u/JoeInOR

The PEG ratio tries to blend valuation and growth, but I have a few problems with it. First I use true FCF not reported earnings. Second, true FCF is too erratic year to year so I use three-year revenue CAGR as a more stable growth proxy.

So I came up with FEG: price-to-true-FCF divided by three-year revenue CAGR. But FEG still doesn't tell you when you get paid. A P/E of 10 is intuitive - you get your money back in ten years. True FCF yield is even better because you compare directly to the risk-free rate. My portfolio yields 9.9% in true FCF against a 5% treasury, which makes me happy (even happier when people talk about potential bond crises and such).

In thinking about growth: NVDA (a stock I wouldn't consider) sits at 1.65% true FCF yield today. If it keeps doubling, in four years it reaches 8%. For that moat quality, maybe you would wait four years. I wanted a way to make that calculation concrete across every name.

Y220: Years to 20% true FCF yield, compounding at current three-year revenue CAGR applied to true FCF.

Why 20%, because CMCSA sits there right now and I own some CMCSA. That's my Godfather number, the offer [yield] I can't refuse.

What the screen shows:

NVDA reaches 20% in 3.6 years if growth holds. This is tempting until you remember it's a $5 trillion company. Compounding at that rate off that base is a different bet than it was at $500 billion.

LLY is the most interesting name that fails my yield test but passes Y220. Revenue has gone parabolic and they're retiring shares aggressively. GLP-1 is early innings. The question is durability at this scale. Not a position but I watch it closely.

LYFT: I took a small starter position based on this screen. Revenue growth trajectory combined with aggressive buyback produces a Y220 that got my attention.

FDS vs. SPGI vs. ROP: I've done the direct comparison before and FDS won on organic growth and share retirement. But Y220 surfaces SPGI and ROP as legitimate quality alternatives if FDS's thesis weakens or its valuation compresses.

HCI: flattered by no major Florida hurricanes. Normalize the yield downward before trusting the Y220 number.

BRK.B: $334B in cash drags the screen. That cash is part of the point, but it makes the screener number worse than the investment case actually is.

The $50B+ scatter plot is the most useful visualization. NVDA is the outlier. Everything else clusters normally. LLY, APP, UBER, BSX, and BKNG all fail the yield test but pass Y220 with varying degrees of revenue growth durability.

Important disclaimer: these metrics are like alcohol. Use them responsibly! PEG says NVDA grows 145% per year — it's already making $159B TTM. That base gets harder. True FCF at 20% for CMCSA is great today but won't be true forever. Tools for thinking. Not verdicts.

Part II coming on smaller cap names where the alcohol warning applies double.

Full piece with scatter plots, trendlines, and the full screener tables: https://cavemanscreener.substack.com/p/my-new-godfather-metric-how-long

Peter Lynch's core idea is that ordinary investors have real informational advantages that Wall Street misses. You know which restaurant is packed, which software your team actually uses, which product line is quietly growing.

I wanted to apply that framework to three companies I've actually worked at. Fair warning: the most recent was Nike ending in 2022, so this isn't inside information. It's personal observations combined with the true FCF screener I run on 16 years of SEC XBRL data.

The valuations to start: LRN at 11.8% true FCF yield, ROP at 6.2%, NKE at 4.1%. LRN and ROP pass my basic screen of paying more than the risk-free rate while NKE does not.

Stride (LRN): I never actually worked for Stride directly. I worked for KC Distance Learning, which operated IQ Academies and was eventually acquired into the K12 universe. The 11.8% true FCF yield looks compelling and the float is shrinking aggressively. But there are public reports from at least one district alleging ghost students on enrollment rolls, the company disputes it, the CEO departure looked abrupt rather than planned, and I have no firsthand knowledge of the allegation but also can't model around it. The incentive structure in online charter education creates real pressure toward aggressive enrollment tactics - per-pupil public funding that follows enrollment in a very compressed enrollment season. The moat is real. The incumbent position is genuine. Conditional buy if the governance questions get answered.

