Pablo Zavala · AI Safety Evaluation · Research Engineering

AI Investment Mapping, Block Center, CMU

A Block Center project mapping more than ten billion dollars in public and private AI investment across eleven metropolitan economies for regional AI-readiness research. The Pittsburgh slice covers 6.3 billion dollars across 133 firms.

Claim precision

What the evidence demonstrates
$10B+ mapped across 11 metros; Pittsburgh: $6.3B across 133 firms
Capability and evidence frontier
Company-level records and maps stay private; the public page shows aggregates and methods evidence.

Restricted data, public aggregate summary

Role: Research and data pipeline contributor at CMU's Block Center.

How to Inspect This Work

Public claim

The portfolio shows aggregate investment scale and the Pittsburgh slice while keeping source-restricted firm-level tables private.

Pipeline evidence

The public RAG evaluation shows retrieval-evaluation discipline; the restricted investment dataset remains outside that verification boundary.

Boundary

A reader should treat this as a public summary of restricted research materials; the underlying dataset remains unreleased.

Case Study

Problem

Regional AI policy often talks about national totals, while investment actually lands through specific metros, firms, and institutions.

Setup

The project builds a structured map of public and private AI investment across eleven metropolitan economies, including a deeper Pittsburgh slice.

Method

I contributed to data engineering and RAG-supported evidence review, with company and regional records kept in source-restricted materials.

Result

The research maps more than ten billion dollars in AI investment, including 6.3 billion dollars across 133 Pittsburgh firms.

Verification scope

Company-level tables and maps stay private because the source data is restricted. The public page uses aggregate evidence only.

Evidence

The public RAG evaluation harness documents retrieval-evaluation discipline; the restricted investment totals remain outside that verification boundary.

Key Outcomes

  • More than ten billion dollars in AI investment mapped across eleven metropolitan areas
  • Pittsburgh mapped at 6.3 billion dollars across 133 firms
  • Public RAG harness demonstrates retrieval-evaluation discipline; restricted investment totals remain outside that verification boundary

Methods

  • Retrieval-augmented generation
  • LLM pipelines
  • Data engineering