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Andrej Karpathy Maps AI Exposure of 342 Jobs in Viral Weekend Project
Andrej Karpathy Maps AI Exposure of 342 Jobs in Viral Weekend Project
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Andrej Karpathy Maps AI Exposure of 342 Jobs in Viral Weekend Project

Andrej Karpathy analysed 143 million jobs across the US economy and assigned each occupation an AI exposure score.

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Key takeaways

  • Analyze 143 million US jobs to assess AI exposure across 342 occupations.
  • Assign AI exposure scores from 0 to 10, with a weighted average of 4.9.
  • Highlight that 42% of jobs scored 7 or higher, indicating significant AI impact.
  • Identify higher-paying roles as more exposed to AI compared to lower-paying jobs.
  • Clarify that the project was misinterpreted, leading to its quick removal.
5 min

OpenAI co-founder Andrej Karpathy, on March 15, unveiled an interactive visualisation rating the AI exposure of 342 major US occupations listed by the Bureau of Labour Statistics, before taking it down hours later amid widespread sensationalism.

However, the interactive site remains accessible, and archived versions continue to circulate online.

Karpathy described the tool as a quick “vibe-coded” experiment completed in about two hours on a Saturday morning. 

“I thought the code/data might be helpful to others to explore the BLS dataset visually, or colour it in different ways or with different prompts or add their own visualisations. It’s been wildly misinterpreted (which I should have anticipated even despite the readme docs), so I took it down,” he wrote in a post on X.

The weekend project analysed 143 million jobs across the US economy and assigned each occupation an AI exposure score from 0 to 10, where 10 signalled the strongest potential impact from AI tools.

The weighted average exposure across all jobs stood at approximately 4.9. About 42% of positions—or roughly 59.9 million jobs paying an estimated $3.7 trillion annually—scored 7 or higher, suggesting a strong potential for AI-related changes in tasks.

Higher-paying roles faced the greatest exposure. Occupations earning more than $100,000 per year averaged 6.7 on the scale, while those earning less than $35,000 averaged 3.4. 

Digital-heavy white-collar jobs such as software developers, data analysts, accountants, lawyers, financial specialists, writers, and medical transcriptionists scored in the 8–10 range. In contrast, hands-on manual roles such as roofers, janitors, and home health aides ranked near the bottom at one or below.

Despite a clear disclaimer on the website and GitHub repository stating that the scores were rough estimates rather than predictions of job loss and did not account for factors such as demand elasticity, regulatory constraints, or social preferences, the map quickly gained popularity online.

“The ‘exposure’ was scored by an LLM based on how digital the job is. This has no bearing on what actually happens to these occupations, which has to do with demand elasticity and a lot more,” Karpathy said in a post on X.

Tesla and SpaceX CEO Elon Musk also weighed in on the discussion, saying, “All jobs will be optional. There will be universal high income,” framing AI advances as a path to abundance rather than crisis.



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