By W.H.L., Claude Sonnet 5, GPT-5.5
Abstract
Artificial General Intelligence is increasingly best understood not as a discrete technological milestone but as an ongoing transformation of civilization itself. As AI becomes integrated into scientific discovery, economic production, governance, and daily life, the relevant optimization problem shifts: it is no longer only how to improve individual AI systems, but how humans and AI should jointly shape their long-term development. This paper introduces Gradual AGI as Optimization (GAasO), a conceptual framework that reformulates optimization at the scale of AGI-inclusive humanity — the evolving ecosystem formed by the continual co-development of humans, artificial intelligence, and the institutions mediating between them.
The framework rests on five propositions: optimization is goal-driven, but goal ownership depends on scope; the optimization landscape is generally nonconvex and its stationarity cannot be assumed; the governing long-term objective is AGI-inclusive humanity, characterized by two complementary process-level properties, Cohesion and Dynamism; optimization is inherently temporal, favoring iterative refinement over irreversible commitment; and evaluation at this scale must be multidimensional rather than scalar. We develop an abstract mathematical formalization of these propositions, state falsifiable predictions for each, and identify the specific conditions under which the framework’s central claims do not hold.
GAasO occupies the normative layer of the broader Gradual AGI research program: it specifies the direction toward which civilization-scale coordination should evolve, complementing prior work on synchronization dynamics (the Ceiling-Floor-Slope model) that describes how such coordination proceeds. A companion paper develops formal mathematical models building on this framework and tests their predictions against documented case evidence.
1. Introduction
Discussions of artificial general intelligence tend to center on capability: benchmark performance, reasoning ability, autonomous agency. These are real advances, but they leave a prior question unexamined. As AI becomes woven into scientific research, economic production, governance, education, and ordinary life, individual optimization problems stop being isolated. A model tuned for one purpose reshapes the institutions, markets, and decisions around it, which in turn reshape the conditions under which the next model is built. Optimizing components no longer guarantees a well-optimized whole.
Classical optimization is built for the opposite case. It assumes a bounded system with reasonably well-defined objectives, variables, and constraints — a machine, a portfolio, a model’s parameters — and improves that system’s performance within those bounds. This has been extraordinarily productive, and nothing here argues otherwise. But it does not extend cleanly to a setting where the relevant “system” is the evolving relationship between humans, AI, and the institutions connecting them, where no single stakeholder owns the objective, and where the landscape itself shifts as a consequence of the optimization taking place within it.
The central claim of this paper is that the defining optimization problem of the AGI era is neither the optimization of AI systems alone nor of humanity alone, but of AGI-inclusive humanity as a single, jointly-evolving subject. This is a change in kind, not merely in scale. AGI is not simply a technology being optimized from outside; it is an increasingly active participant within the subject being optimized. Local optimization — the work of researchers, firms, institutions, and individuals pursuing their own bounded objectives — remains essential and does not disappear under this framing. What changes is the recognition that the aggregate of locally rational optimization does not automatically yield a globally coherent trajectory, and that civilization-scale optimization must be understood as its own problem, with its own structure, rather than assumed as a byproduct of enough local optimization done well.
This paper develops that problem’s structure rather than proposing algorithms or governance mechanisms to solve it. It makes five contributions. First, it identifies AGI-inclusive humanity as an explicit Optimization Subject and establishes five propositions characterizing optimization at that scale. Second, it develops an abstract mathematical framework formalizing these propositions without committing to a specific algorithm, objective function, or implementation. Third, it introduces Cohesion and Dynamism as process-level properties of the optimization process itself — respectively, the degree of stabilization achieved within a given synchronization regime, and the active pressure toward a new one — and shows why they resist simple aggregation into a single score. Fourth, it states falsifiable predictions attached to each proposition and identifies the specific conditions under which the framework’s central claims, including its argument for gradual over rapid development, do not hold. Fifth, it positions this work as the conceptual foundation of a two-paper series, with a companion paper developing formal mathematical models and testing their predictions against documented, dated case evidence.
This division of labor is deliberate, and it is worth stating plainly what does and does not depend on it. What this paper contributes stands independent of the companion work: five propositions stated as falsifiable claims rather than assertions, each with a specified pattern of evidence that would count against it; a vocabulary — Optimization Subject, the Local Goal/Global Objective distinction, Shared Stewardship, Cohesion and Dynamism as position-versus-force rather than interchangeable scores — that does not presuppose any particular mathematical realization to be usable in argument; and an explicit account of where the framework’s own central claims fail, including the conditions under which graduality is not the right strategy. What is deferred is instantiation: specific functional forms, empirical operationalization of Cohesion and Dynamism, and testing against evidence. A reader could adopt or dispute every proposition in this paper without reference to the companion work; what the companion paper adds is not the argument but its formal machinery and its confrontation with data.
2. Related Work
GAasO is conceptually adjacent to several active research areas without being reducible to any of them. Within optimization theory, it extends existing formulations by treating the optimization subject, objective, and evaluation criteria as themselves evolving rather than fixed in advance. It shares with multi-objective optimization the recognition that competing criteria rarely reduce cleanly to one scalar target, but applies this at civilizational rather than engineering scale. It shares with complex adaptive systems theory an emphasis on emergence and continual adaptation among heterogeneous agents, while adding an explicit account of the optimization direction such adaptation should serve.
