EX-BANKERS EXPLAIN: Investment Banking Mistakes To AVOID In Your First Year

EX-BANKERS EXPLAIN: Investment Banking Mistakes To AVOID In Your First Year

It's summer training season. Both Debs and Graham are spending their days running analyst and associate programmes at major firms, which makes this the right moment to step back from the deal-of-the-week format and share the kind of candid advice they wish someone had given them on day one. Graham opens with his own first-year story at Lehman Brothers in 2005, including the pitch book error that earned him an hour-long dressing-down from a VP, and uses it as the entry point to a broader conversation about attention to detail and why the technical work in finance is genuinely not the hardest part of the job.  Debs shares her own near-career-ending moment publishing a flawed research screen as a new associate, and reflects on how she recovered, what her boss told her, and why trust, once lost, takes years to rebuild. From there the conversation moves through the practical advice that gets harder to find as classes get larger and firms get bigger.  How to tell the difference between a recoverable mistake and a career-ending one. Why finance math is simple and getting stuff done well is the actual skill.  How to ask questions that add value versus questions that just take up airtime. Why prep before meetings is the easiest way to stand out, why sharp elbows are usually the wrong instinct, and why being a team player matters more than being the smartest person in the room. The episode closes with their personal survival tips: physical activity, availability, sleep, calendar discipline, and showing up to everything you can.  The kind of advice that sounds basic but separates the analysts who get the return offer from the ones who don't. Key Discussion Points: Attention to detail: why the costs of getting it wrong are high, and how AI changes (but doesn't reduce) the standard.  Recoverable vs. career-ending mistakes: how to tell the difference and what to do in each case.  The technical work is the easy part: why getting stuff done well is the real skill.  Asking questions that add value: how to demonstrate engagement without taking up airtime.  Standing out without sharp elbows: why being a team player and showing up consistently is the most underrated path.  Survival habits: physical, mental, calendar, sleep, and the practical mechanics of not burning out. WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal [https://webmail.wallstreetprep.com/whats-the-big-deal] Follow Us On Socials: LinkedIn: https://www.linkedin.com/company/wall-street-prep/ Instagram: https://www.instagram.com/wallstreetprep/ Resources: https://linktr.ee/wallstreetprep

The $1.75 Trillion SpaceX IPO: Everything You Need to Know.

SpaceX begins trading on Friday at a $1.75 trillion valuation, and the deal looks unlike any major IPO that has come before it.  In this episode, Debs and Graham go inside the prospectus, break down the unusual structural features Elon Musk has pushed through, and debate whether the valuation can be justified. The mechanics alone are remarkable. The IPO is being priced at a fixed $135 per share rather than through a traditional book-build range, putting all of the price risk onto buyers and signalling unusual confidence from the issuer. The free float is less than 5%, which sets up potentially significant post-listing volatility.  Retail investors have been given 30% of the allocation, roughly three times the typical share, raising the question of whether this is genuine democratisation or simply exit liquidity for early holders.  The dual-class share structure leaves Musk with 85% of the voting power despite owning around 45% of the economics.  And the underwriting fee, agreed across a syndicate of 23 banks, has come in at 0.75%, the lowest on record for a deal of this size. The valuation discussion centres on the TAM chart in the prospectus. SpaceX has positioned itself less as a launch and communications business and more as an AI infrastructure and applications story, with $26.5 trillion of AI revenue underpinning the case for the headline number, including $22.7 trillion in enterprise applications alone.  Debs and Graham draw the parallel to the Tesla IPO, where the company was reframed from auto to tech in order to unlock a tech multiple. They also reference Aswath Damodaran's published view that the realistic AI TAM is closer to $5 trillion, and Morningstar's estimate that the fair value of the business is roughly half the IPO valuation. The episode closes on what to watch when trading begins. With oversubscription pointing to a potential pop, but a low free float, a 180-day staggered lock-up creating an overhang, and the Nasdaq 100 fast entry expected to trigger $30 to $50 billion of forced buying, the first six months are likely to be unusually volatile. Both hosts agree the outcome is genuinely unpredictable. Key Discussion Points: The fixed-price IPO mechanism, why it's unprecedented at this scale, and what it signals about the issuer's confidence.  The structural risks: low free float, large retail allocation, dual-class shares and lock-up dynamics.  The fee anomaly: 23 banks, 0.75% — the lowest on record for a mega-deal.  The TAM debate: $23 trillion in the prospectus versus Damodaran's $5 trillion estimate, and how the AI bucket drives the valuation.  The Tesla parallel: reframing the business to land a tech multiple.  What to watch in early trading: oversubscription, index inclusion fast entry, and the 180-day lock-up overhang. WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal [https://webmail.wallstreetprep.com/whats-the-big-deal] Follow Us On Socials: LinkedIn: https://www.linkedin.com/company/wall-street-prep/ Instagram: https://www.instagram.com/wallstreetprep/ Resources: https://linktr.ee/wallstreetprep

