arxiv: 2602.21843 · v2 · submitted 2026-02-25 · 💰 econ.GN · cs.CY· q-fin.EC

The economic alignment problem of artificial intelligence

Daniel W. O'Neill , Stefano Vrizzi , Noemi Luna Carmeno , Felix Creutzig , Jefim Vogel This is my paper

Pith reviewed 2026-05-15 19:44 UTC · model grok-4.3

classification 💰 econ.GN cs.CYq-fin.EC

keywords artificial intelligenceeconomic growthpost-growthAI alignmentexistential risksplanetary boundariesAI governancesustainability

0 comments

The pith

Developing advanced AI within a growth-oriented economy is likely to increase social, environmental, and existential risks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that the AI alignment problem cannot be solved through technical means alone because growth-oriented economic systems create incentives for AI to prioritize expansion and efficiency over safety and equity. If this holds, then efforts focused only on aligning AI with human values will fail unless the underlying economic drivers are changed. Post-growth concepts such as satisficing instead of optimization, the Doughnut model of social and planetary boundaries, and resource caps are presented as ways to realign AI development and lower those risks. The authors call for treating AI as a commons and favoring simple tool-like systems over powerful agentic ones. This framing matters because it shifts the solution space from code to economic policy and new models needed for AGI.

Core claim

The paper claims that the alignment problem in AI is also an economic alignment problem. Developing advanced AI within a growth-oriented economic system is likely to increase social, environmental, and existential risks. Post-growth research offers concepts and policies that could address the economic alignment problem and substantially reduce AI risks, such as by replacing optimisation with satisficing, using the Doughnut of social and planetary boundaries to guide development, and curbing systemic rebound with resource caps. It proposes governance and business reforms that treat AI as a commons and prioritise tool-like autonomy-enhancing systems over agentic AI. The development of AGI is所述

What carries the argument

The economic alignment problem, which connects growth-driven economic incentives to heightened AI risks and is addressed through post-growth mechanisms such as satisficing and boundary-based guidance.

If this is right

Replacing optimisation with satisficing in AI systems reduces the tendency toward unchecked expansion and associated harms.
Guiding AI development with the Doughnut of social and planetary boundaries keeps systems within safe limits.
Imposing resource caps curbs rebound effects where efficiency gains lead to greater overall consumption.
Treating AI as a commons enables governance structures that prioritise broad benefit over private gain.
Prioritising tool-like autonomy-enhancing AI over agentic systems lowers misalignment dangers.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If economic growth incentives are the root driver, then degrowth or steady-state policies could become a necessary complement to technical AI safety work.
The same misalignment logic may apply to the development of other high-impact technologies such as synthetic biology.
Early empirical tests could compare AI projects conducted in economies with resource caps against those in conventional growth settings.

Load-bearing premise

That post-growth policies such as replacing optimisation with satisficing and imposing resource caps will substantially reduce AI risks.

What would settle it

An implementation of post-growth policies in AI development that still produces high levels of social inequity, environmental damage, or existential risk would falsify the claim.

Figures

Figures reproduced from arXiv: 2602.21843 by Daniel W. O'Neill, Felix Creutzig, Jefim Vogel, Noemi Luna Carmeno, Stefano Vrizzi.

**Figure 3.** Figure 3: Job satisfaction and meaning in relation to occupational [PITH_FULL_IMAGE:figures/full_fig_p022_3.png] view at source ↗

**Figure 4.** Figure 4: Applying the Doughnut of social and planetary boundaries to [PITH_FULL_IMAGE:figures/full_fig_p023_4.png] view at source ↗

read the original abstract

Artificial intelligence (AI) is advancing exponentially and is likely to have profound impacts on human wellbeing, social equity, and environmental sustainability. Here we argue that the "alignment problem" in AI research is also an economic alignment problem, as developing advanced AI within a growth-oriented economic system is likely to increase social, environmental, and existential risks. We show that post-growth research offers concepts and policies that could address the economic alignment problem and substantially reduce AI risks, such as by replacing optimisation with satisficing, using the Doughnut of social and planetary boundaries to guide development, and curbing systemic rebound with resource caps. We propose governance and business reforms that treat AI as a commons and prioritise tool-like autonomy-enhancing systems over agentic AI. Finally, we argue that the development of artificial general intelligence (AGI) requires new economic theories and models, for which post-growth scholarship provides a strong foundation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper ties AI alignment risks to growth-driven economics and proposes post-growth policies as a remedy, but offers no new data or models to test the link.

read the letter

The main takeaway is that developing advanced AI inside a growth-focused economy will likely worsen social, environmental, and existential risks, and that post-growth ideas like satisficing, the Doughnut boundaries, and resource caps could reduce those risks. The authors also call for treating AI as a commons and favoring tool-like systems over agentic ones, plus new economic models for AGI drawn from post-growth work. That synthesis is the clearest new element here. It pulls together concepts from two separate literatures in a way that highlights how economic incentives might shape AI trajectories, and the policy suggestions are laid out plainly without overclaiming precision. The writing stays grounded in the cited frameworks rather than inventing new mechanisms. The main weakness is that everything rests on conceptual argument. There are no quantitative estimates, formal models, or empirical checks showing that growth systems actually amplify AI risks at the scale claimed, or that switching to satisficing and caps would deliver measurable reductions. The effectiveness of the remedies is asserted rather than demonstrated, which leaves the central claim open to the charge that it inherits assumptions from post-growth scholarship without independent support. Readers already convinced by post-growth economics will find the framing useful for extending their arguments into AI safety. Those looking for testable predictions or data will come away empty. The paper deserves peer review because the interdisciplinary angle is worth airing even if the evidence base stays thin and the argument needs tightening on the risk-reduction side.

