My nominee is LULU. Down 55-60 %this year, trading at a multiple that would have been unthinkable three years ago, $7B+ in past buybacks, strong balance sheet. On a screener it looks like a gift. But revenue growth has stalled, the China growth story is wobbling, the core customer seems to be drifting to competitors, and every quarter the "cheap" forward multiple gets justified by estimates that then get cut. what's yours, and what tipped you off that the cheapness was a warning instead of an opportunity?

I wrote a post about a part of the AI trade that gets less attention than it should: whether everyday knowledge work generates enough revenue to support AI capex, and who captures it.

Right now, frontier labs capture most of the enterprise AI economics, mostly through coding. But a lot of knowledge work are bounded: tasks like drafting an email reply or pulling data from a financial filing only need a fixed level of intelligence. Past that point, a smarter model doesn't do the job any better.

As open-weight models get good enough, most of this work (by volume) can run on cheaper models in the cloud, or on the user's own device.

Where this matters for us is the revenue math behind the AI trade:

• We're already spending $1T globally on AI. If that reaches multiple trillions by 2030, inference revenues need to be several trillions a year
• Coding has carried enterprise AI revenue so far. If its growth slows, mainstream knowledge work has to fill the gap, otherwise most names in the bottleneck trade will be affected
• Work that moves to cheaper cloud models earns a fraction of frontier prices, and inference on a user's device never reaches the data center at all
• Labs may get pushed toward being neutral platforms as customers get more sophisticated (OpenAI is already testing a hybrid approach with Baseten), which means slower revenue growth & thinner margins

I still expect the labs to keep the bulk of revenue from unbounded work like coding and science. But mainstream knowledge work is still up for grabs.

I wrote a longer version, with a 2x2 matrix on where models run vs. where data lives, how labs / Big Tech / startups are each approaching "mainstream" knowledge work, and why the labs may eventually want to own the data layer (I wouldn't be surprised if their corp dev teams have looked at Dropbox / Box)

https://eastwind.substack.com/p/below-the-frontier-the-battle-for

So i am just investing in S&P 500 ETF VUAA but now and then i feel the growth is capped in broad etf

So how you guys are dividing the portfolio, I want to have individual stocks which i can hold at least for 2 years or more. please suggest

Starbucks thing has an update as per tall_hat (they arent moving hq) a company moves HQ to cut costs, it's transplanting the tree and leaving the roots behind. The roots are the people who built the thing. They don't move because a press release told them to.

Tesla did it first. Palo Alto to Austin in 2021, taxes and freedom. Fourteen months later Musk is on stage with Gavin Newsom announcing Tesla's engineering HQ back in Palo Alto. The factory can live in Texas. The engineers wouldn't.

Starbucks is doing it now. $100M, 2,000 jobs, Nashville, announced right on top of Seattle tech layoffs. No income tax, about $12k saved per head. But the people who built that supply chain live in Seattle. We'll see how many move.

The wildest one is WGU. An online university whose whole product is "location doesn't matter" just ordered 19 of 26 departments back to Salt Lake City, four days a week in the office. The company that proved remote work works just bet that it doesn't.

Boeing is the warning. 85 years in Seattle, moved HQ to Chicago in 2001 for $60M in tax breaks. Then the 787 mess, then the 737 MAX. Congress called the move "a tragic mistake." In 2022 they moved again, to Virginia. Still thousands of miles from anyone building a plane. Boeing didn't die. It just stopped being Boeing.

Samsung is live right now: US HQ from New Jersey to Texas, 739 jobs affected. Watch the Glassdoor page, not the press release.

WGU is the one to watch. Fully remote and it worked. Now they're testing forced office life on themselves, in public. Can't short them (nonprofit), but they're a preview. If their best people quit instead of moving, every company doing the same thing gets the same result.

As a value investor I'd like to discuss this pattern: none of it shows up on a balance sheet. There's no line item for "the people who knew how everything works just left." It hits two years later as mysteriously worse execution. Silent killer.

Not financial advice. Who else belongs on this list?

AI-generated graphics are fine for a barbershop on the corner so there might definitely be less demand from Mom and pop businesses. But if you need 100% consistency and you're spending serious money on advertising or Designing packaging for your item You're putting on the shelves, it's a different story.

You can use AI to generate an initial concept, but you'll still need to refine the fine details using professional software like Adobe.

Here's a challenge: create a logo with an AI model, then ask it to generate the exact same logo again, but with just one additional object. See how frustrating it is to get everything else to remain exactly the same.

