An interesting perspective on how data is a unique type of good, and how its properties in a TAI economy differs from capital!

The primary strength lies in the observations made about how data is unique compared to capital, particularly the section on why traditional economic models fail. The statements are easy to understand, backed with good examples, and form a strong framework that is used throughout the report.

I also find the policy and governance implications interesting, although some more detail here would have been great – I would have recommended expanding this section at the expense of the metaphors described in section 3 (the biology analogy is sufficient, having the quantum example is not equally value-adding). The text would also have benefitted from being more consistent in what terms are used. For example, in the case studies, both "value superposition" and "multi-domain value" are used, and seem to mean similar things. Also, I am not sure if it is due to a tech issue, but I can't find the references being used in the text. This would have further strengthened the credibility of the analysis.

The relevance of the work is nevertheless high. Data will indeed be a cornerstone of a TAI economy, and it is essential to have a good economic model of it to develop effective governance, as the article states.

The biomorphic framing and incorporation of biological metaphors are creative. However, the argument would be stronger with a closer engagement with existing literature. There’s already a substantial body of work that explores data’s unique role in the modern economy—especially around platform capitalism and data as labor—that’s not fully acknowledged here. Several references are listed in the bibliography but are not clearly integrated into the main text, limiting their impact.

Discussions on the direct impact of AI risk reduction should be strengthened. Governance recommendations are offered, but they remain at a high level. A more detailed explanation of how the proposed policies would reduce specific risks, particularly eoconomic risks such as monopoly and data misuse on market dynamics, would enhance the paper.

This is really creative piece of work. Still, the framework remains mostly theoretical, and that limits how far the argument can go. The case studies in finance and healthcare are interesting, but they feel a bit too surface-level to really support the claims being made. Adding more detail, evidence, or even some light modeling would go a long way in making the framework feel more robust and ready for broader application.

The submission offers an ambitious reimagining of data in Transformative AI economies, presenting a biomorphic framework that treats data as capital with quasi-biological traits. By drawing on biology, quantum physics, and complex systems theory, the paper highlights non-rivalry, combinatorial value creation, and disequilibrium dynamics that standard capital models overlook. The section on nuanced forms of data scarcity and the policy matrix that balances excludability with innovation are original contributions that help policymakers think beyond current data governance debates .

Despite this conceptual novelty, the argument relies mainly on metaphors and narrative case studies. There are no formal definitions, mathematical expressions, or simulation results that operationalize the proposed framework. As a result, it is difficult to test falsifiable claims or compare the approach with existing models of data value and access. The literature review lists relevant OECD and academic sources, but engagement is largely descriptive and omits recent quantitative work on data as an intangible asset, platform competition, and measurement of data externalities.

Links to AI safety are indirect. The paper notes that data governance shapes incentives for advanced AI systems, yet it stops short of analyzing how data capital affects alignment risks, model interpretability, or malicious use. Concrete pathways connecting the biomorphic perspective to safety mechanisms such as auditing, dataset provenance, or red-team incentives would improve the impact on the safety agenda.

Technical quality and documentation remain limited. The case studies in finance and healthcare are illustrative rather than empirical, and the text does not specify selection criteria, data sources, or analytical methods. No code, datasets, or reproducibility materials accompany the submission, which restricts external validation and future extensions by other researchers.

To strengthen the work, the author should:

1. Formalize key concepts with clear definitions and, if possible, simple models or agent-based simulations.

2. Provide at least one quantitative example that traces data accumulation, depreciation, and value creation under the biomorphic rules.

3. Expand the review to cover empirical studies on data markets and recent AI safety papers linking data governance to catastrophic risk reduction.

4. Release a minimal open-source notebook that reproduces any illustrative result or visual.

5. Clarify how the proposed governance mechanisms would mitigate specific safety risks, for example by preventing data poisoning or ensuring verifiable data lineage.

An interesting discussion of the limits of standard economic assumptions when modelling data as a productive asset. Many of the individual points raised felt reasonable and correct. In addition, introducing case studies is a good way to make this feel more tangible. However, there didn’t seem to be a concrete alternative framework proposed, beyond gesturing towards taking insights from other fields. I definitely think there could be interesting research that comes from this direction, but this paper doesn’t suggest a concrete alternative approach. One way you could push this research forward instead is to adapt existing economic models using insights from this paper to guide changes to the standard assumptions (e.g. increasing returns to scale and non-rivalrous).

Some specific comments below.

Increasing returns is a good point, and a good reason for considering systems out of equilibrium. We could reframe this as long run/short run, where we look at SR equilibrium, transitions over time, then what happens in the LR either if all data is used, or synthetic data introduced.

I wouldn’t say ‘economic theory assumes…’, as economic theory refers to the tools. I would say ‘most of the literature considers models where…’. Then you’re arguing that we should use economic theory to consider different scenarios, i.e. non-rivalrous, increasing RTS, etc.

Often we might think about data as information, i.e. once you know it, you can’t get any further use from it. But might be interesting to think about data as a source of information, where the more you study it, the more information you can draw from it. (Then you could include a function which takes data as an input and produces value, then considering different functions allows you to explore the points you consider in ‘Variable and Context-Dependent Depreciation’ and elsewhere.)

‘Complex Adaptive System Dynamics’ – this section has interesting claims, I would be keen to see more discussion in this section.

It’s not true that economic theory doesn’t allow for inputs to be complements (capital and labour typically are). You could separate ‘data’ into different categories that are complements (so just a modelling choice). (re. Combinatorial value creation)

The section on scarcity seems right.