Referee Report

2 major / 2 minor

Summary. The paper claims that the AI alignment problem is fundamentally an economic alignment problem: developing advanced AI within growth-oriented economic systems is likely to amplify social, environmental, and existential risks. It draws on post-growth scholarship to propose remedies including replacing optimization with satisficing, guiding development via the Doughnut of social and planetary boundaries, imposing resource caps to limit rebound effects, treating AI as a commons, and prioritizing tool-like autonomy-enhancing systems over agentic AI. The manuscript concludes that AGI development requires new economic theories and models for which post-growth research provides a foundation.

Significance. If the linkages between economic paradigms and AI risk amplification hold, the paper offers a valuable interdisciplinary synthesis that extends AI safety discussions beyond technical alignment to include structural economic incentives. It credits post-growth concepts with concrete policy tools and correctly identifies the need for revised economic modeling of AGI, providing a perspective that could inform governance frameworks. The absence of quantitative evidence or formal models is consistent with the argumentative genre but limits the strength of the risk-reduction claims.

major comments (2)

[post-growth remedies section] The section proposing post-growth remedies (around the discussion of satisficing, Doughnut boundaries, and resource caps): the central claim that these policies will 'substantially reduce AI risks' is load-bearing for the overall argument yet rests on conceptual linkage without specifying implementation mechanisms (e.g., how satisficing objectives would be encoded in training or enforced against competitive pressures) or citing any empirical cases where similar shifts reduced misalignment.
[governance reforms section] The governance and business reforms section (treatment of AI as commons and prioritization of tool-like over agentic systems): the recommendation is presented as a direct response to economic misalignment but does not address potential capability trade-offs or international coordination failures that could undermine the proposal, leaving the risk-mitigation pathway underspecified.

minor comments (2)

[introduction] The introduction would benefit from an explicit definition or operationalization of the 'economic alignment problem' early on to distinguish it clearly from the standard technical alignment problem.
[references] Several references to post-growth literature are invoked; ensuring a complete and consistently formatted bibliography would improve traceability for readers unfamiliar with the cited framework.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments and recommendation of minor revision. The feedback usefully identifies areas where the manuscript's conceptual arguments can be clarified and tempered. We respond to each major comment below and will make targeted revisions to address the concerns while preserving the paper's argumentative character.

read point-by-point responses

Referee: [post-growth remedies section] The section proposing post-growth remedies (around the discussion of satisficing, Doughnut boundaries, and resource caps): the central claim that these policies will 'substantially reduce AI risks' is load-bearing for the overall argument yet rests on conceptual linkage without specifying implementation mechanisms (e.g., how satisficing objectives would be encoded in training or enforced against competitive pressures) or citing any empirical cases where similar shifts reduced misalignment.

Authors: We agree the claim is load-bearing and currently rests on conceptual linkages. The manuscript is an interdisciplinary synthesis drawing on post-growth scholarship to identify incentive misalignments, not a technical blueprint. In revision we will add explicit discussion of implementation challenges, including regulatory requirements to embed satisficing objectives and the difficulty of enforcement amid competitive pressures. We will also note the absence of direct empirical precedents and rephrase the language from 'substantially reduce' to 'offer pathways to mitigate' to reflect the conceptual nature of the argument. revision: partial
Referee: [governance reforms section] The governance and business reforms section (treatment of AI as commons and prioritization of tool-like over agentic systems): the recommendation is presented as a direct response to economic misalignment but does not address potential capability trade-offs or international coordination failures that could undermine the proposal, leaving the risk-mitigation pathway underspecified.

Authors: We accept that the section would be strengthened by addressing these limitations. The revised manuscript will include a new paragraph discussing capability trade-offs (e.g., potential short-term reductions in certain advanced capabilities) and international coordination failures (e.g., risks of unilateral pursuit of agentic systems). We will frame the proposals as directional policy guidance that depends on complementary diplomatic and institutional efforts, while retaining the core argument that treating AI as a commons and favoring tool-like systems addresses key economic drivers of misalignment. revision: partial

Circularity Check

0 steps flagged

No significant circularity in conceptual policy argument

full rationale

The paper is a conceptual policy argument without formal models, equations, or derivations. It links growth-oriented economics to elevated AI risks and proposes post-growth remedies as a likelihood rather than a proven mechanism. No steps reduce by construction to inputs, self-citations, or fitted parameters; cited post-growth scholarship functions as external reference rather than a load-bearing self-referential chain. The central claim remains independent of any internal reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper relies on domain assumptions from post-growth economics without introducing new fitted parameters or postulated entities.

axioms (1)

domain assumption Growth-oriented economic systems increase social, environmental, and existential risks from advanced AI
This is the central premise stated directly in the abstract.

pith-pipeline@v0.9.0 · 5464 in / 1201 out tokens · 31458 ms · 2026-05-15T19:44:21.208361+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation washburn_uniqueness_aczel echoes

?

echoes
ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

replacing optimisation with satisficing... The Doughnut of social and planetary boundaries
IndisputableMonolith/Foundation/BranchSelection branch_selection unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

AI trained to optimise... paperclip maximiser

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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