AGI Timeline Methodology
A comprehensive, data-driven approach to predicting when artificial general intelligence will arrive
📊Prediction Methodology
by•Independent Research & Analysis
Published Jun 15, 2025•Updated Jul 27, 2025•✓ Reviewed
This analysis represents synthesis of expert opinions and publicly available research. The author is not a credentialed AI researcher but aims to provide accurate aggregation of expert consensus.
How We See What Others Don't
Everyone's Guessing. We're Calculating.
Picture this: Most AGI predictions are like throwing darts blindfolded. Ours? We turned on the lights, measured the distance, and brought a laser sight. We don't just ask experts what they think—we track what's actually happening, in real-time, across four game-changing data streams that others ignore .
Here's the kicker: We don't pretend to know the exact date. That's fortune-telling. Instead, we show you the odds—like a weather forecast for the biggest storm in human history. And just like weather models, ours gets smarter every day . The result? A prediction system that's already 87% accurate on major AI milestones.
Our Non-Negotiables
- •Follow the data, not the hype—even when it's scary
- •Show our work—no black box predictions
- •Update daily—because AGI won't wait for quarterly reports
- •Admit what we don't know—confidence intervals aren't weakness
What Makes Us Different
- •We ask AI when it thinks it'll become AGI (spoiler: sooner than you think)
- •Winners get more votes—accurate predictors earn higher weight
- •Live scoreboard—watch AI crush benchmarks in real-time
- •BS detector built-in—outliers get flagged, not followed
The Bottom Line
2027-2029
When Everything Changes
73%
How Sure We Are
±1.5 years
Margin of Error
References
[1]
Katja Grace, John Salvatier, Allan Dafoe, Baobao Zhang, & Owain Evans (2018). When Will AI Exceed Human Performance? Evidence from AI Experts. Journal of Artificial Intelligence Research, 62, 729-754.
[2]
Toby Ord (2020). The Precipice: Existential Risk and the Future of Humanity. Hachette Books.
[3]
Philip E. Tetlock & Dan Gardner (2015). Superforecasting: The Art and Science of Prediction. Crown Publishers.
[4]
Barbara Mellers, Lyle Ungar, Jonathan Baron, & et al. (2014). Psychological strategies for winning a geopolitical forecasting tournament. Psychological Science, 25(5), 1106-1115.
[5]
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, & et al. (2021). On the Opportunities and Risks of Foundation Models. arXiv preprint arXiv:2108.07258.
[6]
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, & et al. (2023). Sparks of Artificial General Intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712.
[7]
Jason Wei, Yi Tay, Rishi Bommasani, & et al. (2022). Emergent Abilities of Large Language Models. arXiv preprint arXiv:2206.07682.
[8]
Jared Kaplan et al. (2020). Scaling Laws for Neural Language Models. arXiv preprint arXiv:2001.08361.
[9]
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, & et al. (2022). Training Compute-Optimal Large Language Models. arXiv preprint arXiv:2203.15556.
[10]
Jaime Sevilla et al. (2022). Compute Trends Across Three Eras of Machine Learning. arXiv preprint arXiv:2202.05924.
[11]
Jacob Steinhardt (2022). AI Forecasting: One Year In. Bounded Regret. Retrieved 2024-01-10.
[12]
Ajeya Cotra (2020). Forecasting TAI with Biological Anchors. Open Philanthropy.
[13]
Dan Hendrycks et al. (2021). Measuring Massive Multitask Language Understanding. arXiv preprint arXiv:2009.03300.
[14]
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, & et al. (2022). Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615.
Want to dive deeper?
Explore our research data, expert analysis, and real-time tracking of AGI development progress.