Research how companies use AI internally with Codex and Google Sheets
I started with a list of companies on GitHub and asked Codex to find out how they actually used AI inside the business. For CXO.dev, the useful examples were changes to engineering, design, and day-to-day operations. Selling an AI product did not tell me whether a company had changed how its own team worked.
Codex searched company articles and public posts, then wrote the findings into Google Sheets with source links, dates, the practice being described, and named practitioners where those were public. That gave me a research ledger I could inspect and question without opening the same pages and copying the same fields over and over.
I pushed on the evidence standard as we went. A firsthand post could earn a place on a watchlist, but an example I would hold up needed concrete mechanics, credible evidence of use, and ideally corroboration. Keeping those distinctions in the sheet made it possible to collect broadly without treating every discovery as an endorsement.
# Research internal AI practices
Adapted from this workflow; not the original transcript.
Starting with the company list I provide, research how each company uses AI internally. Focus on concrete engineering, design, and operating practices. Distinguish internal adoption from marketing for an AI product.
Create a reviewable table with company, practice, source URL, source date when available, public practitioner names where relevant, supporting evidence, and open questions. Prefer firsthand company or practitioner accounts. Do not invent metrics or publication dates.
Keep promising leads separate from well-supported examples. Explain the evidence needed to promote a lead. Deduplicate repeated sources and leave gaps visible. Save the research privately for review; do not contact anyone or publish it.