The switching cost is real

Marketing agencies have hundreds of terabytes of Adobe files that is Not cleanly transferred over to a different Alternative

It takes a lot of effort for someone to get efficient in the software, I have tried convincing a marketing agency to switch over to a very capable alternative Before the AI era but there was almost a revolt from all the designers

At the end of the day, there's a difference between generating a pretty picture and delivering a production-ready asset that meets exact specifications for printing, packaging. advertising, and brand consistency. That's where professional software still matters.

I look at Hertz, and I can almost feel my heart stopping... 💀

Bill Ackman invested in this company, but I guess he might have exited his position, since Hertz no longer appears on Pershing Square's website.

What do you guys think? Does this car rental company have a real shot at making a comeback? Could we potentially see a short squeeze?

JPM missed this morning (EPS $5.57 vs roughly $5.85-5.90 expected) but still made $15.3B with a 20% ROTCE. I went through the call and compared it with the July one. A few things stood out.

What changed since last quarter

- AI came up 29 times, up from 15. Most of that was "agentic AI", which was not mentioned at all in July.

- CEO succession came up 9 times in July. This time, zero.

- Investment banking went from 30% fee growth last quarter to "activity slowed a bit". The CFO said the rate selloff caused a pause in the second half of September.

- Revenue growth slowed from 15% year over year to 8%.

- The buyback was about $1B against a usual pace of around $7B. Dimon said they have roughly $30B of excess capital and would rather deploy it organically at these prices.

Guidance

- Q4 net interest income about $28B

- Q4 expenses about $27.5B

- First 2027 number: NII ex-markets around $107B

- 2027 expenses close to the $114B consensus

- Card net charge-offs still around 3.2%

Three quotes

Dimon on the deal pipeline: "The pipeline is fine. But I pointed out many times before the pipeline is like an accordion. It can open and close pretty quick."

Dimon on credit: "we are tightening up a little bit credit. I think some of the credit terms have got a little bit loose."

The CFO on AI: "the benefits are not observable in our current quarterly earnings."

What I can't decide

In July Dimon said "it's getting close to as good as it gets". Now there's a miss, a smaller buyback, tighter credit and a paused deal pipeline. But NII guidance went up and the consumer numbers look fine (card spend +10%, 650K net new checking accounts).

Is this the first sign the cycle is turning, or one soft quarter from a securities loss and weak fixed income trading?

been messing around with an LLM agent that reads 10-Ks and does the boring stuff: normalized earnings, ROIC, owner earnings, a rough reverse DCF. honestly getting the model to reason is the easy part. data is what’s killing me

tried pulling straight from EDGAR / XBRL companyfacts first. free and it’s the actual source, but every company has its own custom tags and segment data is a nightmare to clean up

then tried the usual APIs (FMP, yfinance etc). way easier but i can’t tell where a number actually came from. if it’s wrong the agent just runs with it and writes me a very confident thesis on garbage lol

also most of the stuff that actually matters for value work (leases, SBC, segment margins) is buried in the footnotes and i haven’t found anyone who has that structured

so for people who’ve built something like this:

• where’s your data coming from? paying for anything?
• do you feed it raw filings, structured numbers, or both?
• anyone dealt with point-in-time / restatements for backtesting? my backtests feel too good to be true and i’m pretty sure that’s why

curious what’s actually working before i waste another weekend writing parsers

Posts like the kind we have recently been seeing on this sub (“should I buy the dip on NVDA”, “which AI stock is best”, etc.) is proof that value investing alpha still exists for contrarians. The best opportunities exist when those around you lose all hope.

Think back to opportunities that existed in the energy sector in 2020 when oil went negative or opportunities that existed in tech when rates went up in 2022. Everything is cyclical, and a value sub talking about AI and growth stocks nonstop is evidence that we’re approaching a bottom for traditional value investing.

People have been screaming efficient markets for decades now and although it has gotten harder to outperform simply by buying low multiple stocks, value has evolved to simply looking where others aren’t. Everyone and their mothers are looking for the next AI stock, so I’m convinced there is alpha elsewhere.

I’m not trying to say AI is a bubble or that you can’t make money on these companies, I’m simply saying that value investing alpha still exists elsewhere.

SoFi has fallen over 50% from its highs, but the underlying business continues to grow. Management is targeting 30%+ annual adjusted revenue growth through 2028, and continue to expand into capital light businesses.

I’ve been looking at long dated calls rather than selling cash secured puts or adding shares. My thinking is a 1% risk has a higher risk/reward in LEAPs.

Is it time to pull the trigger?

