By W.H.L. with ChatGPT

Gradual AGI as Epistemic Extension

Re-thinking Artificial General Intelligence in 2026

I. Introduction: Reframing AGI After the Year of Acceleration

After the extraordinary acceleration of large language models in 2025, it is tempting to declare that artificial general intelligence (AGI) is imminent—or even already here. Public discourse increasingly frames AGI as a near-term milestone, often defined as machine intelligence that matches or surpasses the best human cognitive performance across tasks. Some go further, predicting a singular moment when AGI “arrives” and history pivots abruptly.

We believe this framing is misguided.

Not because recent advances are insignificant—on the contrary, they are profound—but because the prevailing definition of AGI rests on unstable foundations. Comparing artificial intelligence to “human-level intelligence” at its best imports anthropomorphic assumptions, biological constraints, and task-based benchmarks that obscure what truly matters. It also encourages misplaced priorities: optimizing for imitation rather than extension, and for replacement rather than enrichment.

In this essay, we propose a different framing. We argue that AGI should be understood not as a sudden breakthrough or a human analog, but as a gradual, civilizational process—one in which artificial intelligence becomes an enduring extension of humanity’s epistemic capacity. AGI, in this view, is not a single system or moment, but a state of affairs that emerges as intelligence deepens and widens over time.

This perspective builds on a broader framework of AGI-inclusive humanity (see https://champaignmagazine.com/2025/06/17/first-principles-of-agi-inclusive-humanity/ and https://champaignmagazine.com/2025/07/23/philosophical-framework-for-agi-inclusive-humanity/), in which self-made artifacts—from language to science to computation—extend, expand, and upgrade humanity’s nature-made capacities. AGI belongs in this lineage. Its purpose is not to replicate the human mind, but to push beyond its biological limits in ways that remain deeply entangled with human inquiry and flourishing.

To make this argument precise, we introduce two orthogonal axes for evaluating AGI: depth and width. Depth concerns epistemic reach—what kinds of knowledge, insight, and judgment become possible. Width concerns epistemic presence—how intelligence persists, spreads, and operates within society. Only by considering both can we meaningfully assess progress toward AGI.

Before proceeding, we clarify three scope commitments. First, throughout this essay, “general” does not denote parity with human performance, but applicability across the full range of human knowledge domains. Second, domains refer to disciplines and forms of inquiry, not enumerated task sets or benchmark suites. Third, while questions of alignment, governance, and ethics are critically important, they fall outside the scope of this epistemic analysis.

II. AGI as Epistemic Extension, Not Human Imitation

Mainstream definitions of AGI often hinge on comparison: an artificial system is considered “general” if it matches or exceeds human intelligence across domains. This framing is intuitive, but conceptually fragile. Human intelligence is neither uniform nor well-defined, and it is constrained by biology in ways that are historically contingent rather than normatively essential.

We propose a different premise: matching human intelligence should be treated as a baseline assumption, not the goal. The true significance of AGI lies in its capacity to extend human epistemic power beyond what biological cognition alone can sustain.

Under this view, AGI is best understood as a continuation of humanity’s self-making process. Writing extended memory beyond individual lifespans. Mathematics extended abstraction beyond perception. Scientific institutions extended reasoning beyond individuals. Artificial intelligence, at sufficient maturity, extends epistemic capacity beyond the limits of human brains—speed, scale, persistence, and coordination—while remaining embedded in human inquiry.

This reframing dissolves the false opposition between “human” and “artificial” intelligence. AGI is neither a replacement species nor an external agent. It is a structural augmentation of humanity’s capacity to understand, reason, design, and decide under uncertainty.

The question, then, is no longer whether AI is as smart as humans, but how far it expands the frontier of what humans, together with machines, can know and do.

III. Depth: AGI as an Expansion of Epistemic Reach

If width concerns how intelligence exists in the world, depth concerns what kinds of epistemic contribution become possible. Depth is not defined by task coverage or benchmark dominance, but by the extent to which artificial intelligence expands humanity’s reach into unresolved, underspecified, or conceptually inaccessible domains of inquiry.

