Explore every episode of the podcast What's The Big Deal?
| Title | Pub. Date | Duration | |
|---|---|---|---|
| The Truth Behind Paramount's $110 Billion Battle for Warner Bros. Discovery (Winners & Losers) | 05 Mar 2026 | 00:36:25 | |
Welcome to the second episode of the 'What's the Big Deal?' (WTBD) podcast powered by Wall Street Prep. WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal Follow Us On Socials: | |||
| Nvidia Under Pressure: Is the AI Chip Monopoly Finally Cracking? | 14 May 2026 | 00:37:24 | |
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. WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal Follow Us On Socials: | |||
| How AI Data Centres Are Funded — And What Happens When the Money Stops | 07 May 2026 | 00:27:13 | |
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. Why Wall Street Prep? Wall Street Prep is the trusted training provider for the world's top investment banks, private equity firms, Fortune 1000 companies and business schools. Our online training and instructor-led boot camps are direct adaptations of our corporate training, making Wall Street Prep the ideal choice for those looking to break into finance. DISCLAIMER: The information provided in this video is for educational and entertainment purposes only and does not constitute financial, investment, tax, or legal advice. Investing involves risk, and you may lose some or all of your capital. 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. WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal Follow Us On Socials: | |||
| Private Equity: Leveraged Buyouts Explained (How to Analyze Deals Like a Pro) | 30 Apr 2026 | 01:02:49 | |
This week Graham and Debs try something different. Rather than dissecting a single deal, they go back to basics with one of the most important concepts in finance — the leveraged buyout — and build up from first principles using two of the biggest real-world examples in the market right now: the $18B acquisition of Hologic and the $55B acquisition of Electronic Arts. Graham walks through the core LBO framework using an accessible house purchase analogy, explaining how leverage turns a 1.5x equity return into a 3x return, what drives that amplification, and what the key variables in any LBO analysis actually are. From there the conversation covers what makes a good LBO candidate, the concept of cash conversion, how loan-to-value has evolved since the early days of private equity, and the three main value creation levers available to a private equity owner. The second half of the episode puts theory into practice. Graham runs a live napkin LBO on both the Hologic and EA deals — walking through sources and uses, entry multiples, debt paydown assumptions and return calculations — and asks the central question: do the numbers actually make sense? The episode closes with a broader conversation about the evolution of private equity — from the generalist, high-leverage model of the early 90s to today's specialist, operationally-focused landscape — and what record levels of dry powder mean for returns going forward. Key Discussion Points: LBO fundamentals — what a leveraged buyout is, how leverage amplifies equity returns, and the key variables that drive an LBO model. LBO candidates — what makes a business suitable for a leveraged buyout: cash conversion, recurring revenues, predictable cash flows. Sources and uses — how deals get financed, what refinancing existing debt means and why a target's cash is a legitimate source of transaction funding. Money multiple vs. IRR — what each metric measures and why you need both to evaluate a deal properly. The Hologic LBO walkthrough — entry and exit multiples, debt structure, return sensitivity and the revolving credit facility question. The EA deal — 30x entry multiple, $20 billion of debt, and why the base case numbers require a significant EBITDA growth story. Co-investment and sovereign wealth — why mega-deals increasingly rely on structures beyond the traditional GP/LP fund. The evolution of private equity — dry powder, multiple expansion and why operational improvement matters more than ever. Why Wall Street Prep? Wall Street Prep is the trusted training provider for the world's top investment banks, private equity firms, Fortune 1000 companies and business schools. Our online training and instructor-led boot camps are direct adaptations of our corporate training, making Wall Street Prep the ideal choice for those looking to break into finance. DISCLAIMER: The information provided in this video is for educational and entertainment purposes only and does not constitute financial, investment, tax, or legal advice. Investing involves risk, and you may lose some or all of your capital. 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. WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal Follow Us On Socials: | |||
