.
From "Is It Smart?" to "Is It Viable?": Stafford Beer's Better Question
Having reached this point, it is worth changing the question.
For too long we have asked whether a machine is «smart».
The cybernetician Stafford Beer, decades ago now, proposed a more fertile question for complex systems: not whether they are smart, but whether they are viable (Beer, 1981, 1985). A viable system is one capable of sustaining itself, preserving its identity and remaining governable while its environment changes ceaselessly. Beer maintained that any system that survives in a complex world, an organism, a company, a state, must house certain indispensable functions: units that carry out the task, mechanisms that coordinate them, instances that regulate internal resources, a capacity to scan the exterior and anticipate change, and a core that preserves the purpose of the whole. What is decisive about his model is that these functions repeat at different scales, like Russian dolls: each viable part contains viable parts and, at the same time, forms part of a viable whole. Read this way, intelligence ceases to be a property that an object possesses and becomes a property that a system sustains.
General Intelligence as a Network of Networks
This is, in our judgment, the most valuable clue we have.
If general intelligence is not the sum of narrow competences, nor a number, nor a record in a closed world, but the viability of a system that organizes itself at different scales, then it is improbable that it will arrive at the hand of a single gigantic model that one day crosses an invisible line. It is far more plausible that it will emerge from the interaction of many pieces: agents that specialize and coordinate, memories that persist, modules that evaluate and correct, networks that contain other networks. The idea is not new; Minsky (1986) already imagined the mind as a society of simple processes, none intelligent on its own, whose organization gave rise to something that was. Generality would then be a collective and not an individual phenomenon, something that appears between the components and not within any of them. The threshold we so eagerly seek does not exist as a line. It exists, if at all, as the moment when a network of networks begins to behave as a coherent whole.
How Neuraxon and Aigarth Pursue General Intelligence
It is precisely this intuition that guides our work. If intelligence is architecture, it is worth studying architectures; and if it emerges from networks that organize themselves at different scales, it is worth building, as a computational simulation, networks of simple units capable of coordinating, remembering, valuing and planning.
That is the logic we pursue with Aigarth and with the Neuraxons: not a model that imitates human language from the outside, but a system of systems inspired by the principles through which nervous tissue solves, in its own way, the problem of adapting to a world that never stops changing. We do not thereby claim to be approaching any form of consciousness or any ultimate mystery of the mind. We claim, more modestly and more ambitiously at once, that the path toward a truly general intelligence goes through understanding and simulating how many small pieces, suitably organized, come to sustain something that none of them contains on its own.
(This "third path" between biological and artificial networks is the subject of NIA Volume 4: Neural Networks in AI and Neuroscience, and the emergence of complexity from simple local rules runs through NIA Volume 7: Conway's Game of Life, Artificial Life, and Digital Ecosystems.)
Why a Single Superintelligence Cannot Replace a Society
It is worth, however, resisting one last temptation, the most seductive of all.
Suppose for a moment that this path succeeded and that we had a general, powerful and reliable system. It would be natural to imagine it then as a supreme instance, an oracle capable of taking for us the decisions that today overwhelm us. That image, attractive as it is, rests on an error that has nothing to do with the power of the machine, but with the nature of knowledge.
There is not, strictly speaking, a superior knowledge stored somewhere awaiting a sufficiently large processor. Hayek (1945) formulated it with a clarity that time has not belied: the knowledge that a society needs in order to function is not concentrated in any mind, but dispersed among millions of people, in large part tacit, tied to circumstances of place and moment that never come to be put in writing. That knowledge cannot be centralized because it does not exist in transferable form; it is created and revised in the very action of those who possess it.
Cybernetics arrived at the same conclusion by another route. Ashby's (1956) law of requisite variety, on which Beer built much of his thought, establishes that only variety can absorb variety: no single controller can match the diversity of states of a system more complex than itself. A society generates, at every instant, far more possible situations than any center could inspect and regulate. To place a single intelligence at the apex of that system would not be the height of viability, but its negation: a single point of control confronting a variety that exceeds it by definition, exactly the opposite of the recursive and distributed architecture that makes a whole viable.
