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Sprint projectNov 2, 2025Dubai-UAE

Can AI predict its own future

Jessica Shalet Sanctis · Team-MetaMind

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

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Report: Can AI predict its own future

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Can AI predict its own future? We asked today’s most advanced AI models to forecast when and how human-level AI will emerge — and compared their answers side by side. Each model responded with predictions for the year of human-level AI, a confidence range, early and late indicators, and a short narrative about the years that follow. We built a dashboard that visualizes these forecasts, timelines, and narratives, turning raw model outputs into a compelling story about AI’s trajectory.

This open-source project makes AI forecasting transparent, reproducible, and thought-provoking — serving as both a dataset and a storytelling tool for researchers and policymakers.

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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{sanctis2025ai,
  title = {{Can AI predict its own future}},
  author = {Jessica Shalet Sanctis},
  year = {2025},
  month = nov,
  note = {Submitted to The AI Forecasting Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/can-ai-predict-its-own-future-0hdy}},
  url = {https://apartresearch.com/sprints/projects/can-ai-predict-its-own-future-0hdy}
}

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