| How Goldman Sachs, JPMorgan & Morgan Stanley Make Billions (Explained) | 23 Apr 2026 | 00:34:09 | |
Q1 2026 delivered one of the strongest quarters on record for the major investment banks and in this episode, Debs and Graham break down exactly what drove it. Starting with the headline numbers at Goldman Sachs, JPMorgan and Morgan Stanley - nearly $90 billion in combined revenue, up 12% year on year. They unpack why this quarter was unusual: all three core revenue engines fired simultaneously, something that rarely happens. The conversation moves through each division in turn. On the M&A and ECM side, fees were up 40% year on year, with Graham making the case that pent-up demand is driving a deal pipeline that could make 2026 a record year. Trading revenues across the top five banks came in at nearly $50 billion. A figure that surprised even seasoned market watchers, with Debs explaining why volatility, not bull markets, is the real trading revenue driver, and why JP Morgan, Goldman and Morgan Stanley each benefited for very different reasons. Wealth management, meanwhile, posted quieter but resilient growth on the back of significant asset inflows. The episode also doubles as an investment banking primer. With Debs fresh from teaching spring week programmes at major banks, she and Graham walk through how the different divisions are structured, what each one actually does, and what a career in each area really looks and feels like. They close with the central question: is this a one-quarter spike driven by exceptional market conditions, or the beginning of a sustained recovery for investment banking? Key Discussion Points: Q1 earnings overview: combined revenues, growth rates and why broad-based outperformance across all divisions is unusual. M&A and ECM recovery: what's driving the 40% fee growth and whether the pipeline supports continued momentum. Trading revenues: why volatility is the key driver, and how JP Morgan, Goldman and Morgan Stanley each captured it differently. Wealth management: steady asset inflows, fee growth, and why it matters more than the headlines suggest. Investment banking divisions explained: ECM, DCM, M&A advisory, and the difference between markets and banking roles. Career insights: work culture, skills, deadlines, and how to think about which part of the bank suits you. Outlook: sustained recovery or short-lived spike? WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal Follow Us On Socials: | |||
| OpenAI vs. Anthropic Explained: Business Models, Valuations & IPO Breakdown | 16 Apr 2026 | 00:39:05 | |
ChatGPT vs. Claude. Consumer vs. enterprise. Own your infrastructure vs. lease it. On the surface, OpenAI and Anthropic look like the same business. Look closer and the differences are significant and they matter enormously for investors. In this episode of WTBD, Debs and Graham go under the hood of the two most talked-about AI companies in the world, breaking down what their business models actually look like, how their revenues compare, what recent fundraising rounds tell us about their valuations, and whether there's really room for both to win. Topics covered: → Business model differences: consumer vs. enterprise, API vs. subscriptions → Infrastructure strategy: why owning vs. leasing compute matters long-term → Strategic partnerships: Microsoft, Google, Amazon and what they signal → Revenue and cost efficiency: who's doing more with less → Recent fundraising: $850B vs. $380B, why the valuation gap? → IPO outlook: target valuations, retail investor appetite, and the $1 trillion question → Winner-takes-all or fragmented market? The $5 trillion AI market breakdown Whether you're an investor weighing up the AI space, a finance professional tracking the IPO pipeline, or just trying to cut through the hype, this is the episode for you. Why Wall Street Prep? WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal Follow Us On Socials: | |||
| Q1 2026: A Record-Breaking M&A Quarter — Inside the Unilever $45BN Deal | 09 Apr 2026 | 00:28:00 | |
Q1 2026 just delivered the most mega-deals in a single quarter, ever! Why Wall Street Prep? WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal Follow Us On Socials: | |||