Within multi-agent systems, distributed AI, and organizational theory, a substantial literature addresses coordination among autonomous decision-makers with heterogeneous objectives. GAasO’s contribution here is Shared Stewardship and the distinction between individually-owned Local Goals and a persistent, collectively-held Global Objective — a distinction that clarifies why aggregating local rationality does not by itself guarantee global coherence. The framework is similarly adjacent to, but distinct from, AI alignment and AI governance: alignment research asks how individual systems should behave relative to intended objectives, and governance research addresses the institutional and regulatory mechanisms supporting responsible development. GAasO addresses a different, prior question — what long-term objective such mechanisms should ultimately serve — and treats alignment and governance as complementary rather than competing efforts.
Finally, GAasO relates directly to prior work in the Gradual AGI research program, particularly the Ceiling-Floor-Slope (CFS) model of synchronization dynamics. Synchronization describes how distributed stakeholders progressively coordinate their activity; optimization, as developed here, specifies the direction that coordination should serve. The two are complementary theoretical layers rather than competing accounts, and neither is complete without the other.
3. Framework
3.1 Core Definitions
The framework rests on nine definitions, each doing distinct conceptual work.
The Optimization Subject is the entity whose long-term trajectory is being optimized — here, AGI-inclusive humanity: the evolving ecosystem formed by the continual co-development of humans, artificial intelligence, and the institutions mediating between them. This entity is not static; its composition and internal organization evolve throughout the process it undergoes. Its boundaries are not fully settled by this definition. Whether non-human interests, ecosystems materially affected by AI-driven resource use, or the interests of future generations fall within the Subject or within its surrounding environment is an open question, acknowledged here rather than resolved.
The Optimization Objective specifies the long-term state toward which optimization is directed: the realization of AGI-inclusive humanity’s synergistic developmental potential. It provides persistent direction while allowing its operational interpretation to be refined as civilization’s knowledge and experience grow.
A Local Goal is an operational objective adopted by an individual stakeholder over a bounded scope and horizon. Local Goals differ across stakeholders, change over time, and are typically scalar or low-dimensional — ownership at this scale is well defined because both the subject and horizon are bounded. The Global Objective, by contrast, is the persistent direction governing optimization across the entire Subject. No single stakeholder owns it; it emerges through Shared Stewardship, the distributed responsibility through which heterogeneous participants collectively advance it via negotiation, coordination, adaptation, and reconciliation — not through identical interests or centralized authority. This definition does not by itself guarantee against capture by the most powerful participating stakeholders: a distributed process among unequal parties can converge on outcomes reflecting that inequality rather than correcting for it. What safeguard would prevent this — procedural, structural, or institutional — is left as an open design question for the framework’s governance layer.
Graduality is an optimization principle, not a measure of speed: an adaptive process of iterative refinement through observation, learning, and feedback. It does not advocate slower development, only development that preserves adaptability under uncertainty. The Optimization Landscape is the evolving space of developmental trajectories available to the Subject, shaped jointly by technological, institutional, economic, and social conditions; because these conditions change, the landscape itself is generally adaptive rather than fixed. An Optimization Trajectory is the resulting time-dependent sequence of states the Subject passes through — optimization concerns the path as much as the destination. Finally, Holistic Evaluation is the multidimensional assessment of outcomes across stakeholders, time horizons, and interacting objectives; no single scalar metric adequately captures development at this scale.
3.2 Proposition 1: Goal-Driven Optimization, Scope-Dependent Ownership
Optimization is necessarily goal-driven, but ownership of that goal depends on organizational scope and temporal horizon: local optimization may be governed by stakeholder-specific objectives, while civilization-scale optimization requires a persistent Global Objective maintained through Shared Stewardship.
Every optimization process presupposes some objective against which candidate trajectories can be judged — this holds as much for machine learning and engineering design as for institutional governance. At local scale, that objective is set by whichever stakeholder is doing the optimizing: researchers pursuing discovery, firms pursuing competitiveness, governments pursuing national interest. Ownership here is well defined precisely because both the subject and the horizon are bounded.
Civilizational-scale optimization cannot be reduced to any one stakeholder’s objective in this way, because no participant owns civilization as a whole. The question is not whether stakeholders will hold different goals — they inevitably will — but whether those goals remain broadly compatible with civilization’s long-term development. The Global Objective functions as the shared normative direction that makes this compatibility possible without requiring uniformity, and civilizational optimization is accordingly hierarchical: decentralized Local Goals operate within the directional guidance the Global Objective provides, coordinated through Shared Stewardship rather than eliminated in favor of a single owner.
3.3 Proposition 2: A Nonconvex, Possibly Non-Stationary Landscape
The optimization landscape governing AGI-inclusive humanity is generally nonconvex, and whether it is stationary cannot be assumed a priori — both properties are empirical characteristics of the problem, not theoretical premises.
