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Sprint projectNov 3, 2025Madison

AI’s Impact on Video and Game Generation

Mahesh Ramesh · Team Hollywood

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

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AI’s Impact on Video and Game Generation - short survey + forecasting

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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?

  1. * It's very unclear to me how the calibration anchors were set. They seem like the most important part of the entire project, based on what I've read so far, but no details as to how they were arrived at are provided.

    * While this could be valuable for high-level calibration of likelihood for someone unfamiliar with the space, not enough rigor has been put into validating key decisions in this approach: only one simple method is used to investigate the adoption curve, and no details are provided on how the decision variables are defined.

  2. The project arbitrarily sets "calibration anchors" that assume some probability of AGI in certain years, then tries to interpolate between them. This is contrary to the spirit of forecasting, where you don't know what these anchors are in the first place and are trying to justify those anchors. However, this project does not come up with any justification for these anchors.

Cite this project

@misc{ramesh2025ais,
  title = {{AI’s Impact on Video and Game Generation}},
  author = {Mahesh Ramesh},
  year = {2025},
  month = nov,
  note = {Submitted to The AI Forecasting Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/ais-impact-on-video-and-game-generation-xzdj}},
  url = {https://apartresearch.com/sprints/projects/ais-impact-on-video-and-game-generation-xzdj}
}

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