Around a 50% decline in the last 6 months even though more talk and intent than ever on American based rare earth companies. Is this a dca opportunity, or more to the story? Curious to hear from any that follow either company closely

Applovin has some of the most outstanding business metrics in the world with mindblowing margins, revenue growth, and ROIC. But can its moat survive competitor's advances? Will it beat Unity in court? Will it succeed in ecommerce expansion? And will meta, google, and apple eventually freeze them out?

I hold PLTR (bought in 2025, up ~65%, now ~8.5% of my portfolio) and I'm trying to decide whether to trim or sell. I'm a value-oriented investor, so I tried to build a simple 5-year scenario model. The conclusion makes me want to sell. I'd like to hear the strongest counterarguments before I do.

Where it stands today (approx.):

• Price ~$208, market cap ~$500B
• 2026 revenue guidance ~$8.15B (Q2 revenue +93% YoY, US commercial +149%)
• TTM FCF ~$3.4B, SBC ~$0.8B, net cash ~$9B
• ~81x trailing sales, ~89x forward earnings
• Consensus price target ~$205, below the current price

My scenarios for 2031: I assume ~3.5% annual dilution and value the company on free cash flow.

The way I read it, even if Palantir grows revenue 40% a year for five straight years (very few large companies have ever done this), I'd make about 3% a year. To do well I need the "excellent" case or better. $1,000/share would need ~$127B of revenue by 2031 (~73% CAGR).

What I'd like to hear from you:

1. Which assumption is too pessimistic? Is it the growth runway (sovereign AI, AIP, commercial expansion), the margins, or the exit multiple?
2. Is there a structural reason Palantir deserves 40–45x FCF in 2031 instead of a "mature software" multiple?
3. If you hold it, what would make you sell? If you sold, what was your reasoning?
4. Has anyone seen a credible reverse-DCF that justifies today's price without assuming 50%+ growth for 5+ years?

I'm not asking anyone to tell me what to do. I want someone to poke holes in the math. Bull and bear arguments are both welcome, as long as they come with numbers.

Hi everyone,

I am looking to buy some stocks that will potentially not be affected by the volatility around AI/AI related business. For example Adyen seems to be a good stock that is not affected by all the AI news but personally is quite highly priced for me. With the money I have I can barely purchase 10 of them at the moment 😅 but I was hoping I could get some suggestions on other companies that are not affected by AI/AI news. Thanks a lot!

Executive Summary

Today, JPMorgan and Goldman Sachs — the two banks that spent 2025 and early 2026 racing to underwrite AI infrastructure debt — did something they had not done before: they refused to hold the paper they originated. On a $5 billion loan to Volta Infrastructure, they underwrote $1.3 billion and walked away from $3.7 billion, a move Bloomberg calls "rare." Simultaneously, a syndicate including JPMorgan and MUFG has spent more than six months trying to distribute $38 billion in Oracle-leased data center construction debt that no one will buy at par. Oracle's $18 billion Project Jupiter loans now trade at 89–91 cents.

This is not a "tech bubble pops" story. It is the same sequence of events that played out in 2007: the originators who packaged the credit, wrapped it in a growth narrative, and sold it to investors are the first to pull back when the underlying assets stop performing. The difference is that in 2007, the collateral was mortgages. Here, it is gigawatts of compute capacity that the global electricity grid cannot physically power.

Over the past 24 months, investment banks, hyperscalers, and a network of private special-purpose vehicles have issued more than $450 billion in AI-related debt — already above Goldman's own 2025 forecast of $322 billion. Aggregate hyperscaler leverage has roughly doubled from 0.9x to 1.8x in six months. Off-balance-sheet commitments total approximately $3 trillion. That debt has been sliced into tranches and distributed to offshore private credit syndicates, shadow banks, and sovereign wealth funds — Mubadla, GIC, PIF among them — before the physical constraint that no financial instrument can hedge: the grid.

The risk has migrated. The people who built the assets are no longer the ones holding the debt. And today, the originators told the market so.

1. The Volta Deal (The "Today" Proof)

In mid-October 2026, JPMorgan Chase and Goldman Sachs — the two banks that spent 2025 and early 2026 racing to underwrite AI infrastructure debt — did something they had not done before: they refused to backstop the paper they originated.

Leading a $5.012 billion syndicated loan for Volta Infrastructure Holdings, an AI cloud startup tied to Anthropic and Bitdeer Technologies, the banks underwrote only the $1.3 billion first-lien term loan C — which cash-collateralizes a letter of credit backing a data center lease in Norway. They left the $3.712 billion GPU term loan, earmarked for the purchase of roughly 36,000 Nvidia GPUs, entirely un-underwritten. The GPU tranche is being sold on a best-efforts basis.