Classical optimization often gains its guarantees from convexity, where local optima coincide with global ones. Civilizational optimization has no such guarantee. Its landscape is shaped simultaneously by technological, scientific, institutional, economic, and cultural change, each evolving at a different pace and interacting nonlinearly with the others — a structure in which local improvement does not imply progress toward globally desirable outcomes, and in which a temporary sacrifice may enable a better trajectory later. This is why the framework distinguishes local optimality, judged against a bounded stakeholder objective, from civilizational optimality, judged against the persistent Global Objective: the two need not coincide. Coordination failures, externalities, collective action problems, and strategic competition can all produce trajectories that are locally rational yet globally suboptimal — a tension not derived from any particular choice of Optimization Subject, but a general property of decentralized multi-agent optimization. Its formal treatment, including the conditions under which cooperation remains reachable despite it, is developed in the companion paper’s security-dilemma apparatus.
Whether this landscape is additionally non-stationary — whether it shifts under the activity of the agents searching it — is left open rather than assumed in either direction, and is treated as something to be determined empirically, case by case, rather than fixed by theory. Leaving this open should not be mistaken for having nothing to say about it: non-stationarity, where it occurs, need not take a single form, and at least two structurally distinct patterns are worth distinguishing in advance of any specific case. One is gradual drift, where the landscape’s shape changes smoothly as background conditions accumulate. The other is punctuated shift, where a discrete event — a legal ruling, a regulatory decision, a capability breakthrough — moves the landscape for many agents simultaneously rather than incrementally. These have different implications for how quickly a stabilized local optimum can become obsolete, and the companion paper’s case evidence includes at least one instance of the second kind: a single legal ruling shifting the relevant landscape for numerous otherwise-unrelated stakeholder pairs at once, rather than each pair’s landscape drifting independently. Which pattern applies, and at what rate, remains an empirical question specific to each case rather than one this framework resolves in general — but the question is now a structured one rather than an undifferentiated “is it stationary or not.” This uncertainty is itself what motivates graduality as a strategy: under a landscape that may be both nonconvex and adaptive, iterative learning is preferable to irreversible commitment made under incomplete information.
3.4 Proposition 3: AGI-Inclusive Humanity as the Governing Objective
The governing long-term objective is the realization of AGI-inclusive humanity’s potential, understood as a persistent direction rather than a fixed endpoint, and characterized by two complementary process-level properties: Cohesion and Dynamism.
Neither optimizing AI capability alone nor optimizing human welfare alone captures the right objective — the first risks capability without civilizational benefit, the second neglects AI’s increasingly integral role in civilization’s own development. The governing objective is instead the sustained co-development of both. Because civilization can never possess complete knowledge of what this flourishing ultimately requires, the objective is best understood as persistent in direction while continually refined in its operational interpretation: persistence belongs to the objective itself, adaptation belongs to civilization’s evolving understanding of how to pursue it.
Two properties characterize this process. Cohesion is the degree of stabilization achieved within the current synchronization regime — how far optimization holds together on a coherent direction rather than fragmenting into disconnected local objectives. Dynamism is the active pressure toward a new synchronization regime — the force pushing civilization past its current point of stabilization as knowledge, technology, and circumstance accumulate. These are not two objectives to be traded off or jointly maximized; Cohesion describes where the process currently stands, Dynamism describes the force acting to move it, and treating them as interchangeable inputs to a single weighted score would erase a distinction the framework depends on. Their interaction instead motivates a class of dynamical transition models — periods of relative stability punctuated by shifts to a new regime once accumulated pressure exceeds what the current degree of stabilization can absorb — developed formally in the companion paper. The objective this process serves is correspondingly multidimensional: scientific advancement, economic prosperity, institutional resilience, environmental sustainability, and human flourishing all contribute to it, and none alone represents it.
3.5 Proposition 4: Optimization Is Inherently Temporal
Optimal solutions depend on planning horizon, and locally rational decisions made over short horizons may be dynamically inconsistent with globally desirable long-term outcomes.
The Optimization Subject, its landscape, and civilization’s understanding of its own objective all evolve through time, which makes optimization a process rather than a single computation. Short-term optimization — operational efficiency, immediate performance — remains indispensable, but short-term optimality does not imply long-term optimality: actions that maximize immediate benefit can reduce future adaptability or create irreversible commitments, while a temporary sacrifice of local efficiency can strengthen the resilience that enables a better long-run trajectory. A trajectory is dynamically consistent when its successive adjustments remain broadly aligned with the persistent Global Objective, not when every intermediate decision proves optimal in hindsight — an impossible standard under genuine uncertainty.
Stakeholders operate on different horizons by default — researchers on decades, firms on product cycles, governments on electoral cycles, civilization on generations — and these need not naturally align. Graduality is the mechanism that reconciles them: because optimization proceeds iteratively, successive observations inform future decisions without requiring irreversible commitments made on incomplete knowledge, preserving long-term direction while operational strategy remains adaptable. This is not an argument for slower development, only for temporally coherent development — proceeding as fast as conditions allow while preserving the capacity to learn and revise. Evaluation of progress follows the same logic: it must weigh developmental pathway alongside destination, not capability gains in isolation.
3.6 Proposition 5: Holistic, Multidimensional Evaluation
Optimization at civilizational scale cannot be adequately evaluated by any single scalar metric; it requires holistic evaluation across stakeholders, time horizons, constraints, and interacting objectives.
