Winners for AI Forecasting Hacathon:

The trajectory of AI development represents one of the most consequential questions for humanity's future. Understanding when and how transformative AI capabilities will emerge is critical for policy, safety research, and societal preparedness. Yet current forecasting methods struggle with unprecedented technological shifts, compounding uncertainties, and the challenge of predicting emergent capabilities.

That's why we're launching the AI Forecasting Hacthakon.

In this hackathon, you can:

• Build forecasting models and evaluation pipelines to anticipate AI capabilities and timelines

• Create tools for scenario exploration, uncertainty quantification, and model benchmarking

• Develop monitoring systems for key indicators of AI progress across research, industry, and policy

• Write policy briefs and governance proposals grounded in forecasting insights

• Explore new methodologies inspired by projects like AI 2027 and EpochAI's empirical forecasting work

• Pursue other projects that advance the field of AI forecasting!

You will work in teams over one weekend and submit open-source forecasting models, benchmark suites, scenario analyses, policy briefs, or empirical studies that advance our understanding of AI development timelines and trajectories.

💰 $2,000 in prizes will be awarded to the top projects.

Winning projects will be published openly, shared with safety researchers, and invited to continue development within the Apart Fellowship.

Tracks

1. AI Capability Forecasting & Timeline Models

This track emphasizes transparent, reproducible methods to predict AI development milestones and anticipate transformative AI capabilities. Projects may:

• Design benchmarks and empirical models to forecast transformative AI timelines and capability emergence

• Build evaluation pipelines that interpret scaling laws and compute trends (e.g., inspired by EpochAI's Direct Approach)

• Develop quantitative models predicting automation milestones for coding, research, and economic tasks

• Compare forecasting approaches (expert surveys, trend extrapolation, biological anchors) and their policy implications

• Create tools for estimating timelines to AGI using multiple methodological frameworks

2. Scenario Planning & Uncertainty Analysis

This track focuses on modeling possible AI futures, their uncertainties, and acceleration dynamics. Projects may:

• Create interactive frameworks for multi-year scenarios, referencing approaches like AI 2027's pathway modeling

• Build tools for uncertainty quantification, confidence calibration, and sensitivity analysis

• Develop "what-if" simulators exploring policy interventions and their effects on AI trajectories

• Design tabletop exercises and wargaming tools for strategic planning around critical AI decision points

• Analyze branching points and their implications for policymakers using forecasting-driven insights

3. AI Progress Monitoring & Early Warning Systems

This track focuses on real-time tracking and rapid response to AI advancement indicators. Projects may:

• Build dashboards monitoring key metrics across compute investment, model performance, and economic impact

• Create detection systems for capability jumps, breakthrough papers, or acceleration signals

• Develop APIs aggregating progress indicators from research labs, industry, and academia

• Prototype early-warning systems enabling rapid regulatory or institutional responses

• Design tools tracking algorithmic efficiency gains and their implications for timeline estimates

4. Governance, Policy & Meta-Forecasting

This track addresses integrating forecasts into governance while ensuring forecasting rigor. Projects may:

• Map sources of error and uncertainty in AI timelines (parameter choices, trend shifts, domain boundaries)

• Compare strengths/limitations of different forecasting methodologies through systematic critique

• Create educational resources ensuring rigorous, transparent forecasting practices across the field

What you will do

Participants will:

• Form teams or join existing groups.

• Develop projects over an intensive hackathon weekend.

• Submit open-source forecasting models, scenario analyses, monitoring tools, or empirical research advancing our understanding of AI trajectories

What happens next

Winning and promising projects will be:

• Awarded with $2,000 worth of prizes in cash.

• Published openly for the community.

• Invited to continue development within the Apart Fellowship.

• Shared with relevant safety researchers.

Why join?

• Impact: Your work may directly inform AI governance decisions and help society prepare for transformative AI

• Mentorship: Expert forecasters, AI researchers, and policy practitioners will guide projects throughout the hackathon

• Community: Collaborate with peers from across the globe working to understand AI's trajectory and implications

• Visibility: Top projects will be featured on Apart Research's platforms and connected to follow-up opportunities