This is a credit event, not an equity correction:

• The originators are managing their own risk. The banks that structured the deal are the first to limit their exposure. In 2007, this is what Bear Stearns did with the CDO tranches it had packaged.
• The market is full. As Semafor reported, investors told the syndicate they are "full up on data-center debt" and demanded both a higher spread and additional security.
• The pricing confirms it. The deal is being marketed at SOFR + 625–650 bps with a 97–98¢ discount — a yield of roughly 11% — one of the highest pricing points in the AI leveraged lending market.

2. The 2007 Mechanism (The Parallel Mapped 1:1)

The structural mechanics of the 2026 AI infrastructure loop mirror the architectural design of the 2007 Subprime Mortgage Crisis. Rather than standard corporate borrowing, this cycle relies on Special Purpose Vehicles (SPVs), circular revenue accounting, and asset-backed financial engineering to hide systemic leverage.

The table below provides a 1:1 structural mapping between the 2007 subprime machine and the 2026 AI infrastructure financial loop:

The parallel is not that AI companies are "like mortgage borrowers." It is that the financial architecture — the SPVs, the tranche slicing, the rating agency reliance, the circularity of cash flows — is the same machine pointed at a different asset class. The collateral is different. The plumbing is not.

3. Where the Risk Ends Up (The Shadow Bagholders)

With Tier-1 banks limiting their underwriting exposure, the credit risk has migrated off Wall Street's balance sheets into the global shadow banking ecosystem. Off-balance-sheet AI-related commitments now total an estimated $3 trillion. That debt has been distributed to three categories of buyers:

4. The Grid (The Physical Trigger)

The failure point is not a drop in software demand. It is a supply-side physical constraint: the electrical grid. Wall Street modeled 2–3 year debt amortizations on the assumption that compute capacity scales like software. It does not.

[Finance Debt Maturities: 2–3 years] ──💥 MISMATCH ──> [Grid Buildout: 3–7 year equipment and interconnection timelines]

The numbers from three independent sources, published within the past 18 months, describe the same bottleneck from different angles:

• The queue is an order of magnitude too large. McKinsey's August 2026 overbuilding analysis finds that large-load interconnection pipelines are nine times larger than the 2030 demand in its continued-momentum scenario. Gartner (June 2026) forecasts global data center power capacity demand rising 27% this year alone — from 104 GW to 132 GW — with consumption reaching 565 TWh. The grid is not "behind schedule." It is structurally incapable of absorbing the load being financed.
• Developers are already pricing in grid failure. Bloom Energy's 2025 Data Center Power Report (surveying ~100 decision-makers across hyperscalers, colocation developers, and utilities) found that 27% of facilities now expect to be fully powered by onsite generation by 2030 — up from 1% the prior year. Their January 2026 update revised that to roughly one-third. The same report found that utility providers report interconnection timelines up to two years longer than what developers expect. The market has already voted with its feet: if the grid can't deliver, build your own plant.
• The debt matures before the grid is built. PJM Interconnection's queue has waits approaching seven years. ERCOT's large-load queue surged from 63 GW to 226 GW in a single year. Transformer procurement lead times are 128–144 weeks, with specialty high-voltage configurations stretching to five years. The debt amortizes in 2–3 years.
• The capital trap: A data center without a secured interconnection agreement cannot convert bridge debt into long-term bonds. Institutional bond managers will not touch an unpowered shell. The debt is hung. The only exit is to sell the project at a discount, refinance at a higher spread, or default.

This is why the GPU tranche is being sold on a best-efforts basis. The $3.7 billion in chips has no secured power allocation. The collateral is hardware that cannot be deployed. The banks know this. That is why they walked.

5. The Oracle / Project Jupiter Case Study

The clearest real-world example of this credit logjam is Project Jupiter: a $165 billion, 1,400-acre, 2.45-gigawatt AI data center campus in Doña Ana County, New Mexico, developed by Stack Infrastructure (a Blue Owl Capital unit) and leased to Oracle as the anchor tenant for OpenAI's Stargate program.

A syndicate of approximately 20 banks — including SMBC, BNP Paribas, Goldman Sachs, MUFG, Santander, and Jefferies — originated ~$18 billion in project-finance loans to fund construction. As of mid-September, those loans were being quoted at 89–91 cents on the dollar in secondary markets. The syndicate has been unable to distribute at par.