Roper Technologies (ROP): I worked supporting DAT Solutions, their freight brokerage software, in 2012-2013. Genuinely results-driven data culture. Weekly business reviews where every team presented comprehensive data on their world. The most important analytical observation from my time there was watching what happened when Roper acquired a smaller competitor. The decline was exponential. Fewer loads meant fewer truckers checking the platform, which meant even fewer loads, which accelerated the exit of truckers. Two-sided marketplace collapse is nonlinear because both sides reinforce each other. That same dynamic protects DAT on the upside. More loads attract more truckers which attracts more loads. At 6.2% true FCF yield with a share count starting to decline, this is a buy. The only reason I don't own it is I already own FDS with similar metrics and what I think is a better AI monetization path.

Nike (NKE): Worked there twice, 2016 and 2018-2022. Smart energetic people who genuinely cared. The talent and culture are not the problem. The 4% true FCF yield with declining revenue and FCF since 2022-23 doesn't pass my screen, and the market seems to be counting on a Jordan-era recovery that I'm skeptical is coming. My theory: the Jordan era was a genuine cultural monoculture moment requiring a kind of gravity that's rarer in a fragmented subreddit world. Before Jordan, Nike's real moat was running. I ran in Zoom Flys during COVID and loved them. Then switched to Asics Superblasts and Hoka Skyflows. The other brands caught up in a space Nike was supposed to own. Nike is a good company being right-sized for a more fragmented era.

Full piece with annual and quarterly charts for all three: https://cavemanscreener.substack.com/p/invest-in-what-you-know-part-i-some

Two separate things are happening in the market simultaneously and most analysis treats them as separately.

The Macro angle: Warsh is keeping rates flat while NGDP runs hot. Longer rates are already backing up, and that sequence historically ends one of four ways and none of them are comfortable for today's equity multiples.

The Valuation angle: the S&P 500 earnings yield is 3.48% against a 4.59% 10-year treasury, negative by 1.11%. But that earnings yield has been inflated by companies reporting GAAP income while piling CapEx and SBC into the denominator. Once you use True FCF yield instead of earnings yield, the gap opens to negative 2.51%.

That -2.51% figure is not unprecedented. It shows up in the scatter plot in 1987, 1992, 2000, and in the 2008 aftermath when earnings collapsed. In every case something big happened to resolve the gap, either earnings rose dramatically or prices fell.

The chart that makes this concrete: there's a stability band between 5-7% treasury yields where equity risk premiums get thin and multiples get rich because monetary policy is calm. Below 4% you get post-crisis fear and big equity risk premiums. Above 8% you get inflation fear and big equity risk premiums. The 5-7% band is where nobody is scared and "everything is awesome".

We're at 4.6-4.7%. Just below the lower edge of the stability band. Close enough that today's thin risk premiums are defensible historically, but not so close that I'm comfortable buying the index.

How I'm positioned to survive the four scenarios I think are plausible:

• USFR 18.79%: explicit optionality for tail scenarios. Converts to equity in one trade.
• CMCSA 14.28%: 17.5% True FCF yield on a spinoff that closes the conglomerate discount regardless of what Warsh does.
• CB 9.40%: insurance float compounder that owns short-duration bonds. Goes up in the scenarios where everything else goes down.
• ADBE 9.39%: beaten-down SaaS dying of a theory. Cash flows still marching on.
• BRK.B 9.30%: $334B in cash waiting for the moment the risk premium gap resolves.
• THC 8.72%: USPI ambulatory surgery platform trading at 40% discount to standalone value. Healthcare demand doesn't depend on Warsh getting it right.
• EPD 8.51%: PPI-indexed midstream infrastructure with fixed long-dated debt. Cleanest inflation hedge in the portfolio.
• FRFHF 7.08%: Fairfax Financial, float compounder with opportunistic capital allocation on top. Trading at 1.3x book while growing book value 20%+ annually. I had HCI as a starter position but liked FRFHF's valuation more. HCI is still on my watch list.
• FDS 6.71%: four consecutive quarters of ASV acceleration while being sold as an AI casualty.
• EOG 4.45%: direct Hormuz risk premium bet, sized small because slowing economy cuts demand at the same time supply shock pushes price up.
• CF 3.34%: nitrogen producer, newest and smallest position, sized like I'm not totally sure yet.