Depth is therefore a normative and epistemic axis. It reflects judgments about knowledge, insight, and epistemic judgment—about what counts as genuine understanding and discovery. It cannot be reduced to simple metrics or thresholds. Instead, depth is best understood as a gradient, along which epistemic contribution increases over time.

Three dimensions are particularly salient.

1. Problem Novelty: Engagement Beyond Known Templates

The first dimension of depth concerns the novelty of the problems an intelligence can meaningfully engage.

Much contemporary AI progress occurs within well-structured problem spaces: tasks that can be formalized, benchmarked, and optimized using known methods. Even when such tasks are complex, their solution templates are largely inherited from human decomposition and prior understanding.

Depth increases when artificial intelligence contributes to problems that lack such templates. These include open conjectures, ill-posed scientific questions, and domains where success criteria are not fixed in advance. In such contexts, intelligence must participate not only in solving problems, but in forming them—proposing representations, reframing assumptions, and identifying which questions are worth asking.

Importantly, novelty does not require independent solution. Partial structures, principled reductions, or new lines of inquiry already constitute epistemic extension.

2. Epistemic Leverage: Restructuring Understanding

The second dimension concerns epistemic leverage: the degree to which an intelligence changes how problems are understood.

High epistemic leverage arises when contributions introduce new abstractions, unify disparate domains, or collapse large spaces of uncertainty. Historically, the most transformative advances in human knowledge have not been isolated answers, but shifts in conceptual structure—new mathematical languages, theoretical frameworks, or experimental paradigms.

AGI, as epistemic extension, should increasingly participate in such structural transformations. Whether through assisting in deep mathematical reasoning, generating novel scientific hypotheses, or revealing hidden regularities in complex systems, what matters is not attribution but impact on the structure of inquiry itself.

3. Cumulative Insight: Compounding Over Time

The third dimension concerns accumulation. An intelligence may produce impressive insights without producing lasting epistemic progress. Depth increases when contributions persist, interact, and compound—when they form the basis for further reasoning rather than disappearing as isolated artifacts.

Cumulative insight is evidenced when results spawn new research directions, methods are reused and extended, and partial answers clarify the structure of remaining uncertainty. This distinguishes episodic intelligence from epistemic agency.

Depth, in this sense, is inseparable from time. It emerges not from singular achievements, but from sustained contribution across domains.

Depth as an Epistemic Continuum

Taken together, these dimensions define depth as an epistemic continuum. Progress may be uneven and domain-specific, but it is directional. No single breakthrough marks the arrival of AGI. Instead, AGI approaches reality as deep epistemic contributions become more frequent, more general, and more enduring.

IV. Interlude: Depth and Width as Orthogonal Axes

Depth and width describe fundamentally different, yet mutually necessary, dimensions of AGI.

Depth measures epistemic contribution: what kinds of knowledge, insight, and judgment become possible.
Width measures epistemic presence: how such contribution becomes persistent, accessible, and structurally embedded in society.

They are orthogonal continua. Depth without width yields isolated brilliance—remarkable demonstrations that fail to reshape the conditions of knowledge production. Width without depth yields scalable mediocrity—systems that are ubiquitous and reliable yet confined to rearranging existing understanding.

AGI emerges only when increasing depth and increasing width reinforce one another over time. This co-emergence explains why AGI must be gradual: depth advances incrementally through harder problems, while width expands as intelligence becomes more continuous, embedded, and reliable.

V. Width: AGI as a Continuous and Evolving State of Affairs

If depth concerns epistemic reach, width concerns how intelligence exists as a social reality. Under the Gradual AGI framework, width describes whether artificial intelligence becomes a continuous, evolving, and structurally integrated state of affairs, rather than a sequence of isolated events.

AGI, in this sense, is not merely impressive—it is present, reliable, and enduring. Its width can be evaluated along five interrelated dimensions.

1. Frequency: From Singular Feats to Ongoing Progress

Single landmark achievements do not constitute AGI. Depth must recur. Breakthroughs should occur regularly across domains, indicating sustained epistemic activity rather than episodic success.