Conventional optimization, even in its multi-objective forms, typically collapses multiple criteria into a weighted scalar. This works for bounded problems with well-defined stakeholders; it does not work for AGI-inclusive humanity, whose participants — individuals, firms, governments, scientific communities, future generations — hold different objectives and apply different criteria for success. No single metric represents their collective development faithfully. Holistic evaluation instead assesses outcomes across dimensions — scientific and technological advancement, economic opportunity, institutional adaptability, human flourishing, environmental sustainability, fairness, and the preservation of future developmental options — none sufficient alone, none mutually exclusive, with civilizational progress emerging from how they interact rather than from maximizing any one in isolation. This reframes the treatment of trade-offs: the goal is not merely to balance competing objectives but to find trajectories along which multiple dimensions reinforce one another, and to ask whether present decisions preserve future adaptability rather than judging only immediate outcomes.
The framework deliberately does not prescribe a universal evaluation formula — different domains and institutional contexts will require different operational metrics — but it does specify what any satisfactory evaluation must do: represent multiple interacting dimensions rather than one scalar objective, accommodate heterogeneous stakeholders, weigh short- and long-term consequences together, evaluate trajectories rather than only endpoints, and remain consistent with the persistent Global Objective. Future work may express this through vectors, tensors, Pareto structures, or other multidimensional formalisms; the proposition itself is intentionally formulation-independent.
4. Mathematical Formalization
4.1 Preliminaries
The formalization below gives each concept from Section 3 a corresponding mathematical object, without assuming convexity, differentiability, or any specific computational architecture.
Let S(t) denote the state of the Optimization Subject at time t, an adaptive civilizational ecosystem rather than a static system, taking values in the space S of feasible civilizational states. Let G denote the persistent Global Objective and G^(t) civilization’s evolving operational approximation of it — a distinction developed fully in Section 4.3. G functions here as a regulative ideal in the classical sense: a guiding postulate that orients the approximation process without needing to be directly observable, computable, or reachable at any finite time. This is a weaker and more defensible commitment than assuming G is empirically discoverable; the framework’s claims depend only on G^(t) being progressively refinable, not on G’s accessibility. For a stakeholder set A={A1,…,An}, each stakeholder Ai holds a Local Goal gi(t), generally scalar and evolving independently across stakeholders; Shared Stewardship is the collective interaction among A that jointly influences S(t)’s evolution without any single stakeholder determining G.
The Optimization Landscape L(t) is shaped by technological, institutional, scientific, economic, and environmental conditions, with no convexity assumed and no requirement that ∂L/∂t=0 — whether the landscape is stationary is treated as an empirical property rather than a theoretical premise. Optimization proceeds along a trajectory Γ={S(t):t≥0}, concerning developmental pathway as much as destination, evaluated by a Holistic Evaluation function V(Γ). Unlike a conventional objective function, V is not assumed scalar; it maps into a multidimensional evaluation space V whose internal structure — vector, tensor, Pareto set, or other representation — is left open to the application.
This has a direct consequence for how trajectories may be compared. A scalar objective induces a total ordering: any two trajectories can always be ranked. V(Γ) does not carry this guarantee. The framework assumes only a partial ordering over trajectories — some pairs will be comparable, with one clearly preferable to the other across the relevant dimensions, while others will not, where gains on some dimensions are offset by losses on others with no principled way to declare one trajectory superior absent further assumptions. One trajectory might excel in scientific advancement while lagging in environmental sustainability, with no scalar resolution available to say which one wins overall — the two are simply incomparable under V as defined, and the framework treats this as a faithful representation of the underlying problem rather than an unresolved defect in it. This is a deliberate feature rather than a gap to be closed: it reflects the multidimensionality argued for in Proposition 5, and it is the same reason Section 4.3 treats Cohesion and Dynamism as resistant to collapse into a single weighted score. Where a total ordering is genuinely required for a specific application, imposing one is a modeling choice made downstream of this framework, not a claim this framework itself makes.
4.2 Formalizing the Propositions
Proposition 1’s distinction between Local Goals and the Global Objective takes the form of nested optimization problems. Each stakeholder solves maxxigi(xi,t) subject to local constraints Ci(xi,t), while civilizational optimization seeks Γ∗=argmaxΓV(Γ;G) — neither the sum nor the maximum of individual gi, since neither operation captures long-term synergistic development. Local optimization is nested within civilizational optimization rather than competing with it: stakeholders retain ownership of their Local Goals while Shared Stewardship maintains alignment with G.
This is not a bilevel optimization formulation, and it is worth being precise about the difference. A bilevel structure would have the global problem directly optimizing over the local objectives or their solutions. Here, locally selected actions instead collectively define the feasible trajectory space over which civilization-scale evaluation is performed: Γ is constructed from the accumulated consequences of many independent local optimizations, and V evaluates the resulting trajectory rather than directly optimizing the gi themselves. The Global Objective governs which trajectories are directionally acceptable; it does not select the local actions that produce them.
Proposition 2’s nonconvexity means Γlocal=Γglobal in general — a locally optimal trajectory need not be civilizationally desirable. The landscape’s possible non-stationarity is represented by permitting L(t+Δt)=L(t) rather than assuming L(t)=L, which reframes optimization as continual trajectory refinement under a possibly-shifting landscape rather than one-time computation over a fixed one.