| SpaceX to File for Biggest IPO of All-Time ($1.75 Trillion Valuation) | 26 Mar 2026 | 00:38:26 | |
Elon Musk's SpaceX is reportedly preparing to file its U.S. IPO prospectus as early as this week, targeting a public listing this June. WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal Follow Us On Socials: | |||
| Will AI Replace Wall Street Investment Banking Jobs? | 19 Mar 2026 | 00:48:20 | |
Welcome to the fourth episode of the 'What's the Big Deal?' (WTBD) podcast powered by Wall Street Prep. AI is advancing at an exponential pace. Tools like Claude, ChatGPT, CoPilot and Shortcut are fundamentally reshaping workflows and what it means to be an investment banking analyst. But where are we right now? And where are we heading? In this special episode of the podcast Wall Street Prep Founder & CEO Matan Feldman and Graham Smith discuss the current state of play and if analyst jobs are really under threat. SPOILER ALERT: The overwhelming takeaway is completely counterintuitive to the current prevailing narrative and that is potentially very exciting. Why Wall Street Prep? Wall Street Prep is the trusted training provider for the world’s top investment banks, private equity firms, Fortune 1000 companies and business schools. Our online training and instructor-led boot camps are direct adaptations of our corporate training, making Wall Street Prep the ideal choice for those looking to break into finance. Time Stamps: Introduction: 00:00 The Current State of Play for AI Tools in Investment Banking 02:18 How Good Are These Tools Right Now 04:39 What Do These Tools Do Well 5:52 The Dangerous Errors These Tools Make 7:39 Are These Tools Good At Tricky or Advanced Analyst Work 11:42 The Impact Will These Tools Have on IB Recruiting Pathways And What You Need To Be Successful 14:47 The Potential Increased Demand for Investment Bankers 20:52 Are Investment Banks Replacing Analysts With Technology 23:38 What AI Tools Are Investment Banks Using (Claude, CoPilot, GPT) 26:35 What You Need To Do As A Future Investment Banker 27:21 Why You Still Need Fundamental Financial Knowledge As Well As AI Tools 30:03 The Dangerous Temptation Of AI Tools & The Wrong Way To Break Into The Industry 34:29 Which Divisions Are Adopting AI 36:26 What AI Doesn’t Do: 39:23 Are Roles Under Threat & How Do You Future Proof Yourself 42:42 Will Be An Investment Banker In The Future Become More Interesting 45:50 Advice for Analysts & Future Investment Bankers 47:33 DISCLAIMER: The information provided in this video is for educational and entertainment purposes only and does not constitute financial, investment, tax, or legal advice. Investing involves risk, and you may lose some or all of your capital. 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. WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal Follow Us On Socials: | |||
| The $1.75 Trillion SpaceX IPO: Everything You Need to Know. | 11 Jun 2026 | 00:35:21 | |
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 Follow Us On Socials: | |||
| Will the $4 Trillion AI IPO Wave Break the Market? SpaceX, OpenAI & Anthropic | 04 Jun 2026 | 00:28:14 | |
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 Follow Us On Socials: | |||
| Can Claude Replace Investment Bankers? We Graded the Output. | 28 May 2026 | 00:24:32 | |
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 Follow Us On Socials: | |||
| Claude for Finance: Building a Live Merger Model with AI | 21 May 2026 | 00:49:43 | |
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 Follow Us On Socials: | |||
| EX-BANKERS EXPLAIN: Investment Banking Mistakes To AVOID In Your First Year | 18 Jun 2026 | 00:30:51 | |
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 Follow Us On Socials: | |||
| Global M&A Just Hit a RECORD $2.8 TRILLION. Here's What's Driving It. | 02 Jul 2026 | 00:23:50 | |
Global M&A hit a record $2.83 trillion in H1 2026, the highest total since records began, eclipsing the 2021 peak of $2.74 trillion. Deal values in Q2 alone were up 41% year-on-year, deal count up around 10%, mega deals continue to dominate, and small-cap and venture activity is up 200%. But the picture underneath the headline numbers is more complicated, and both think the drivers behind the surge may not last into 2027. In this episode, Debs and Graham work through Bain's mid-year M&A report and unpack the themes behind the numbers. They open with market context: SpaceX post-IPO with the share price now below $160 and the first lock-up expiries approaching, the AI mini-correction of June driven by CapEx discipline concerns, Meta