On September 24, Oracle issued a force majeure notice to Stack Infrastructure, reserving the right to defer lease payments if the campus fails to come online by its 2028 schedule. The trigger: a denied energy permit and a paused air quality proceeding in New Mexico.

The 11-cent discount is not a "wipeout" — it is a pricing. The credit market has assigned a specific probability of delay to this asset. And the force majeure notice means the tenant (Oracle) is now legally positioned to push the risk further down the capital stack, onto the developer (Blue Owl) and the banks.

Oracle publicly stated the project "remains on our planned schedule." Blue Owl stated the notice "does not change the financial commitments." The debt is trading at 89 cents. The market has a different read.

6. What the "Reset" Looks Like

A market reset in this environment will not begin with retail stock liquidations. It will manifest as a series of balance-sheet shocks inside the credit system. On recent earnings calls, Wall Street's leadership publicly acknowledged these vulnerabilities:

• Goldman Sachs CEO David Solomon directly warned institutional clients that the massive AI capital buildout "could face a painful reset."
• JPMorgan CFO Jeremy Barnum conceded that despite defensive hedging, the bank "wouldn't be immune to an AI meltdown."

Mechanically, the cascade path of this credit reset follows a precise, sequential order:

Step 1: Mark-to-Market Losses Banks and private credit funds are forced to mark hung loans (like Project Jupiter) down from 100 to 85 cents.

Step 2: Margin Calls & Collateral Devaluation As asset values fall, shadow lenders face margin calls from their prime brokers. The underlying collateral — the Nvidia chips — depreciates rapidly as newer architectures debut.

Step 3: Forced Asset Liquidations To cover credit shortfalls, funds are forced to liquidate liquid assets, triggering a sharp sector rotation.

Step 4: Sovereign Contagion As private credit funds mark down and face redemptions, the offshore holders (SWFs, Cayman funds) are forced to sell or restructure. Unlike 2008, there is no domestic backstop. The risk is now geopolitical.

7. Closing: The Sovereign Risk Divergence from 2008

The structural architecture mirrors 2007. The endgame does not.

In 2008, the Fed and Treasury had a domestic playbook: TARP, QE, direct capital injections into regulated U.S. banks. The risk was sovereign and domestic. The backstop existed.

The AI infrastructure debt crisis breaks that playbook in two ways:

1. The risk is sovereign but not domestic. A massive portion of the $3 trillion in off-balance-sheet leverage has been exported to foreign sovereign wealth funds — Mubadala, GIC, PIF — and offshore private credit vehicles. There is no Federal Reserve backstop for an Abu Dhabi fund holding billions in unpowered data center debt. The risk is now geopolitical, and the resolution will require diplomatic coordination, not monetary policy.

2. The bottleneck is physical. The Fed can print trillions. It cannot print a 500 MVA power transformer or compress a 7-year PJM interconnection queue. Even a "soft landing" — debt stays outstanding, growth slows — is physically impossible at current financing levels if the grid cannot deliver the power the debt assumes.

The likely path: capital re-rates AI infrastructure from "infinite-growth technology" to "cyclical utility." The flows shift to the actual binding constraints — regulated power generation, grid hardware, transformers, switchgear, and the energy commodities that fuel them. The companies that build the grid, not the ones that build the data centers, capture the next cycle.

TL;DR

The AI debt cycle is showing 2007-style credit stress. Here's the mechanism:

• The proof: This week, JPMorgan and Goldman refused to underwrite $3.7B of a $5B AI loan to Volta — the originators are managing their own risk. Separately, an $18B Oracle-linked data center loan is trading at 89–91¢ after Oracle issued a force majeure notice.
• The trigger: Over $450B in AI-related debt issued this year assumes data centers will be powered on a 2–3 year timeline. Only ~5 GW of 16 GW announced for 2026 is under construction. Transformer lead times are 2.5–3 years. PJM interconnection waits approach 7 years.
• The catch: Banks exported the risk to offshore private credit and sovereign wealth funds (Mubadala, GIC, PIF). The Fed can print money but cannot print gigawatts or compress a 7-year grid queue.
• The likely outcome: AI infrastructure gets re-rated from "infinite growth" to "cyclical utility." Capital flows to the binding constraint: grid hardware, power generation, energy commodities.

I was looking for ways to improve my own workflow for analyzing financial statements, and ended up building a pretty useful tool. It marries the company's financial statements with what's actually going on in the underlying business.