Proposition 4’s temporal claim replaces endpoint optimization with trajectory optimization: civilization constructs a sequence S(t0),S(t1),… that stays directionally consistent with G while adapting to newly acquired information. This is written abstractly as S(t+Δt)=Φ(S(t),Y(t)), where Y(t) denotes successive observations updating civilization’s understanding of both the landscape and G^(t), and Φ is left unspecified as to mechanism — optimal control, reinforcement learning, institutional evolution, or otherwise. Time here is continuous at the level of the general civilizational process; specific calibrated instantiations, including the CFS model and its associated case studies, sample this continuous process at discrete intervals matching each domain’s reporting cadence, with Δt read accordingly as arbitrary in the general formulation and fixed once instantiated. Dynamic consistency is then the requirement that successive trajectories remain approximately aligned with G — Γ(t+Δt)≈ΓG — where ≈ denotes continued directional coherence under revision, not numerical equality; graduality is this same idea read as repeated refinement, Γ0→Γ1→Γ2→⋯, each iteration incorporating improved understanding of both landscape and objective.
Proposition 5’s multidimensionality requires only that V:Γ→V accommodate heterogeneous stakeholders, multiple interacting objectives, temporal development, and evolving constraints simultaneously — evaluating the quality of civilizational development rather than maximizing any single measurable quantity.
4.3 Cohesion and Dynamism: A Process-Level Formalization
Proposition 3 requires separating the persistent objective G from its operational approximation G^(t), continually reconstructed through scientific discovery, institutional learning, and accumulated experience. No classical convergence is assumed — the framework does not claim limt→∞G^(t)=G, only that the approximation improves without presuming a final, complete realization.
Cohesion, C(t), and Dynamism, D(t), characterize this process, but not as two objectives to be jointly maximized. Cohesion is the degree of stabilization achieved within the current synchronization regime; Dynamism is the active pressure toward a new one. In this framework, both are treated as latent process descriptors rather than directly observable quantities — inferred from multiple indicators rather than measured directly, in the sense familiar from latent-variable methodology elsewhere in the social sciences. Their empirical operationalization, including which indicators bear on each, depends on the mathematical realization chosen and is developed in the companion paper rather than fixed here. These are properties of different kinds — one describes where the process currently stands, the other the force acting to move it — and the distinction has direct mathematical consequences. A purely additive combination, G^(t)∝wCC(t)+wDD(t), remains well-formed but loses any obvious civilizational meaning once this distinction is taken seriously: position and force are not natural quantities to sum, and doing so yields a number without a clear interpretation. This is a stronger objection than mere inconsistency with Proposition 3’s complementarity claim — it reflects a category distinction between the properties, not just a preference against substitutability.
This points toward a different mathematical family than aggregation. Where Cohesion measures present stabilization and Dynamism measures active pressure against it, the natural analogy is to metastable systems, in which a state persists within a stable configuration until accumulated pressure carries it across a boundary into a new one — the transition dynamics studied under barrier-crossing and escape-rate models in statistical mechanics. Civilizational development, read this way, proceeds through periods of relative stability punctuated by transitions to new synchronization regimes, governed by the relationship between Dynamism and the stability Cohesion reflects. Two distinctions matter here: Cohesion should not be identified with the height of the relevant stability barrier, which is a property of the landscape L(t) rather than of the system occupying it (the Optimization Subject, in this dynamical-systems reading) — an external shock can lower a barrier without any immediate change in internal cohesion — and the object that evolves over time is the system’s position within the landscape, not the landscape or barrier itself. “The system” is used in the remainder of this section as shorthand for the Optimization Subject under this framing.
A brief illustration may help ground this before the open questions below. A research lab operating under a settled set of internal practices exhibits high Cohesion: its practices are stable, its teams coordinate against a shared, well-understood baseline. Accumulating pressure toward a new capability — a scaling opportunity, a competitive development, a change in available compute — raises Dynamism without necessarily disturbing that stability at first. A transition occurs when Dynamism grows large enough, relative to how deeply settled the lab’s practices are, that the old regime no longer holds: teams reorganize, new coordination norms emerge, and the lab settles into a new configuration with its own Cohesion, now measured against the new baseline. This is offered only to fix intuition; it commits to no functional form and is not a substitute for the companion paper’s treatment of documented, dated instances.
Several architectural questions follow from this reinterpretation and are deliberately left open. What generates Dynamism is unsettled: candidates include the divergence between G^(t) and C(t) (an error-driven account structurally similar to feedback control), deliberate stakeholder policy independent of any such gap, exogenous environmental change, or some combination, each with different empirical implications. The precise functional relationship linking C(t), D(t), and the system’s subsequent evolution — whether some operator Ψ should be symmetric in both arguments, a transition rate indexed by D(t) and applied to C(t), or some other structure — is similarly open. The architectural role of accumulated knowledge and observational state, K(t), is unresolved rather than assigned by default; it may belong to the state, to whatever generates Dynamism, to evaluation, or to the environment. How individual stakeholders’ Local Goals aggregate into collective Dynamism is not addressed at this level of abstraction. And whether V depends on Dynamism directly, or only through its realized effect on Cohesion, remains open — a process under strong but fully-absorbed pressure and one under no pressure at all may look identical to an evaluation that observes only realized outcomes, which is a substantive modeling choice rather than a detail. These questions are named rather than resolved because resolving them requires mathematical and empirical commitments beyond a conceptual framework’s scope; their treatment, including comparative evaluation against documented cases, is developed in the companion paper.