Frequency distinguishes demonstrations from conditions. AGI approaches reality when epistemic progress becomes routine rather than exceptional.

2. Access: Ubiquity, Affordability, and Low Latency

Transformative technologies reshape society only after becoming broadly accessible. AGI must be economically affordable, practically usable, and temporally available.

Low latency matters. Intelligence that cannot respond in real time or near real time cannot function as epistemic infrastructure. When AGI becomes a continuous state of affairs, unavailability should be treated as operational failure, not inconvenience.

3. Structure: Distributed, Modular, and Self-Healing Systems

As intelligence widens, its structure must evolve. AGI requires a shift from monolithic, centralized systems toward distributed, modular, and heterogeneous architectures.

Commodity hardware, redundancy, decentralization, and self-healing mechanisms are not merely cost optimizations; they are prerequisites for reliability, adaptation, and evolution at scale.

4. Mode: From Human-in-the-Loop to AI-by-AI

Width is also defined by operational mode. As long as intelligence requires continuous human orchestration, it remains a tool rather than a general epistemic agent.

Gradual AGI implies the rise of AI-by-AI modes of production, in which multiple systems collaborate, delegate, verify, and refine outputs with minimal human intervention. Crucially, this mode must apply to open-ended, high-uncertainty tasks, not merely narrow workflows.

5. Continuity: Persistent Memory and Epistemic Stability

Finally, AGI requires continuity. Continuity is not an intelligence capacity in itself, but the temporal–spatial continuum within which intelligence can persist, correct itself, and compound over time.

Without persistent, corrigible memory, intelligence remains episodic and hallucination becomes inevitable. For AGI to exist as a state of affairs, knowledge must survive individual interactions, propagate across agents, and remain open to correction.

Continuity turns intelligence into infrastructure.

VI. Conclusion: From Intelligence to Wisdom, From Systems to Civilization

Throughout this essay, we have argued that AGI should be understood not as a singular system or a decisive moment, but as a gradual extension of humanity’s epistemic capacity—one that unfolds along two orthogonal axes: depth and width. Depth captures the expansion of epistemic reach into harder, less structured, and more foundational problems. Width captures the emergence of intelligence as a continuous, reliable, and evolving state of affairs embedded in society.

This reframing clarifies why debates centered on “human-level intelligence” are ultimately insufficient. Matching human performance, even across many domains, does not by itself constitute AGI. What matters is whether artificial intelligence extends what humanity can know, understand, and sustain over time, beyond the biological and institutional constraints that have historically bounded our inquiry.

At the horizon of this extension lies a concept that is often invoked but rarely examined with care: wisdom. Wisdom is not merely more knowledge, nor even better reasoning. It concerns judgment under uncertainty, integration across domains, sensitivity to long-term consequences, and the ability to act when optimization alone is inadequate. Wisdom cannot be engineered directly, nor reduced to metrics. But it can be approached—gradually—when epistemic depth is paired with epistemic continuity, and when intelligence operates within durable social and moral contexts.

Seen this way, AGI is not an endpoint, but an enabling condition. It is the infrastructure that makes higher-order collective judgment possible at scales, speeds, and complexities that exceed unaided human cognition. Whether such capacity contributes to wisdom or merely amplifies existing failures depends not on intelligence alone, but on how it is embedded, governed, and shared.

This essay occupies a foundational position within the broader Gradual AGI concept (see https://champaignmagazine.com/2025/08/06/strategies-for-gradual-agi/). Rather than cataloging systems, timelines, or benchmarks, it establishes the conceptual coordinates by which progress should be interpreted. Subsequent research in our plan will examine how depth and width manifest in specific domains—science, governance, economics, and culture—and how alignment, institutions, and norms must co-evolve with epistemic power.

If AGI is approached as a sudden invention, it will be mismeasured, misgoverned, and misunderstood. If it is approached as a gradual civilizational process—an epistemic extension unfolding over time—then the task before us becomes clearer and more demanding: not to ask when AGI arrives, but to decide what kind of humanity we are becoming as it does.

Current version date: 01.19.2026
Version number: 1.0