Weighted average True FCF yield: 8%. Risk premium over the 4.6% 10-year: plus 3.4%.

Full piece with scatter plots, the True FCF divergence chart, and the complete allocation table: https://cavemanscreener.substack.com/p/macro-vs-free-cash-flow-yield-a-thesis

I've been reading Poor Charlie's Almanack while watching Reddit freak out (and then rejoice) about markets. Meanwhile my holdings (ADBE, FDS, CMCSA, THC, BRK-B, CB) have been doing well.

Of course we know that Berkshire Hathaway is named for a textile company Buffett bought and came to regret. When someone asked whether to invest in a new loom to drive down costs, Munger said that it was a terrible idea. The benefits wouldn't go to the investor, but rather to the consumers.

Buffett made the same point about airlines. What could be more wonderful than flying through the air on a whim? But add up all airline profits over their entire history and you get bupkis. Transformative technology and good investments are different things.

The data that makes this concrete for AI:

Microsoft fiscal 2024: true FCF fell 8.5% from $59.6B to $54.6B while revenue grew 18%. CapEx grew 79.6%. Operating cash flow grew 34.4%. Infrastructure spend is outrunning cash generation and there isn't enough AI monetization catching up.

The revenue gap: covering current CapEx plans through AI-specific revenue would require roughly $2.5T per year in AI income. More than all of tech's combined revenue today. Actual current AI services revenue triangulated across multiple independent methodologies: roughly $150-220B annualized. About 6-9% of what's needed.

The depth-of-use problem: only 10% of euro-area firms using AI report doing so intensively per the ECB. Average US executive uses AI 1.7 hours per week. 50% of UK businesses using AI pay nothing for it. Median firm AI spend per worker: $10.66 per month.

The sharpest data point: token consumption is growing faster than revenue, meaning a meaningful share of usage is free-tier switching not monetized growth. A BIS economist estimates roughly a third of current AI CapEx may represent zero-sum competitive spending, companies poaching each other's users rather than expanding the market.

True FCF yield by layer tells the whole story visually: compute leasers at -2.8% to -19.4%, hyperscalers at -0.5% to 1.9%, beaten-down SaaS at 5.8% to 8.0%. The companies the market thinks are being killed by AI are generating more cash per dollar than the companies building it.

Full piece with the flow chart, the Google advertising exception, and the Munger conclusion here: https://cavemanscreener.substack.com/p/bridges-to-nowhere-part-iv-a-lesson

Disclosure: I own ADBE, FDS, CMCSA, THC, BRK-B, CB and a small starter position in ASML. The ASML position is a price-aware hedge against being wrong about who wins, not a reversal of the thesis.

Microsoft and Amazon interrupted the rotation trade that had been gaining steam, but the growth that they're seeing in on the cloud/infrastructure side.

I worry a lot. Too much probably. Whenever I think of Buffett saying an investor needs a world-class temperament over a world-class intellect, I even worry about that.

For years I didn't worry much because I was in index funds. But the inelastic market hypothesis and Liberation Day tariffs pushed me back to Graham, and now I have a portfolio of individual picks with macro worries layered on top of company-specific worries.

My macro framework mostly comes from Scott Sumner. Here's why I trust him specifically:

2008: He correctly saw that paying interest on reserves while not lowering rates fast enough was a massive tightening when everyone else was watching the headline rate. He was right and almost nobody else was.

Obama years: While prognosticators called for hyperinflation from QE, he correctly called continued stagnation and low rates. Right again.

Post-COVID: While everyone said rising rates meant tight policy, he kept asking where the actual tightening was showing up in NGDP. Right again.

When I run that framework against the current environment I get three scenarios:

Scenario 1 (15%): Warsh fights commodity inflation while NGDP collapses. There's $1.4T in margin debt, $220B in leveraged ETFs, and the inelastic market hypothesis inflating the top market caps. If that leverage unwinds at the same time Warsh is tightening to fight oil prices, the financial plumbing breaks. Lower rates and lower inflation at the cost of a financial crisis. The danger is nobody identifies the Fed as the culprit in real time, just like 2008.

