Ai Economic Index

AI development has been super crazy these day. Its use cases span everything from creating and analyzing documents, images, and videos, autonomous research assistants that can draft papers and design simulations (https://www.anthropic.com/research/vibe-physics|Vibe physics: The AI grad student \ Anthropic), discovering vital security vulnerabilities (https://www.lesswrong.com/posts/7aJwgbMEiKq5egQbd/ai-found-12-of-12-openssl-zero-days-while-curl-cancelled-its|AI found 12 of 12 OpenSSL zero-days (while curl cancelled its bug bounty) — LessWrong), and even orchestrating entire workflows across industries. AI is widely believed to disrupt job security, as automation and intelligent systems increasingly replace tasks once performed by humans. But is that really true? This simple question is actually what led me to build data pipelines and do my own analysis about AI role in today's economy. I use the Anthropic Economic Index raw dataset (https://huggingface.co/datasets/Anthropic/EconomicIndex|Anthropic/EconomicIndex · Datasets at Hugging Face) as the main source for my data pipeline, since Claude by Anthropic is widely regarded as one of the most effective models in today’s professional market. I also incorporate some external data such as country population, country GDP, and O*NET dataset (comprehensive occupation dataset) to enrich the output dataset. You can view the results of my work in the visual report available at https://gofhilman.github.io/ai-economic-index/|AI Economic Index The source code is also publicly available at |gofhilman/ai-economic-index: End-to-end data pipelines and dashboards delivering insights on AI’s role in today’s economy The whole data pipeline workflow for this work, which uses Kestra, Terraform, GCS and BigQuery by Google, dbt, Evidence, and Bruin, is illustrated in the image. But what I want to highlight here is how I use Bruin as an AI data analyst. To enable this, I first set up a Bruin pipeline that passes through key intermediate and marts dbt models, such as task-to-SOC mappings, enriched datasets, and reporting tables. Every Bruin asset and its table columns were also fully documented so that it will be easier for Bruin to parse the data and generate queries. Once the pipeline was in place, I connected Bruin as an app in Discord, so the Bruin dataset could be consumed directly for AI-driven data analysis by users. To discover more insights from my data, for example, you can just simply ask Bruin, "Based on the data, what kind of tasks can AI help me as a physicist?" Then, Bruin will relate your question to the data it has, query the data assets from the pipelines, and return answers based on actual query results. This approach helps reduce, or even avoid, what's often called AI hallucinations, which happen when an AI generates information that sounds convincing but isn't accurate. By linking questions directly to real data, the system stays focused, delivers more reliable answers, and highlights insights that truly matter. You can join the AI Economic Index Discord server here: https://discord.gg/N4RBw6nkwE Finally, check out the full analysis report on my personal blog: https://stacked-stories.pages.dev/ai-economic-index-reports-13|AI Economic Index: Reports — Stacked Stories