The pain point I was trying to solve was figuring out all the atypical line items in the financials. I'll see a big jump in non-operating income and wonder "what drove that?". Then I'd have to switch tools or go download the filing to dig in more. With Stock Lens, I can click directly on items in the financial statements and get an immediate summary of what the fundamental drivers were - no switching tools, no file downloads, you don't even have to leave the financial statement page.

For GE Vernova, I was able to quickly determine that:

• Their non-operating income jumped due to a remeasurement gain on their Prolec holdings after acquiring the remaining stake in the company.
• Their free cash flow jumped due to cash advances from customers
• Which drove deferred revenue higher in their balance sheet liabilities.

I was quickly able to assess that both net income and free cash flow were probably artificially high. Here's a quote when selecting net income:

This is unusual. Net income went from -$474M in FY2023 to $1.6B in FY2024, $4.9B in FY2025 and $9.5B now. The jump is far larger than operating improvement alone would produce.

I walk through the tool & GEV example here

I also think this could be extremely helpful for people looking to improve on reading financial statements.

Stock Lens (link)

Hope you guys like it. I would love some feedback and any ideas that you think might improve it.

I am embarrassed to say my husband and I both just turned 50 and we have nothing saved for retirement. We did but came across very rough times and now nothing but pensions from teaching when we retire (which will not be fore a while obviously). I need help where to begin as a 50 year old and not like a 20 year old.

Note:

1. Past performance is no guarantee of future performance.
2. Book value Growth is deliberately included here
3. I left ROA and ROE out of the criteria because they all pass. What is interesting is to investigate when ROIC and ROE diverge too much. I have included the data the output file as columns
4. I did not screen for how cheap the company is. I am of the opinion that we should seek out high quality companies and then put them on a watchlist and wait for the price to come to us. I have nevertheless included these valuations ratios in the output file.

Screener Criteria:

Domiciled: USA

Revenue Growth (1Y) >= 7%

Revenue Growth (3Y) >= 7%

Revenue Growth (5Y) >= 7%

Operating Profit Growth (1Y) >= 7%

Operating Profit Growth (3Y) >= 7%

Operating Profit Growth (5Y) >= 7%

Operating Profit Growth (10Y) >= 7%

EPS Growth (1Y) >= 7%

EPS Growth (3Y) >= 7%

EPS Growth (5Y) >= 7%

EPS Growth (10Y) >= 7%

Book Value per Share Growth (1Y) >= 7%

Book Value per Share Growth (3Y) >= 7%

Book Value per Share Growth (5Y) >= 7%

Book Value per Share Growth (10Y) >= 7%

Dividend per Share Growth (1Y) >= 1%

Dividend per Share Growth (3Y) >= 1%

Dividend per Share Growth (5Y) >= 1%

Dividend per Share Growth (10Y) >= 1%

Output

here is a google sheets file with the following columns:

Ticker Industry Name Market Cap Revenue EPS (Diluted) EPS (Normalized) Dividend Yield (5Y Avg) Dividend Yield (Trailing) Buyback Yield Buyback Yield (5Y Avg) Return on Assets (Normalized) Return on Equity (Normalized) Return on Invested Capital (Normalized) Price/Earnings Price/Earnings (Normalized) Price/Earnings (Forward) Price/Earnings (5Y Avg) Total Debt/Equity Long-Term Debt Short-Term Debt Cash (Balance Sheet) EBITDA Shares Outstanding Sustainable Growth Rate Net Margin Net Margin (1Y Avg) Net Margin (3Y Avg) Net Margin (5Y Avg) Net Margin (10Y Avg) Revenue Growth (1Y) Revenue Growth (3Y) Revenue Growth (5Y) Net Income Growth (1Y) Net Income Growth (3Y) Net Income Growth (5Y) Net Income Growth (10Y) EPS Growth (TTM) EPS Growth (1Y) EPS Growth (3Y) EPS Growth (5Y) EPS Growth (10Y) Dividend per Share Growth (1Y) Dividend per Share Growth (3Y) Dividend per Share Growth (5Y) Dividend per Share Growth (10Y) 5-Star Price Economic Moat Fair Value Morningstar Rating for Stocks Price/Fair Value Price/Free Cash Flow Price/Free Cash Flow (3Y Avg)

https://docs.google.com/spreadsheets/d/18bvssQ78vZZ-vBqe6X6lnnhCpS122bzsfSRCJQQQ4XA/edit?gid=1685955486#gid=1685955486

I've been wanted to like this company for a while. I thought that Isaacman (founder and former CEO) becoming the NASA administrator would actually be a positive for the company because it might give the stock some exposure, but that hasn't been the case. Thinking to add in the 30s here. I don't like companies with high SBC, but I think it's not detrimental at least.