Scenario 2 (60%): There's a lot of ruin in a nation. The AI trade corrects without systemic crisis. Value rotates up. Index funds lose a little. My beaten-down SaaS, healthcare, and insurance positions quietly rerate. This is what rotation days look like, and they're becoming more frequent.

Scenario 3 (25%): Warsh does Trump's bidding the way Arthur Burns did Nixon's. Inflation stays above target and the Fed chases it with ever-increasing rates without catching it. Government spending keeps running while the adults have left the room. Historically that combination produces inflation not deflation.

The normal distribution for these three scenarios is 5/90/5. I'm putting it at 15/60/25 right now because of the Iran War, Warsh's apparent skepticism of expectation management, the Trump administration's unpredictability, and the genuine absence of any coherent alternative fiscal framework from either party.

What I do with this: when monetary policy seems to be tracking 4-5% NGDP growth, I stay in value stocks. When the tails widen, I add to short-term treasuries. I'm at 20% USFR right now. Monday July 27th was a perfect rotation day, up 2% while QQQ was down 1%, and I took a small amount off the table and added to USFR.

Full piece: https://cavemanscreener.substack.com/p/different-shocks-different-strategies

I sold my index fund because I couldn't unsee the inelastic market hypothesis: passive flows piling into cap-weighted mega-caps regardless of price, just because that's where the next 401(k) dollar is mechanically required to go. I still believe it. I'd also be lying if I said it hasn't cost me. The index kept climbing without me, and the AI names inside it did a lot of the work. Being wrong and being early look identical for a while.

So I finally priced the thing I'd been avoiding instead of just believing my own priors. I mapped all 8 layers of the AI trade, hyperscalers, chip designers, TSMC, memory, ASML, equipment, materials, and beaten-down SaaS, and ran true FCF yield (OCF - CapEx - SBC / market cap) across roughly 30 tickers.

What I found surprised me. Adobe generates an 8.59% true FCF yield. Salesforce 7.76%. FactSet 6.10%. Nvidia, the name everyone thinks of as "the AI winner," generates 1.82%. The cash machines got sold and the dream got bought.

The section I'd most welcome pushback on: I think the "AI kills SaaS" thesis is priced in far more aggressively than the actual disruption risk justifies for names sitting on genuinely proprietary data (Adobe's 800M user behavioral dataset, FactSet's 30 years of financial model data). I don't think that's true of every SaaS name that got sold off in the same basket, just the ones with real data moats versus commodity workflow tools.

Full breakdown with the diagrams and true FCF tables: https://cavemanscreener.substack.com/p/early-isnt-wrong-pricing-the-ai-trade

The two analytical questions I'd most welcome pushback on from this community:

First: how durable is the 2022-2023 Florida tort reform improvement? The loss ratio improvement from 55% to 29% is partly structural and partly a reserve release tailwind. My rough math strips about 6 points off the headline 18% yield to get to a normalized 12%. If tort reform gets relitigated or reversed that number compresses further. Anyone closer to Florida insurance litigation trends has better visibility on this than I do.

Second: the reinsurance structure. I pulled the actual June 2026 catastrophe reinsurance filing rather than trusting summaries. $4.06 billion total coverage, $162.6 million maximum first-event retention, Florida Hurricane Catastrophe Fund participation. The retention is up 4% year over year, not down, even as total coverage expanded. I'd want someone who reads reinsurance programs regularly to tell me if that 4% increase in first-loss retention is routine or a signal that reinsurers are starting to price more risk back to the cedent.

The Citizens depopulation pipeline is the most structurally interesting part of the thesis and the least discussed. The "20% rule" is literal Florida statute, not an informal program. 60,820 policies and $216.7 million in annualized premium assumed from Citizens in 2025 alone. HCI built its own quoting and risk-mapping software specifically to cherry-pick from this pool. That's not passive participation in a government program. That's active arbitrage of a government program by the company with better pricing technology.