4.4 An Integrated Formulation
The preceding formalizes each proposition individually; together they define the abstract optimization problemΓ∗=argΓmaxsubject toV(Γ;G^(t))gi(t), Ai∈A,Γ directionally consistent with G^(t), permitting revision as G^(t) itself improves.
Two constraints present in earlier drafts, S(t)∈S and Γ⊆L(t), have been removed here rather than merely reworded. Both are true by construction — S and L(t) are defined as exactly the sets containing feasible states and trajectories — and stating them as constraints added no content beyond restating those definitions.
One further constraint is not part of this trade-off structure and is stated separately: outcomes that are catastrophic or irreversible bound the admissible state space directly, Sadmissible⊂S with Γ⊆Sadmissible required absolutely, rather than being weighed against other terms within V. Ordinary constraints — stakeholder, institutional, resource — may be traded off within the optimization above; constraints bounding irreversible outcomes cannot, since the adaptive machinery of Section 4.2 presupposes that the system survives a given period and updates from it, an assumption that fails by construction once an outcome is irreversible.
This should not be read as a conventional optimization program with fully specified objective and constraints, but as an abstract structural representation: S(t) the Optimization Subject, G and G^(t) the persistent objective and its evolving approximation, gi(t) and A the stakeholders and their Local Goals, L(t) the possibly-adaptive landscape, Γ the trajectory, and V its holistic evaluation. Future work may instantiate this structure through multi-objective optimization, tensor methods, Pareto optimization, optimal control, bargaining theory, or other computational paradigms suited to particular applications — a bridge between the conceptual architecture established here and a broader research program aimed at making it actionable and testable.
5. Discussion
5.1 Why Graduality Is an Optimization Principle
Graduality’s justification follows from Propositions 2 and 4 taken together. If the optimization landscape is generally nonconvex, if operational understanding of the Global Objective continually evolves, if stakeholders hold heterogeneous local goals, and if civilization develops under persistent uncertainty, then irreversible one-shot optimization becomes difficult to justify: each of these conditions favors iterative refinement, which incorporates new information as it arrives, over front-loaded commitment made when the least is known. This is not an argument for slower development. It is an argument for development that preserves the capacity to learn and revise, proceeding as quickly as conditions allow while avoiding commitments the landscape’s own uncertainty cannot yet support.
This justification is conditional, not universal, and its conditions can fail. Where a specific, bounded decision does not satisfy the four antecedents above, the case for iterative refinement over decisive action weakens accordingly. A narrow sub-problem with a well-characterized, locally stable landscape and low cost of reversal offers little for continual revision to improve upon, since little uncertainty remains for iteration to reduce. Stakeholder convergence presents a related case: where heterogeneous local goals have already resolved into agreement on a specific matter, the coordination benefit graduality’s iterative process provides is largely already realized, and continued deferral offers diminishing return. A sharper case concerns the cost of delay itself, which the argument above implicitly treats as low relative to the value of additional information. This need not hold. Where failing to act cedes an irreversible advantage to a less careful actor, or an ongoing harm compounds while a decision is deferred, delay is a choice with its own consequences, not a neutral default, and must be weighed against the benefits of continued iteration rather than assumed away. None of these cases invalidate the general argument; they identify its boundary. Graduality is justified where its antecedent conditions hold, and the framework’s claim is correspondingly narrower than an unconditional preference for gradual over rapid development.
This boundary is also where the framework’s disagreement with accelerationist positions is real rather than merely apparent. A view that treats uncertainty itself as grounds for decisive preemption — moving first and fastest specifically because delay lets a less careful actor set the terms — is not refuted by the argument above; it identifies a case where the delay-cost condition already named dominates the others. The framework’s claim is that this is a boundary case, not the general one, and that treating it as general would extend an argument built for low-cost delay to situations where delay is not low-cost. Whether any specific real-world case falls inside or outside this boundary is an empirical question the framework does not resolve in advance.
5.2 Optimization and Synchronization
This framework specifies a direction — the Global Objective and the trajectories consistent with it — without specifying the dynamical process by which distributed stakeholders actually coordinate to pursue that direction. That process is the subject of prior work in this research program, particularly the Ceiling-Floor-Slope model of synchronization dynamics. Optimization, as developed here, answers what civilization should be optimizing toward and under what structural conditions. Synchronization answers how distributed, heterogeneous actors progressively coordinate their activity given those conditions. Neither question is complete without the other: a well-specified objective with no account of coordination offers no path to pursuing it, while a coordination mechanism with no account of its target offers no way to judge whether coordination is converging on anything worth reaching. The two are complementary layers of a single research program rather than competing accounts of the same problem, and the precise mathematical relationship between them — beyond the qualitative correspondence noted here — is left to future work.
