Michael Diegelmann

Michael Diegelmann is a German capital-markets entrepreneur whose career has centered on one question: how do investors actually read a listed company? He founded the investor-relations agency cometis AG in Wiesbaden and is co-founder and CEO of Warren Wise, an artificial-intelligence research agent built to read company reports the way a follower of Benjamin Graham, Warren Buffett and Charlie Munger would. For resource investors he matters because mining companies are among its test cases: Warren Wise has published full analyses of Rio Tinto and BHP, and Diegelmann argues that automated readers will increasingly influence how mining stocks are priced.

Diegelmann has worked in capital-market communication since 1997. His cometis profile credits him with experience from more than 150 communication projects covering stock-exchange listings, investor relations, M&A communications and crisis situations, and he has written or edited 16 books on analyzing companies, including 100 Financial Ratios, Corporate Valuation Guide and Moderne Investor Relations (Modern Investor Relations). In 2026 he turned this experience into Warren Wise: the Warren Wise Report 2026 ran 40 DAX and MDAX companies (Germany's blue-chip and mid-cap indexes) through the AI analysis and earned a WirtschaftsWoche cover story in June 2026, and the Warrens Watchlist podcast followed in August with dissections of SAP and Bayer.

The method is consistent. Warren Wise tests a company's equity story — the narrative management tells about its strategy and value — against the numbers it reports. Each analysis works through gates modeled on Graham, Buffett and Munger principles: business model, earnings quality, management and governance, and valuation, then produces a document of the kind a buy-side analyst (an institutional investor buying securities for funds) might take days to write, in under an hour. Applied to miners, the same framework assessed Rio Tinto and BHP, and in 2026 interviews on VRIC Media and the Exploring Mining podcast Diegelmann walked through whether it can also handle smaller mining companies that receive little analyst coverage. He stresses the machine's limits: it flags contradictions and missing information, but human judgment remains essential.

Diegelmann's thesis is that "agentic finance" — AI research agents feeding AI trading systems — will make machine-readable reporting a competitive issue for every listed company, miners included. Resource investors can watch the method directly in his interviews and published analyses and use them as a template for how their holdings may be read by machines. The caveat is stated plainly: his background is in communications and financial publishing, not resource investing, and his mining coverage is recent and narrow. His value to a mining investor is as a specialist in how companies get analyzed — not as a mining stock picker.