Will the $4 Trillion AI IPO Wave Break the Market? SpaceX, OpenAI & Anthropic

Three mega IPOs are heading to market: SpaceX, OpenAI and Anthropic. Between them they could push the largest tech names to nearly half of the S&P 500, at valuations that have drawn obvious comparisons to the dotcom era. In this episode, Debs and Graham debate whether those comparisons hold, and where they break down. They start with the triggers: extreme index concentration, the scale of the valuations being floated, and the structural role of index funds that are obliged to buy these companies once they join the benchmark.  They then look back at the dotcom boom and bust, drawing lessons from failures like Web Van and Pets.com, businesses whose underlying ideas were sound but whose execution and unit economics were not, and the survivors like Amazon and eBay that collapsed before figuring out their models. The core of the episode is a genuine bull versus bear debate. Debs makes the case that 2026 is not 1999: the S&P trades at around 23 times forward earnings against a long term average near 18, a world away from the Nasdaq's 60 times in 1999, and today's dominant AI names generate real profits and cash flow.  Graham presses the bear case: the CapEx burn behind the AI build-out is enormous, run rate revenue is not a GAAP concept and is open to management, and the return on all that data centre spending remains unproven. They agree the sharpest risk is concentration. With AI-focused names potentially approaching 50% of the index, a miss on a few key data points could move the entire market. They close by each picking the IPO they would back today. Both land on Anthropic, citing a more measured profile and the principle that being first is not the same as being best, while acknowledging that the real financial picture for OpenAI and Anthropic will only become clear once their S-1 filings arrive. Key Discussion Points: The three mega IPOs: SpaceX, OpenAI and Anthropic, and the valuations being floated.  Concentration risk: the Magnificent Seven, index fund mechanics and the path toward 50% of the S&P 500.  Lessons from the dotcom crash: why execution and unit economics mattered more than being first. Valuation reality check: forward earnings multiples today versus 1999.  The bull case: real profits and cash flow among today's AI leaders.  The bear case: CapEx intensity, run rate revenue scrutiny and unproven returns.  The IPO process: where SpaceX, OpenAI and Anthropic each sit, and why the S-1 filings matter.  The verdict: which IPO each host would back and why. WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal [https://webmail.wallstreetprep.com/whats-the-big-deal] Follow Us On Socials: LinkedIn: https://www.linkedin.com/company/wall-street-prep/ Instagram: https://www.instagram.com/wallstreetprep/ Resources: https://linktr.ee/wallstreetprep

Can Claude Replace Investment Bankers? We Graded the Output.

How good is AI at building a DCF?  In this episode, Debs and Graham continue their Claude for Excel series, this time prompting the tool to construct a full discounted cash flow valuation for Lululemon from a single instruction.  The goal is to test what AI can and cannot do in real valuation workflows, and what that means for analysts working in equity research, investment banking and M&A. Graham walks through DCF fundamentals from first principles, covering future cash flow projections, WACC, terminal value and the inputs that genuinely drive valuation outcomes.  He then opens Claude for Excel and gives it a structured prompt — anchored to consensus EPS estimates for stage one, with explicit instructions on modelling best practices including no hardcoded inputs in formulas, standard colour coding, and transparent assumption sourcing. The audit that follows is instructive on both fronts. Claude handles the structural build well — linking assumptions to formulas, applying the Gordon Growth formula correctly for terminal value, and producing a workable enterprise value output.  But the limitations show up in the details that matter most for senior review: the free cash flow build conflates levered and unlevered measures, time period construction is simplistic rather than properly anchored to fiscal year ends and a valuation date, and some formula constructions are opaque enough that auditing them line by line would take longer than rebuilding the section manually. The verdict: a B-minus output.  Workable as a first pass, but not yet at the level where it can be submitted without significant human review.  The broader question the episode closes on is whether AI tools like Claude for Excel are positioned to replace the analyst role or to elevate it — with Graham making the case that the analyst job as historically defined is exactly the workflow these tools are now competent at, while the judgement-heavy associate role remains some distance from being automated. Key Discussion Points: DCF fundamentals: future cash flows, discount rates, terminal value and the inputs that actually drive valuation outcomes.  Prompting strategy: how to structure a Claude for Excel prompt to anchor projections to consensus estimates and enforce modelling best practices.  Where AI delivers: structural build, formula linking, Gordon Growth application, sensitivity analysis output.  Where AI falls short: free cash flow build, time period construction, opaque formulas that resist quick audit.  Sensitivity analysis: long term growth rate versus WACC as the two real swing factors in any DCF.  AI in finance careers: the analyst role versus the associate role and what realistic automation looks like over the next 12 to 24 months. WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal [https://webmail.wallstreetprep.com/whats-the-big-deal] Follow Us On Socials: LinkedIn: https://www.linkedin.com/company/wall-street-prep/ Instagram: https://www.instagram.com/wallstreetprep/ Resources: https://linktr.ee/wallstreetprep

Claude for Finance: Building a Live Merger Model with AI

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. WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal [https://webmail.wallstreetprep.com/whats-the-big-deal] Follow Us On Socials: LinkedIn: https://www.linkedin.com/company/wall-street-prep/ Instagram: https://www.instagram.com/wallstreetprep/ Resources: https://linktr.ee/wallstreetprep