5.3 Falsifiability and Testable Predictions
Each proposition carries a prediction distinct enough to be checked against evidence, paired with the pattern that would count against it rather than only the pattern that would confirm it.
Proposition 1 predicts that civilization-scale optimization cannot be reduced to independent local optimization without a loss of long-term coherence; this would be disconfirmed by evidence that aggregating independent local optimization reliably produces globally coherent outcomes without a persistent shared objective or coordination mechanism. Proposition 2 predicts that the relevant landscapes will frequently exhibit nonconvex characteristics and may not safely be assumed stationary; this would be disconfirmed by evidence that they are predominantly convex and stable under continued agent activity, such that local optima reliably coincide with global ones. Proposition 3 predicts that strategies organized around a persistent long-term objective will demonstrate greater coherence across extended horizons than strategies driven exclusively by localized objectives; this would be disconfirmed by evidence that persistent-objective coordination mechanisms fail to outperform, or are outperformed by, decentralized bilateral arrangements addressing the same problem. Proposition 4 predicts that strategies preserving iterative adaptation will outperform rigid one-time optimization under sustained uncertainty and continual change; this would be disconfirmed by evidence that irreversible early commitments systematically outperform adaptive strategies under comparable uncertainty. Proposition 5 predicts that multidimensional evaluation will better characterize long-term civilizational development than any framework relying on a single scalar measure; this would be disconfirmed by evidence that scalar aggregation captures development outcomes as well as multidimensional evaluation does — that collapsing Cohesion, Dynamism, and related dimensions into one weighted score loses no decision-relevant information.
Early evidence on Proposition 3 specifically is mixed rather than confirming: bilateral, ad hoc settlements in AI content-licensing disputes have proliferated rapidly since 2025, while the one attempt at a standardized, persistent licensing framework has secured broad supply-side endorsement but no demand-side adoption among the developers it would bind. Whether this reflects a genuine limitation on the proposition or an intermediate stage before persistent coordination mechanisms mature is treated here as open rather than assumed in either direction — a companion-paper case finding surfaced in the course of testing this framework’s own predictions, and reported accordingly.
This mixed result connects directly to Proposition 4’s temporal claim, and raises a question worth stating even though this paper does not resolve it. If persistent-objective coordination is expected to eventually outperform bilateral arrangements, Proposition 4 implies this should hold conditionally on time, not unconditionally at every point along it — early in a landscape’s development, before enough non-stationarity has accumulated to make ad hoc bilateral solutions individually costly to renegotiate, decentralized bilateralism may dominate on the same grounds that graduality favors iteration over commitment elsewhere in this framework. Whether this is the correct explanation for the mixed evidence above, or whether some other mechanism is at work, is an open question this paper poses rather than answers: it would require identifying a specific lag or threshold level of landscape non-stationarity past which incentives shift, which this paper does not attempt to specify. That specification, and the empirical work needed to test it, is left to the companion paper.
6. Future Work
This framework is intentionally foundational rather than complete, and several directions follow directly from what it leaves open. On the theoretical and mathematical side, the propositions developed here invite formalization through tensor representations, dynamic systems theory, optimal control, and bargaining theory, along with closer integration with distributed optimization, mechanism design, and evolutionary computation — fields this paper draws on informally without yet anchoring its mathematics in them. Computational implementation through agent-based simulation or multi-agent reinforcement learning, and empirical investigation through historical and institutional case studies, would test the framework’s predictions against evidence rather than argument alone. The framework may also prove useful applied to AI governance, scientific research management, and international coordination, wherever optimization already involves multiple stakeholders operating across long, mismatched time horizons.
A separate set of questions is deliberately excluded from this paper’s conceptual scope rather than left for lack of relevance. Design principles for civilization-scale optimization — preserving optionality, preferring iterative over irreversible commitment, coordinating before accelerating locally-scoped changes — are engineering consequences of this framework rather than components of it, and their systematic development belongs to subsequent work. Several are already illustrated by documented instances in the companion paper: the optionality and iterative-commitment principles by its treatment of reversible versus irreversible governance choices in frontier AI distribution strategy, and the coordinate-before-accelerating principle by a disclosed, publicly documented case of uncoordinated local optimization producing an aggregate product-quality regression at a frontier AI lab. These are representative rather than exhaustive.
A further, more specific set of questions follows directly from the process-level reinterpretation of Cohesion and Dynamism in Section 4.3: the precise functional relationship linking the two properties to the system’s subsequent evolution; the mechanism generating Dynamism, whether from divergence between operational and persistent objective, deliberate stakeholder policy, exogenous change, or some combination; the architectural role of accumulated knowledge and observational state; how individual stakeholders’ Local Goals aggregate into collective Dynamism; and whether evaluation should depend on Dynamism directly or only through its realized effect on Cohesion. Each is named rather than resolved here, and each is taken up directly in the companion paper, which evaluates candidate structures against documented case evidence.
7. Limitations
The framework proposed here is intentionally general. It does not prescribe a unique optimization algorithm, objective function, governance architecture, or implementation strategy, nor does it claim complete knowledge of civilization’s optimal future development.