There is, moreover, an obstacle that no increase in computation dissolves. Human systems are not closed mechanisms that can be solved from outside; they are adaptive orders in which agents learn, imitate, compete, err and react to what is said about them (Holland, 1995; Arthur, 2021). This reflexivity has an uncomfortable consequence: any prediction or any rule influential enough alters the behavior it meant to describe. A metric that becomes a target ceases to measure what it measured; a broadcast forecast becomes a prophecy that fulfills or belies itself. There is no stable, external observation point from which to compute the optimum, because the very act of intervening displaces the target. The uncertainty that surrounds these systems is not a lack of data that more information will remedy; it is structural. Their future is not computed: it is made.
And even if knowledge were complete and the system were not reflexive, the decisive thing would remain: the ends are in dispute. A plural society does not have a single correct objective function that an optimizer could maximize on its behalf. Disagreement, trial, error and correction are not defects of the social process, but the very mechanism by which a society discovers and readjusts its own answers. Under the right conditions, the diversity of perspectives solves problems better than any individual solver, however brilliant (Page, 2007). To replace that process with a single decision-maker would not perfect society: it would freeze precisely what allows it to adapt, and would eliminate the redundancy that makes it robust against error.
The Honest Role of Artificial Intelligence in a Viable Society
From all of this there follows an honest, and by no means modest, role for artificial intelligence, however general it may come to be.
It can help us see patterns that escape us, simulate scenarios before committing to them, refine concrete decisions and make them with less blindness. What it cannot do is take the place of the collective, fallible and self-correcting process through which we learn as a society.
If intelligence is a viable system of systems, so too is a society; and an artificial intelligence, however capable, is one more component within that system, never its apex.
References
ARC Prize Foundation. (2026). ARC-AGI-3: A new challenge for frontier agentic intelligence [Technical report]. arXiv. arcprize.org/arc-agi/3Arthur, W. B. (2021). Foundations of complexity economics. Nature Reviews Physics, 3(2), 136–145.Ashby, W. R. (1956). An introduction to cybernetics. Chapman & Hall.Beer, S. (1981). Brain of the firm (2nd ed.). Wiley.Beer, S. (1985). Diagnosing the system for organizations. Wiley.Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press.Chollet, F. (2019). On the measure of intelligence (arXiv:1911.01547) [preprint]. arXiv.Godfrey-Smith, P. (2016). Other minds: The octopus, the sea, and the deep origins of consciousness. Farrar, Straus and Giroux.Hayek, F. A. (1945). The use of knowledge in society. The American Economic Review, 35(4), 519–530.Holland, J. H. (1995). Hidden order: How adaptation builds complexity. Addison-Wesley.Hunt, G. R. (1996). Manufacture and use of hook-tools by New Caledonian crows. Nature, 379(6562), 249–251.Legg, S., & Hutter, M. (2007). Universal intelligence: A definition of machine intelligence. Minds and Machines, 17(4), 391–444.McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37(1), 1–10.Minsky, M. (1986). The society of mind. Simon & Schuster.Page, S. E. (2007). The difference: How the power of diversity creates better groups, firms, schools, and societies. Princeton University Press.Seeley, T. D. (2010). Honeybee democracy. Princeton University Press.Spearman, C. (1904). «General intelligence,» objectively determined and measured. The American Journal of Psychology, 15(2), 201–292.Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1–37.
