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Predicting Real-World Achievement from Open-Source Intelligence

Explores whether online public outputs (code, writing, competition rankings) can serve as better predictors of intelligence and talent than traditional IQ tests.

status: Rough

Status Indicator

The status indicator reflects the maturity of the content: • rough — Initial thoughts, unstructured • growing — Being actively developed • evergreen — Stable, well-developed content • withered — No longer being maintained This helps readers understand the completeness of the content.

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certainty: data-driven

Confidence Rating

The confidence tag expresses how well-supported the content is, or how likely its overall ideas are right. This uses a scale from "impossible" to "certain", based on the Kesselman List of Estimative Words: 1. "certain" 2. "highly likely" 3. "likely" 4. "possible" 5. "unlikely" 6. "highly unlikely" 7. "remote" 8. "impossible" Even ideas that seem unlikely may be worth exploring if their potential impact is significant enough.

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importance: 9/10

Importance Rating

The importance rating distinguishes between trivial topics and those which might change your life. Using a scale from 0-10, content is ranked based on its potential impact on: - the reader - the intended audience - the world at large For example, topics about fundamental research or transformative technologies would rank 9-10, while personal reflections or minor experiments might rank 0-1.

iq, achievement, prediction
Filed under: psychology

Idea

This paper explores whether public online outputs (code quality, writing style, productivity, originality) can act as better predictors of intelligence or real-world ability than legacy measures like GPA or IQ scores. You’ll analyze open-source work, blog posts, portfolios, or contest results and correlate them with proxies of success or domain expertise. The paper challenges the conventional gatekeepers of intelligence assessment and raises questions about how we identify potential in the 21st century.

Citation
Yotam, Kris · Jul 2025

Yotam, Kris. (Jul 2025). Predicting Real-World Achievement from Open-Source Intelligence. krisyotam.com. https://krisyotam.com/papers/psychology/achievement-open-source-iq

@article{yotam2025achievement-open-source-iq,
  title   = "Predicting Real-World Achievement from Open-Source Intelligence",
  author  = "Yotam, Kris",
  journal = "krisyotam.com",
  year    = "2025",
  month   = "Jul",
  url     = "https://krisyotam.com/papers/psychology/achievement-open-source-iq"
}