One detail that surprised me in the 10-K: only $14 million in dividends flowed from insurance subsidiaries to the parent in 2025 against $430 million in consolidated true FCF. Florida statutory dividend caps mean a meaningful portion of that FCF sits inside regulated subsidiaries. The current buyback program is partly funded by the Exzeo IPO proceeds, a one-time capital event. That's not a dealbreaker but it's a different cash flow profile than the headline number implies.

Full piece with historical data back to 2011: https://cavemanscreener.substack.com/p/hci-rock-you-like-a-hurricane

Buffett's framework for lifecycle investing applies here in a specific way. His best investments have often been businesses in transition from high-growth to mature compounder phases, where the market continues pricing them as if the high-growth rate is the right baseline rather than the mature rate.

Coca-Cola wasn't growing at 20% per year when Buffett bought it in 1988. It was a mature compounder returning cash aggressively. He paid 15x earnings for a business the market was treating like it had peaked. It hadn't peaked, but rather it had matured.

Adobe went from 20% annual revenue growth to 10%. The market treated that deceleration as existential. The actual numbers: $8.25 billion in true FCF on a business with 10% topline growth, 9.3% true FCF yield, deferred revenue accelerating in the historically weakest quarter, enterprise contracts extending 30% longer on average, and $25 billion in authorized buybacks running through 2030.

The specific Buffett parallel: this is the pricing power and switching cost moat he looks for expressed in software rather than consumer brands. Adobe's PSD files don't open natively anywhere else. Enterprise compliance workflows can't be ripped out mid-year. Legal indemnification for commercial creative work is something Midjourney can't offer.

Link: https://cavemanscreener.substack.com/p/died-of-a-theory-adobe-saas-and-ai

Background: I've spent twenty years doing data ontology work professionally — building the semantic structures that turn raw, ungoverned data into something usable, most recently at SurveyMonkey. On the side I've built a personal screener pulling 16 years of SEC XBRL data across roughly 1,700 tickers, normalizing inconsistent tags so true FCF (operating cash flow minus CapEx minus SBC) is comparable across companies. I'm posting this here specifically because I think the methodology question is more interesting than the stock picks, and this sub seems like the right place to have that argued with rather than just agreed with.

The consensus trade and why I think it's incomplete

Everyone agrees the AI infrastructure trade is the data platform layer — Snowflake, Databricks, Amplitude. Raw data storage, query, and governance tooling. The market has priced this consensus in fully; these names carry premium multiples on the "picks and shovels" thesis.

My argument: raw data infrastructure is closer to a commodity than people are pricing it as. SQL servers, data warehouses, analytics capture platforms — this category has been re-invented every decade with marginal differentiation, and the switching costs, while real, are mostly operational (migration pain) rather than epistemic (the new platform can do everything the old one could, eventually). What's scarce isn't the pipe. It's validated, structured, domain-specific content moving through the pipe.

The taxonomy I'm using

I split AI-relevant data companies into four categories:

Foundational language data — Reddit (RDDT) is the only name here. Granular subreddit classification plus upvote-based quality signal is genuinely unique training corpus for natural, idiomatic language. I don't own it — FCF yield too low for my framework, still in a cash-consuming growth phase — but the data moat argument is real.

Industry-specific contextual data — FactSet (FDS), Veeva (VEEV), Roper (ROP), S&P Global (SPGI). These companies have spent decades organizing messy, heavily regulated domain data into clean, structured ontologies: financial workflows, FDA-validated clinical trial records, county tax administration, credit ratings methodology. None of this is scrapeable. A general model trained on public web data has zero exposure to what a structured clinical trial submission or a properly normalized financial model actually looks like internally.

Workflow/usage data — Adobe (ADBE), Salesforce (CRM), SS&C (SSNC). The moat here is encoded human process rather than raw content. A Salesforce lead-to-contact-to-opportunity data model isn't bad design — it's encoding a specific sales workflow that took years to standardize across millions of companies. Replacing it means replicating not just the data but the process logic embedded in how that data gets created and transformed.

Data foundation platforms — Amplitude (AMPL), Snowflake (SNOW). The commodity layer described above.

