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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Every AI product you use runs on semiconductors. And for the last several years, the narrative has been almost entirely about Nvidia.

But Q1 2025 results are painting a more nuanced picture and for the first time, the question of whether Nvidia's dominance is structural or temporary feels like a live debate rather than a hypothetical.

In this episode, Debs and Graham go inside the semiconductor industry from first principles, mapping out who does what across the AI chip ecosystem before turning to the latest results and what they mean for valuations.

Graham explains how GPUs, CPUs and memory chips work together to power AI, covering why the parallel computational demands of AI models require so much chip capacity, why that has driven up the price of consumer memory, and why Nvidia's software ecosystem creates a lock-in that competitors are only now beginning to challenge seriously.

Debs then walks through the competitive landscape in detail: Broadcom winning custom chip mandates from Google and Meta on energy efficiency grounds, AMD posting 57% data centre revenue growth, TSMC delivering 41% revenue growth with 66% margins, Samsung flagging memory supply constraints into 2027, and Intel up 150% year to date on the back of a foundry pivot and reported talks with Apple.

The valuation discussion unpacks why chip designers like AMD trade at a premium to manufacturers like TSMC despite TSMC's superior margins, the role of CapEx intensity and cash conversion in driving that gap, and the Taiwan geopolitical risk discount embedded in TSMC's 18x multiple.

The episode closes with Debs and Graham weighing whether semiconductor valuations reflect genuine AI demand or a market that has run ahead of itself, and flags Nvidia's own results on 20 May as the next major test.

Key Discussion Points:

Semiconductor ecosystem: GPUs, CPUs, memory and custom chips, who makes what and how they work together.

Nvidia's competitive position: software lock-in, hardware leadership and the first real signs of competitive pressure.

Q1 results: AMD, Broadcom, TSMC, Samsung and Intel, what the numbers say about demand, market share and supply constraints.

Valuation framework: why growth and cash conversion drive the premium for chip designers over foundries, and what geopolitical risk does to TSMC's multiple.

Nvidia's S&P 500 weighting: how index inclusion and passive fund flows affect valuation independent of fundamentals.

Outlook: memory supply constraints into 2027, the Intel/Apple story and Nvidia's results on 20 May as the next major market catalyst.

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OpenAI has missed a revenue target in the run-up to what is expected to be one of the largest IPOs in history. Sam Altman and the company's CFO have been publicly at odds.

And behind all of this sits close to $700 billion of committed CapEx across the major hyperscalers, much of it financed through project finance structures that were built on the assumption of hyper-aggressive AI revenue growth.

In this episode, Debs and Graham use the OpenAI revenue miss as a lens to examine how AI infrastructure financing actually works, who is exposed when things wobble, and how a shortfall at the end of the chain could propagate upward.

Debs walks through the mechanics of project finance as it has been adapted for data centre construction. SPVs are set up to construct and operate individual facilities, with construction contracts and take or pay revenue agreements signed in advance to create predictable cash flows.

That predictability is what allows the SPV to finance itself at up to 90% debt, significantly more leveraged than a typical LBO, and on 15 year lease terms.

The financing is bankruptcy remote, meaning SPV investors have no direct recourse to the hyperscalers themselves.

That structure works cleanly until one of the counterparties at the end of the chain stops performing.

Oracle, which handles two thirds of OpenAI's compute commitments and carries the weakest credit rating among the major hyperscalers, is identified as the most exposed party.

A sustained revenue miss from OpenAI puts Oracle under pressure on its own SPV contract obligations, raising the prospect of a credit downgrade from just above investment grade to junk, with potential covenant implications that would compound the problem further.

The episode closes with the broader question of whether the AI infrastructure build-out is entering its first genuine stress test, and what the next 12 months of investor reporting might finally reveal about the numbers behind the narrative.

Key Discussion Points:

> OpenAI pre-IPO: what the revenue miss and exec conflict signal about the state of the business.

> Hyperscaler CapEx commitments: the scale of spending committed for 2026 and how it is being financed across public and private markets.

> Project finance mechanics: SPV structure, construction contracts, take or pay agreements, and the debt waterfall.

> Leverage and risk: why data centre project finance operates at 90% leverage and why that is only sustainable with locked-in cash flows.

> Oracle's position: credit rating, exposure to OpenAI and the domino risk within the financing chain.

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DISCLAIMER:

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Past performance is not indicative of future results. Please conduct your own due diligence or consult with a certified professional before making any financial decisions.

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