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Sprint projectNov 2, 2025Canada

Simulating Automation Timelines Through Labor-Capability Modeling

Arya Hariharan , Marie-Louise Thurton · 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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The non-linear advancement of AI creates significant uncertainty. Our core objective is to simulate the interaction between accelerating AI capability growth and human labor skill requirements. The critical questions raised are: which economic tasks will be automated first and how soon will this fundamentally reshape the entire industries?

We used granular occupational data (O*NET) and quantitative AI performance benchmarks (MMLU-Pro, SWE- Bench) to generate a dynamic forecast with uncertainty intervals. A probabilistic forecasting pipeline assessing the future impact of AI on the labor markets 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{hariharan2025simulating,
  title = {{Simulating Automation Timelines Through Labor-Capability Modeling}},
  author = {Arya Hariharan and Marie-Louise Thurton},
  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-modeling-a5dh}},
  url = {https://apartresearch.com/sprints/projects/simulating-automation-timelines-through-laborcapability-modeling-a5dh}
}

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