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Sprint projectFeb 2, 2026San Francisco

Red Lines Forecasting: When Will Frontier AI Cross Compute Thresholds?

Kwesi Amanfu · Team BPG

Submitted to The Technical AI Governance Challenge. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Red Lines Forecasting: When Will Frontier AI Cross Compute Thresholds?

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We forecast when frontier AI training runs will cross compute thresholds relevant to AI governance. Frontier models are those trained with the largest computational budgets each year; we measure training compute in FLOPs (floating-point operations, the total computational work used to train a model). Using Epoch AI data, we find that both policy-defined thresholds (EU AI Act 10²⁵ FLOP, US EO 10²⁶ FLOP) have already been crossed. Compute milestones at 10×, 100×, and approximately 3000× the US EO threshold, which are expected within one to three years. We validate forecasts with rolling origin cross-validation and calibration correction. We distinguish policy thresholds from compute milestones. The latter are training budget forecasts, not capability claims.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. Impact potential & innovation: 2.5

    Plausibly useful infrastructure for policy makers to think about timelines. But the backward-looking finding will not be surprising, and it would have been more helpful to have policy recommendations or some more novel addition to the discourse

    Execution quality: 3.5

    Validation framework is fairly rigorous, higher than the average hackathon submission. Would have been helpful to explore the saturation question, potentially with a quick sensitivity analysis

    Clarity: 3.5

    The structure is logical and writing accessible - I imagine this would be legible to a regular journalist or political staffer. Some small amounts of sloppiness, e.g. 'data sources' in section 3.1 is empty, but that's forgiveable given the hackathon setting. I am a bit confused by the methodology section claiming the selection was piecewise and figure 4 showing linear. I doubt this matters much but it adds a bit of confusion

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  2. The elephant in the room is saturation modeling. The paper acknowledges the possibility of diminishing returns but doesn't attempt any S-curve or logistic alternatives, which would substantially strengthen the analysis given active discussion of data exhaustion, hardware limits, and algorithmic diminishing returns. The analysis also ignores algorithmic efficiency entirely — raw FLOP trends don't capture that each FLOP buys more capability over time. With only 14 frontier data points, the piecewise model selection over the more parsimonious linear model deserves more explicit defense. Solid

Cite this project

@misc{amanfu2026red,
  title = {{Red Lines Forecasting: When Will Frontier AI Cross Compute Thresholds?}},
  author = {Kwesi Amanfu},
  year = {2026},
  month = feb,
  note = {Submitted to The Technical AI Governance Challenge, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/red-lines-forecasting-when-will-frontier-ai-cross-compute-thresholds-6mit}},
  url = {https://apartresearch.com/sprints/projects/red-lines-forecasting-when-will-frontier-ai-cross-compute-thresholds-6mit}
}

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