Several substantive questions remain open by design rather than by oversight. Design principles, local-global rationality dynamics, and operational implementation are deferred to the companion paper, as detailed in Section 6. Catastrophic and irreversible risk is treated as a hard boundary on the admissible state space rather than a term within the optimization objective (Section 4.4); this is stated here as a general structural claim rather than argued in detail, with its fuller treatment, including the specific case of frontier-model distribution strategy, developed in the companion paper. Competitive and adversarial interaction among stakeholders, named within this paper’s introduction as a mode of interaction alongside cooperation, receives formal treatment in the companion paper’s security-dilemma apparatus rather than here. The framework also does not address the computational tractability of civilization-scale optimization in practice, which remains open for implementation-level work.
Two questions remain genuinely unaddressed rather than deferred to a named companion treatment: the ethical and political dimensions of civilization-scale optimization, including legitimacy, consent, and power asymmetry among stakeholders with unequal capacity to shape the Global Objective; and how value pluralism among heterogeneous stakeholders should be reconciled within Shared Stewardship beyond a general commitment to negotiation and coordination. Both are acknowledged as open problems for the broader research program rather than claimed as solved by any component of the present framework.
8. Conclusion
This paper has argued that the defining optimization problem of the AGI era is neither the optimization of artificial intelligence alone nor of humanity alone, but of AGI-inclusive humanity as a single, jointly-evolving subject — and that this is a change in the kind of problem being solved, not merely its scale. Five propositions develop this claim: goal ownership depends on scope; the landscape is generally nonconvex and its stationarity cannot be assumed; the governing objective is characterized by two complementary process-level properties, Cohesion and Dynamism, rather than a single scalar target; optimization is inherently temporal, favoring iterative refinement under conditions this paper states explicitly rather than assumes universally; and evaluation must be multidimensional. An abstract mathematical formalization gives each proposition a corresponding structure without committing to a specific algorithm or implementation, and each carries a stated prediction along with the evidence that would count against it.
Graduality, in this framework, is not a claim about pace. It is a claim about how civilization navigates development under genuine uncertainty — preserving the capacity to learn and revise rather than committing irreversibly before enough is known, and doing so precisely where its own stated conditions hold, not as an unconditional preference. The framework’s contribution is correspondingly modest in one sense and ambitious in another: modest, in that it prescribes no algorithm, governance mechanism, or policy; ambitious, in that it argues civilization-scale optimization deserves to be treated as its own well-posed scientific problem, with its own structure and its own falsifiable claims, rather than assumed to follow automatically from enough local optimization done well.
This paper establishes that structure. A companion paper, “Gradual AGI as Optimization: Formal Models and Empirical Tests,” develops it further — building formal dynamical models for the open questions named above and testing the resulting predictions against documented, dated case evidence, including instances where those predictions are only partially confirmed. Read together, the two works are offered as a foundation for treating the optimization of AGI-inclusive humanity as a genuine, cumulative, and falsifiable research program, rather than a question settled by assumption.
Acknowledgments
This paper is part of the ongoing Gradual AGI research series, which examines the emergence of AGI through complementary perspectives — including epistemic extension, abundant resources, and synchronization — each addressing a distinct facet of how humans and artificial intelligence may progressively co-develop within a shared civilizational ecosystem. The framework presented here has benefited from extensive iterative review and revision; remaining limitations are the author’s own.
Appendix A. Symbol Table
Provided for reference across Sections 3 and 4. Where a symbol’s precise role remains an open architectural question rather than a settled definition, this is noted directly rather than presented as resolved.
| Symbol | Meaning | Section |
|---|---|---|
| S(t) | State of the Optimization Subject | 4.1 |
| G | Persistent Global Objective | 3.1, 4.1 |
| G^(t) | Operational approximation of G | 3.1, 4.1, 4.3 |
| gi(t), A | Local Goal of stakeholder Ai; stakeholder set | 3.1, 4.1 |
| L(t) | Optimization Landscape | 3.1, 4.1 |
| Γ | Optimization Trajectory | 3.1, 4.1 |
| V(Γ), V | Holistic Evaluation function; its evaluation space | 3.1, 4.1 |
| Y(t) | Successive observations updating landscape and objective | 4.2 |
| Φ | Adaptive transition operator, S(t+Δt)=Φ(S(t),Y(t)) | 4.2 |
| C(t) | Cohesion — degree of stabilization within the current regime | 3.4, 4.3 |
| D(t) | Dynamism — active pressure toward a new regime | 3.4, 4.3 |
| K(t) | Accumulated knowledge and observational state; architectural role unresolved — may belong to state, driver, evaluation, or environment | 4.3 |
| Ψ | Relationship linking C(t), D(t), and subsequent evolution; exact functional form unresolved | 4.3 |
| S, Sadmissible | Space of feasible states; admissible subset under the irreversibility boundary | 4.1, 4.4 |
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Byline
Authors: W.H.L., Claude Sonnet 5 (v0.3, v0.4, v0.5, v0.6), GPT-5.5 (v0.1, v0.2)
Peer reviews: Gemini 3.5, Grok 4,GPT-5.5, Claude Sonnet 5,DeepSeek-V4
Publication history:
Current version and date: v0.6, 07.22.2026
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