The valuation argument

The names in categories 2 and 3 are trading at meaningfully better true FCF yields than the consensus infrastructure plays, despite (in my view) deeper and more durable moats — partly because the SaaSpocalypse selloff has lumped them in indiscriminately with software companies that genuinely do have weak, scrapeable moats. I think the market is pricing the wrong layer of the stack.

The honest open question I'd actually like pushback on

Is "irreplaceable context" really a durable moat, or just a temporary information asymmetry that AI labs close over time as they get better at synthetic data generation, data partnerships, or simply paying for licensing access to exactly this kind of structured content? If OpenAI or Anthropic can license FactSet's data outright, or if regulatory data eventually becomes more standardized and shareable industry-wide (think FDA pushing toward common data standards), does the moat compress faster than the multiple suggests it will? I think the moat holds longer than the market is currently pricing, but I'm genuinely less certain about the 10-year case than the 3-year case, and would like to hear from anyone closer to enterprise AI procurement or regulatory data standards on how real this risk is.

Full piece with the four-category breakdown and a true FCF yield comparison table is here, for anyone who wants the data: https://cavemanscreener.substack.com/p/context-is-50-iq-points-part-ii-data

Disclosure: I own FDS and ADBE.

Last week I ran a bear case DCF on Comcast using normalized FCF of $16B, assumed 3% annual decline for 12 straight years, 10% discount rate, and explicitly stripped $89B in net debt from the terminal value.

Bear case fair value: $30-38 per share against a $22 price.

At the end of the piece I wrote a "dream scenario" section:

"A company that spun off Versant doesn't seem unlikely to eventually spin off other pieces - broadband infrastructure or Universal Studios as a standalone entity. According to my sum-of-parts analysis, a spinoff scenario could put CMCSA at $55-75 per share."

This morning Comcast announced exactly that. NBCUniversal and Sky spinning off into a separate publicly traded company. Broadband, wireless, and business services staying in the remaining Comcast. Stock up 20% in premarket.

The broadband rump - which produced 24x the adjusted EBITDA of the content business in Q1 2026 - is now going to trade as a pure-play infrastructure company. The content business gets its own multiple separately.

Even after the 20% move the stock is still below my bear case fair value of $30-38. The spinoff still needs regulatory approval and closes in about a year. The story isn't over.

Full DCF with the math, the debt analysis, and the buyback cannibalization model here: https://cavemanscreener.substack.com/p/buying-2-for-1-a-comcast-dcf-update

A cannibal is a company retiring its own shares aggressively - but not the fake kind where buybacks just offset employee SBC dilution. NET buybacks. The kind where the share count actually shrinks.

The math is interesting: if true FCF is growing while shares shrink, the yield available to you as a shareholder is compounding twice. And here's the psychological twist — if the stock drops during the buyback, that's actually good news. Lower price means more shares retired per dollar, which means higher ownership stake. You almost get to root for the price to drop.

My filters: buyback yield of at least 5% (net share reduction) plus true FCF yield of at least 8% (OCF minus CapEx minus SBC). Here's what came out of my screener.

The standouts: ADBE, CMCSA, DBX, PYPL, DVA, BCO. Profit margin as a moat proxy puts ADBE, CMCSA, FISV and GPN at the top.

The Adobe section of the piece is what I'm most interested in hearing pushback on. The Reddit bear case I link argues freemium is a warning sign. My counter: Adobe is attracting 800 million users and generating creative workflow behavioral data at a scale that Midjourney and DALL-E simply don't have. As a data ontologist by trade, that context corpus looks more like a moat than a threat. Very few people question whether Anthropic's freemium model creates value. Why is Adobe different?

Full piece with jaws of life charts: https://cavemanscreener.substack.com/p/the-jaws-of-life-finding-stocks-that

Short piece I wrote before heading on vacation. More philosophical than usual but I think it's worth discussing.

The core argument: value investing is primarily a battle against your own psychology, not a battle to find the right numbers. Every cheap stock you buy will probably drop first. Momentum is real and it works against you in the short term. Buffett's edge wasn't intellect — it was temperament.

On CMCSA specifically: I ran a segment-by-segment 15-year DCF. Assumptions are deliberately bearish — connectivity FCF shrinks 4%/yr, Peacock reaches only modest profitability, zero terminal value. Parks grow at ~3%/yr post-Epic Universe.