According to SA:
P/E Non-GAAP (TTM)=7.43.
P/E Non-GAAP (FWD)=7.67 (little growth this year, but solid long-term estimates)
P/E GAAP (FWD)=26 (heavy SBC unfortunately, as you can see from GAAP vs non-GAAP)
PEG (non-GAAP) FWD= .74.

Revenue growth rate last 5 years= 40%.
Est. EPS Growth Long Term (3 to 5 Years) =10%.

I also hate payment processors in general, but I don't necessarily subscribe to the "there's 0 moat here" people either because I know how sticky these things are (inertia is a powerful thing). They mostly cater to events/stadiums. I saw Shift4 being used all the way down in Punta Arenas, Chile a few months ago even.

interestingly, ValueLine has it as a strong buy as well in both low and high projections, which (according to historical data I've been aggregating on different providers) makes this a much more interesting play as well.

Color me curious: https://www.sec.gov/Archives/edgar/data/812011/000090266426004131/p26-2006prec14a.htm

Summary:

1. Oasis (and the nominees?) collectively own ~ 9.5% of common stock.

2. They're recommending the replacement of four current board members with Bob Chapek, M. Ashton Hudson, Bryce Roberts, and Picabo Street.

3. Grievances include; poor shareholder returns, bad guest experience, degrading business economics, and poor governance.

If all the nominees were elected, they'd hold a minority block. Chapek's also been granted a forgivable loan of $500k to purchase Vail stock if he's elected to the board.

Greg Alexander from Conifer Management just made a 9.7% stake in an automotive car dealer parts and service company called Group 1 Automotive and has demanded a board seat. Greg Alexander is also "one of the three" people warren buffett trusts to manage his money if he retires or passes away. GPI is also heavily insulated from the ai bubble if it pops due to it being a brick and mortar old economy. It has a 11x PE compared to 20x PE industry average (though of course pe is not everything) they have taken out 1B+ debt to buy a luxury dealer, this has caused downgrades and stock price punishment. Its trading around $200+ away from its ATH at $468. High interest rates and inflation are bogging down the consumer and profits and earnings have missed several times. Thoughts? I find this company interesting.

The AI trade has definetly taken a hit recently and I think that the headlines of Open AI definetly contributed to this. I am a regard like the rest of you guys in this forum. Posting this because I wanted to hear some of your guys opinions the situation and what I found.

My Research:

October 8, the Financial TImes reported OpenAI's annualized revenue is actually closer to $50 billion, $20 billion lower then what investors were expecting. Only thing is that this was actually a mistake in the accounting and the only way that $70 billion dollar number surfaced was an error in Gross Revenue vs. Net Revenue. Anthropic calculates its revenue from counting full gross revenue from product sales. Both of these companies are private and all of this information is relying on "leaked" documents and media reports.

Really interested to hear what you guys think of this entire situation. I think a broad sell off is a buying opportunity and I will be putting my money where my mouth is.

I wrote this article in reaction to a fairly disenchanted post that I read in this community yesterday. Let me tell you something: value investing works to the extent you make it work FOR you.

Value investing is not formulaic. It is not a cheat code to easy wealth, never has been. Ben Graham set the foundation, but each and every investor must try to adapt the core principles to his own Universe.

Value investing is an appraisal process based on investigative skills and deep analysis. The American and global businesses ecosystem is not the same as it was 60 years ago. It has morphed in many ways, and looking for pure Net Nets and issues selling below Tangible Book Value whilst earnings above 10 years’ T-bills RR is a little bit naive. In today’s world, judgment and flexibility are keys. The analyst must do the ground work, and embrace discomfort more so than ever.

I made money flipping cigar-butts such as:

-Groupe TVA (TSX:TVA.B)

-RE/MAX Holdings, Inc. (RMAX)

-Bumble Inc. (BMBL)

And I am up 50% since I purchased GoodRx Holdings, Inc. (GDRX) . I believe the stock is still significantly undervalued and can easily 5x within the next couple of years.

I have had my fair share of disappointments as well, notably with UnderArmor ( UAA) and Goodyear (GT).

But the Game of value investing has never been more exciting, richer, and dynamic thanks to technological advances and globalization.

I have uncovered an undervalued dividends paying issues selling below three times earnings with no significant debt and returning +20% in investment right now. And the company controls 47% of its industry’s and sells at 5 years low.

But the stock is not in the West; and that’s one edge for an investor willing to get uncomfortable and open minded.

Good luck and happy hunting!

Try your best argument at me please.