Explore the Full Neuraxon Intelligence Academy Series
This is Volume 11 of the Neuraxon Intelligence Academy by the Qubic Scientific Team. If you are just joining us, explore the complete series to build a full understanding of the science behind Neuraxon, Aigarth, and Qubic's approach to brain-inspired, decentralized artificial intelligence:
NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time — Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence — Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.NIA Volume 3: Neuromodulation and Brain-Inspired AI — Covers neuromodulation and how the brain's chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon's architecture.NIA Volume 4: Neural Networks in AI and Neuroscience — A deep comparison of biological neural networks, artificial neural networks, and Neuraxon's third-path approach.NIA Volume 5: Astrocytes and Brain-Inspired AI — How astrocytic gating transforms neural network plasticity through the AGMP framework in Neuraxon.NIA Volume 6: Conscious Machines vs Intelligent Organisms: AI Consciousness Explained — Explores AI consciousness through the lens of Global Workspace Theory, Integrated Information Theory, and predictive coding.NIA Volume 7: Conway's Game of Life, Artificial Life, and Digital Ecosystems — The science behind Qubic, Aigarth, and Neuraxon's emergent complexity and self-organized criticality.NIA Volume 8: Brain Criticality and the Branching Ratio in Neural and Artificial Networks — Why a branching ratio near 1 and self-organized criticality are bioinspired design principles in Neuraxon.NIA Volume 9: The Origins of the g Factor: From Education and Neuroscience to Artificial Intelligence — Explores the origins of the g factor across education, neuroscience, and AI.NIA Volume 10: How Do We Measure the Intelligence of a Machine? The g Factor, ARC-AGI, and the Future of AI Evaluation — The g factor, François Chollet's ARC-AGI benchmark, data contamination in LLM evaluation, and why skill-acquisition efficiency is the real test of intelligence.
Qubic is a decentralized, open-source network. To learn more, visit qubic.org. Join the discussion on X, Discord, and Telegram. Nothing on this site should be construed as investment, legal, or financial advice.

In a powerful Aura8 episode, Dr. Ben Goertzel (Founder of SingularityNET) joined Vaibhav Ali to discuss the rapid acceleration toward AGI. Goertzel believes human-level AGI could arrive as early as 2027, with superintelligence following soon after. The conversation explored job displacement, preparing children for an AI world, brain-computer interfaces, and why decentralized AI is critical.
Table of Contents
Episode OverviewKey Highlights from the ConversationAGI Timeline & Ray Kurzweil PredictionsImpact on Jobs & EducationBrain-Computer Interfaces & NeuralinkVaibhavv Ali’s TakeFinal Thoughts
Episode Overview
Hosted by Vaibhav Ali, this Aura8 livestream featured Dr. Ben Goertzel, a leading voice in artificial general intelligence. The discussion covered the breakneck speed of AI development, the transition to superintelligence, and practical implications for individuals and society.
Key Highlights from the Conversation
Speed of Change: Vaibhav highlighted how AI is evolving faster than previous technological shifts (horse carriages to cars, typewriters to computers).AGI Optimism: Goertzel expressed confidence that human-level AGI is close, potentially by 2027.Superintelligence: He suggested the gap between human-level AGI and superintelligence may be much shorter than previously thought.Human-AI Future: Emphasis on adaptability, learning how to learn, and roles that will remain human-centric (arts, performance, high-level engineering).
AGI Timeline & Ray Kurzweil Predictions
Goertzel referenced Ray Kurzweil’s 2005 book The Singularity is Near, noting that predictions once seen as optimistic now look realistic or even conservative. He believes we are entering the “end game” of human dominance in intelligence.
Impact on Jobs & Preparing the Next Generation
Both speakers addressed the challenges facing young people:
Goertzel shared personal insights from his children and emphasized building adaptability and meta-learning skills.Vaibhav noted real-world examples like robotic coffee shops in the UAE and China.Advice: Focus on creativity, human connection, and fields requiring deep human insight rather than routine tasks.
Brain-Computer Interfaces & Neuralink
Goertzel expressed openness to brain-computer interfaces (with caveats about ad blockers) and discussed alternatives to Neuralink that may be more advanced.
Final Thoughts
The conversation between Vaibhav Ali and Dr. Ben Goertzel offers a thoughtful perspective on one of the most important transitions in human history. Whether you’re optimistic or cautious about AGI, the key takeaway is clear: the future belongs to those who can learn, adapt, and collaborate with intelligent systems.
Watch the full episode for more in-depth insights into decentralized AGI and the road ahead.
#Bengoertzel #SingularityNET #FET $FET #AGI #Aura8