joining xAI in selling excess compute capacity, and news of a Chinese frontier model claiming performance at 10% of the cost of leading Western models. The M&A conversation itself focuses on the defensive posturing thesis. With geopolitical, business model, and AI-disruption uncertainty at unusual levels, large corporates are buying up smaller disruptive companies as insurance against being outmaneuvered. The 200% increase in small-cap and venture activity supports the read. Debs highlights the awkward setup for a typical M&A cycle: hawkish interest rate environment, frothy sector valuations, and low certainty, none of which usually correlate with peak deal activity. The sector-by-sector split reveals financial services quietly lagging the broader market. Graham & Debs speculate on why, and neither has a strong answer. Europe is the standout regional performer, driven partly by the valuation gap versus the US (roughly 15x forward P/E versus 22x) and partly by the possibility that acquirers are moving now ahead of a mooted merger benefit test that could add a second regulatory hurdle to European deals. The episode closes on H2 predictions. Debs is skeptical the second half will match the first, citing hawkish rates, frothy valuations, and the pent-up demand carrying over from 2025 already being absorbed. Graham expects continued growth from the existing pipeline but flags 2027 as the genuine question mark: once the current pipeline works through, whether the fundamentals actually justify M&A at this level is an open question. Key Discussion Points:
Bain M&A Report: https://www.bain.com/insights/m-and-a-midyear-outlook-2026-a-winners-paradox/ WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal Follow Us On Socials: | |||
| Private Equity vs. Private Credit Explained in 15 Minutes | 25 Jun 2026 | 00:14:33 | |
Two of the biggest growth areas in finance over the last decade, but the differences between private equity and private credit are often misunderstood, especially by candidates trying to decide between them. In this episode, Debs sits down with Graham, who spent a decade at Ares Management for a Q&A-style explainer that breaks down what each actually is and how the day-to-day differs. Graham starts with the fundamental distinction: private equity invests in companies that don't trade on the public market, private credit makes illiquid loans to those companies. From there the conversation moves through the deeper differences. The motivation gap, where equity investors are hunting for upside and credit investors are protecting capital because their upside is contractually capped. The return profiles, with PE targeting 15%+ IRRs at the asset level versus credit closer to high single digits to low double digits, and how private credit funds use fund-level leverage to amplify those returns. The conversation then turns to how the two sides actually interact. Graham flags that he never saw a firm finance its own PE deals with its own credit fund and that the base case is keeping the two operations independent. He explains how closely PE sponsors and credit providers negotiate during deal-making, what makes a company attractive to both sides simultaneously (recurring revenue, cash flow visibility, growth prospects), and why the diligence focus differs significantly. Equity focuses on the upside thesis, credit focuses on every way you could lose money. The episode closes on career-relevant differences. Single-deal depth in PE versus higher deal flow in credit, the generalist versus specialist question, and how the route into both has fundamentally changed since Graham's own start at Lehman Brothers in the mid-2000s. Key Discussion Points: The fundamental distinction: investing in companies vs. making illiquid loans. Motivation gap: upside potential vs. capital preservation, and what capped upside means in practice. Return profiles: 15%+ IRR in PE vs. high single digits to low double digits in credit, plus how fund-level leverage closes some of that gap. Firm independence: why PE and credit arms within the same firm don't typically finance each other's deals. Deal mechanics: how PE sponsors and credit providers negotiate, and what makes a company attractive to both sides. Diligence focus: market opportunity vs. downside protection, and how the two diligence mindsets differ. Career-relevant differences: deal flow, depth vs. volume, generalist vs. specialist, and how to break in today. WTBD Newsletter: https://webmail.wallstreetprep.com/whats-the-big-deal Follow Us On Socials: | |||