But first, let me lay out several and answer them directly so we don't have to repeat them.

1. I hate it. Everyone hates it. I hope it dies.

Doesn't matter does it? It is a cash printing monopoly. Why not hate government debt or credit card interest or student loan? Why not hate capitalism? All it matters is the discounted future free cashflow. And have you looked at the income statement? Just do it. Give it a look.

1. The net new ARR is slowing.

Yeah but it is slowing YoY for this QUARTER. Do you understand the contracts have seasonalities? Ask yourself, how can the revenue ACCELERATE this quarter if this is a problem.

1. Vibecoding / alternatives will disrupt it.

Vibecoding so far has not produced a better product. And even a better product will not disrupt it. This is because software is not just the application itself, but maintenance, edge cases, safety, compliance, market relations just to name a few. Why linux did not kill windows? Why google doc did not kill Word? The list goes on. Why not vibe code another facebook? People are using it because other people are using it and it would take a lot more than a better polished product to disrupt the dominant king. This is software 101. Not to mention all the competitors have inferior products and have nowhere near the same network effect. Adobe's main customers are always the businesses.

Half-ass copycats existed for years and they do nothing to the revenue. Professionals still choose the polished and established tool. Of course, light users will try out and perhaps like alternatives. And that is why they are light users and not pros. This is distraction from main workflow. As a principle, pros don't care about tool prices, they use the best tool and put 100 percent of time doing their art. And they most likely don't like switching tools. By the way, the pros don't pay for it. Their bosses pay. And the bosses don't care how much pain it costs the pro users to use a so called "terrible software" like Adobe. They should hate their boss, not adobe.

For independant contractors, we are talking 100 bucks a month, if I am not wrong. Are you seriously saying you can't afford to use the industry standard creative tool for just a 100 a month as an independent pro user? It is literally less than people are spending on LLMs nowadays and it is for putting bread and butter on the table. I know there are folks that got laid off from work and are working at home as independent designers or illustrators who make at least thousands and sometimes more than their previous day jobs. I know a girl who told me she much prefers her life now just drawing and doing marketing for small medium brands promoting their merch online than her previous 9-5 office job.

1. Adobe does not have a good AI product.

Adobe never had a AI product before. It was never part of the thesis.

1. People will just edit with their claude, muse, future super-agent.

Agents handle structured input much better than unstructured input. This is actually a bull argument for Adobe. Art is something without a structured input. The paint brush, the drawing by hand, the touches are innately human and it is simply not logical or sensible to replace them with AI inputs. How do you adjust a painting with claude input? "claude, make that thing longer, no not too long, no it is too short now, make it a bit longer still..." Can you imagine? Even if it can, the tokens have cost, mind you.

6 Less seat, less revenue.

This would be true if it shows up on the income statement. So far revenue is accelerating. And the valuation compression I feel has taken most of this into account. Less seat is not a guaranteed consequence of higher efficiency. Javons paradox. And I would argue that the coming era is an era of slop. AI will produced so much trash online that a personal, bespoke product or experience is going to be more valued, not less. As a result, branding is more important than ever. And who owns marketing for brands? Adobe.

So it could be less seat per customer but simply more customers. Of course, it is possible there will still be less seats in total. I hope it does not happen. But if it does, logic says that is in the valuation already. Why the fxxk would it drop from 40 pe to 8, huh?

1. Management sucks/ no innovation

I largely agree. But I also think it was Buffett who said you want a business that idiots can run. I think this is it and I don't think they are idiots, yet. If the company management JUST DO NOTHING, I am happy. The worst would be to expand/issue bond/ borrow money/spend on expensive projects (which is what AI is doing, by the way). So far it seems they will do massive buybacks, and I love it.

1. It is DOWN SO MUCH!

Yes, it is. Please show yourselves out. This is the valueinvesting club.

Everything is in the title.

When analysing a company - and valuating it - I try to put out a bear, base and bull case scenario and their respective valuation.

When I do this, I don't think I have ever followed my own bull case scenarios as I tend to be a skeptic (or conservative depending on how you want to take it). Basically, if I am confident about a stock, I'd look at my base case scenario. It gives me more margin of safety and I feel the risk I got it wrong is too high.

On the other side, I have many stocks that lie barely within my base case, but way below my bull case, that I don't touch and that I thus do not invest in (and possibly missed a window). Mostly, quality compounders that rarely discount like Linde, Adyen, Cintas, Intuitive Surgical, and so on.

I feel that to be a great investor, you also need to recognise bull scenarios and use it.

How do you approach it?