Apr 28, 2025

The Early Economic Impacts of Transformative AI: A Focus on Temporal Coherence

Félix Dorn, Xavier Ferrés, Elsie Jang, Valmik Nahata

🏆 1st place by peer review

Details

Details

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Summary

We investigate the economic potential of Transformative AI, focusing on "temporal coherence"—the ability to maintain goal-directed behavior over time—as a critical, yet underexplored, factor in task automation. We argue that temporal coherence represents a significant bottleneck distinct from computational complexity. Using a Large Language Model to estimate the 'effective time' (a proxy for temporal coherence) needed for humans to complete remote O*NET tasks, the study reveals a non-linear link between AI coherence and automation potential. A key finding is that an 8-hour coherence capability could potentially automate around 80-84\% of the analyzed remote tasks.

Cite this work:

@misc {

title={

The Early Economic Impacts of Transformative AI: A Focus on Temporal Coherence

},

author={

Félix Dorn, Xavier Ferrés, Elsie Jang, Valmik Nahata

},

date={

4/28/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Reviewer's Comments

Reviewer's Comments

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Yulu Pi

The idea of temporal coherence as a distinct axis of task difficulty is timely and well-framed. The authors do an excellent job grounding their work in current task-based economic models and recent literature on transformative AI. This creates a strong conceptual bridge to the new bottleneck they highlight. However, temporal coherence is a nuanced and novel concept, and it should be introduced more clearly and earlier in the abstract or introduction.

To strengthen the foundation further, the paper would benefit from engagement with cognitive science or psychology literature that explores the types of cognitive capacities required to perform long-horizon tasks. This could help clarify how temporal coherence maps onto real-world human capabilities and limitations.

The reliance on a single model and a relatively small validation set (45 tasks) raises some concerns about robustness. More detail is needed to establish why the manually annotated golden values are reliable enough to validate the model’s outputs. Perhaps the most critical shortcoming is that the system prompt asks the model to estimate only the “effective time” required for a human to complete the task, using that time as a proxy for coherence. This implicitly reduces temporal coherence to task duration, which appears to oversimplify the concept.

One final suggestion is to include some modeling of the cost of labor versus the cost of automation. A task being technically automatable does not necessarily make it economically viable to automate. Factoring in cost dynamics would make the projections more grounded.

I do like the idea behind the paper, it for sure worths more future work.

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.