Result: $158B in present value cash flows against an $89B market cap.

The bear case requires believing the market is right that CMCSA is worth less than 6 years of its own normalized FCF. The DCF says that's too pessimistic even in a slow-death scenario.

Full piece: https://cavemanscreener.substack.com/p/the-sad-life-of-a-value-investor

Built a screen using 15 years of SEC XBRL data. Filters: True FCF yield (OCF minus CapEx minus SBC) above 10% for the entire history of the stock, at least 10 years of data, P/B between 0.1 and 10, no financials, real CapEx present.

Got a list with some garbage and some names worth looking at. Added Sirius, Ford and Mattel manually since I wanted them in the comparison.

The interesting ones: CMCSA, HOG, TAP. Here's what I found on each:

Comcast: 20%+ True FCF yield. Revenue correlation to US nominal GDP is 96% over the last 5 years. It wasn't always this way, the empire-building era (2012-2017) kept correlation negative. Now it's one of the most NGDP-integrated businesses I've found. The bear case (fiber competition, Peacock losses, debt costs) is real and so is the yield.

Harley-Davidson: 9.7% of float retired in a single year and 13.4% total shareholder yield. 0.79x tangible book with almost no goodwill - the manufacturing business is essentially debt-free. While the brand is aging, the capital allocation seems exceptional.

Molson Coors: Trading below book. In 17 years FCF never went negative. 10.2% total shareholder yield. Is it safe? Well, it's beer.

Also added a rolling 5-year NGDP correlation layer which shows Shutterstock losing its cash generation ability in real time (FCF went negative in 2024 while revenue kept growing.

Methodology in the piece. Happy to share the data: https://cavemanscreener.substack.com/p/lookin-for-value-in-all-the-wrong

The Starlink business is genuinely excellent — $11.4B revenue, 63% EBITDA margin, 50% growth. If you could buy Starlink as a standalone utility it would be one of the best businesses on the planet.

But you can't. Here's what you're actually buying:

→ xAI lost $6.36B on its own in 2025 → xAI's $16B in debt was refinanced onto SpaceX's balance sheet via a $20B bridge loan in March. That debt is now SpaceX's — and soon to be public investors' → The entire xAI revenue bull case is one customer: Anthropic, funded by Google and Amazon — xAI's direct competitors → Each Class B share carries 10 votes. Musk controls all shareholder outcomes. SpaceX is explicitly listing as a "controlled company" exempt from Nasdaq governance requirements → The fast-track index inclusion rule means passive funds are legally forced to buy within 15 days of listing at whatever price it's trading

Less than 7% of the $1.75T valuation is backed by current profit.

60% of mutual fund and ETF assets are now passively managed. An extra $1 of active investment generates $5 of passive buying. That's how you get to $1.75T without anyone deciding it's worth that — the machine decides.

Full piece: https://cavemanscreener.substack.com/p/the-175-trillion-trojan-horse-how

With interest payments now equaling defense spending, I wanted to find businesses that structurally benefit from inflation rather than just survive it.

The template is Enterprise Products Partners (EPD) with PPI-indexed revenues and fixed-rate debt under 5%. In an inflationary environment their upside reprices while their cost of debt stays fixed.

I ran the same screen across dividend aristocrats: revenue correlation to CPI over 16 years of SEC data:

> Realty Income (O): 92.7% - CPI-linked lease escalators baked into contracts

> American Express (AXP): 81.4% CPI + 52% NGDP - rides both inflation and real growth

> ExxonMobil: 79.6% - energy is the CPI basket

> Republic Services: 77.8% - waste hauling contracts directly CPI-indexed

> Chevron: 72.3%

The mechanism is the same for all of them: revenues reprice with inflation whereas debt doesn't.

AXP is the most interesting with a 7.25% true FCF yield, a huge Buffett position, and it automatically clips a percentage of every nominal transaction in the economy.

Full screen with true FCF yields and 10-year averages: https://cavemanscreener.substack.com/p/surfin-inflation-finding-the-businesses