How good is AI at building investment banking models?

In this episode, Debs and Graham put Claude for Excel to the test by prompting it to construct a full merger model from scratch, using GameStop's $56 billion bid for eBay as the live case study, but with the focus squarely on the AI workflow rather than the deal itself.

Graham walks through the merger model framework from first principles before opening Claude for Excel and giving it a single instruction: build me a merger model for the proposed acquisition.

What follows is a live demonstration of what AI can and cannot do in a real M&A modelling workflow.

The verdict is nuanced. Claude sources factual data quickly, structures the model sensibly, makes a credible first pass at sources and uses, and saves the kind of analyst time that used to go into manual press release scrubbing and 10-K data extraction.

But it also makes errors that anyone trained in proper modelling would catch immediately, hardcoded assumptions buried in cell formulas, fiscal year mismatches between acquirer and target, missing synergy inputs that were publicly disclosed, and modelling practices that would never pass a senior banker's review.

The takeaway: Claude for Excel is a powerful first-pass tool that can compress hours of analyst work into minutes, but it is dangerous in the hands of anyone who cannot audit the output.

The fundamentals of modelling, accounting and finance still matter - arguably more than ever, because the cost of accepting AI output without scrutiny is now embedded in every workflow.

Key Discussion Points:

Merger model framework: accretion, dilution, sources and uses, pro forma adjustments, LTM calendarisation.

Prompting strategy: what a minimal prompt produces versus what structured prompting would deliver.

Where AI saves time: factual data sourcing, model structure, first-pass build.

Where AI fails: modelling best practices, hardcoded inputs, technical errors, judgement calls.

Stress-testing in real time: how to use AI to iterate on synergy, consideration mix and financing assumptions.

AI in finance careers: why the fundamentals matter more than ever in an AI-enabled workflow.

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