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Sprint projectNov 2, 2025Canada

Simulating Automation Timelines Through Labor-Capability

Ifeoma Ilechukwu , Arya Hariharan, Marie-Louise Thurton , Oluwagbemike Olowe, Rijal Saepuloh · Team A-team

Submitted to The AI Forecasting Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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This paper presents the technical architecture for the Simulating Automation Timelines Through Labor-Capability Modeling system, a probabilistic forecasting pipeline designed to assess the future impact of Artificial Intelligence (AI) on labor markets in India and Nigeria. Addressing the critical uncertainty regarding the pace and focus of AI-driven job automation, the system operates by fusing three core data streams: detailed occupational skill requirements using O*NET, quantitative time-series AI benchmark scores (projecting future capability growth), and real-time policy signals sourced via GDELT for events in India and Nigeria.

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Does the project meaningfully advance AI timeline prediction and capability forecasting? Does it clearly connect to measurable indicators of AI progress (compute, benchmarks, economic impacts, automation milestones)? Does it build on or challenge existing forecasting frameworks like biological anchors, scaling laws, or scenario planning? Does it offer novel methodologies, data sources, or empirical insights that could improve forecast accuracy? Is it grounded in observable trends rather than pure speculation?

Does this project inform critical decisions about AI development and preparedness? Does it help identify key uncertainties, decision points, or early warning indicators? How well does the project connect technical metrics to real-world impacts and policy needs? Could the output guide resource allocation, safety research priorities, or regulatory timelines? Does it reduce uncertainty around transformative AI milestones or capability emergence?

Is the project methodologically rigorous, reproducible, and technically sound? Is the forecasting approach well-calibrated with appropriate uncertainty quantification? Are the data sources, assumptions, and limitations clearly documented? Does the project demonstrate sound statistical methodology and honest treatment of model uncertainties? Would the tool, model, or framework be useful for ongoing forecasting efforts, research planning, or policy analysis?

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Cite this project

@misc{ilechukwu2025simulating,
  title = {{Simulating Automation Timelines Through Labor-Capability}},
  author = {Ifeoma Ilechukwu and Arya Hariharan and Marie-Louise Thurton and Oluwagbemike Olowe and Rijal Saepuloh},
  year = {2025},
  month = nov,
  note = {Submitted to The AI Forecasting Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/simulating-automation-timelines-through-laborcapability-quvk}},
  url = {https://apartresearch.com/sprints/projects/simulating-automation-timelines-through-laborcapability-quvk}
}

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