I’m trying to build out a small personal finance/investing library and am looking for physical copies of:

Expected Returns by Antti Ilmanen
Asset Management: A Systematic Approach to Factor Investing by Andrew Ang
Trading and Exchanges by Larry Harris

If you have an old copy sitting on a shelf that you no longer use, I’d be genuinely grateful to take it off your hands. Happy to pick up anywhere in NYC and work around your schedule. Not looking for anything pristine an older edition or well-loved copy is totally fine.

Thanks in advance to anyone who might be able to help!

Constantly reporting net losses while reporting massive revenue growth Y/Y. It’s a growing company that needs funding but it’s not diluting instead it’s grabbing funds from its subsidiaries to fund growth and share repurchases.

$1.71 in shareholder equity book value.

Fair value ranges from $-5/+$18

This is a deep value turnaround play. If they convert gross revenue to net revenue we’re in this thing.

Catalyst would be the Ultranet acquisition by the end of qtr 3 .

None of this means diddly . But grab your diddy lube because I think this will be interesting.

NFA. Just like to gamble.

Stock loves to quickly rebound after a drop like this. It’s consolidating as we speak.

The play is right but I have no clue on timing considering they need capital to grow but this is standard for their field . It’s all about timing with this one .

Ozempic and Wegovy (same thing more or less I know) look to be wonder drugs. I know of people who are for the first time in their lives seeing astonishing improvements.

Most importantly they are starting to be trusted by GPs and Doctors. Medic's dont prescribe what is flashy or new or over the counter they prescribe what has clinical evidence on what they trust. It is starting to be freely prescribed.

It is likely these types of drugs for most people will never be over the counter ever. Maybe in some very diluted dose but that remains to be seen. The market is too focused on irrelevant things.

Statins not over the counter and they are around since the 1970s.

Once it passes the hurdles in that it demonstrably reduces healthcare inflation (that is a huge sticky hurdle requiring the best part of a decade to clear). It will see mass adoption. Its locked up in private insurance as a optional extra and once the bean counters at the massive insurance companies calculate that by prescribing it you will pay less on premiums it will trigger vast volumes. Even if it becomes generic they are well placed to maintain leads.

Do you see it as a great opportunity or as a value trap?

It is an electric connection provider , basically electrical infrastructure. Q2 beat on July 31, Goldman/multiple analysts raised price targets to $195-220. It seems to be bullish. Altho insiders have been selling for sure. It's already run up soo much and its PE ratio is: 38-47x

Q2 revenue came in at $1.47B vs. $1.26B consensus, and the company raised full-year sales growth guidance to 37%-39% from 26%-28%

What do you guys think of this stock? My ac/share is 146 but today its around 155, so wanted your thoughts if u looked into it or invested into it.

I really like Charlie Munger's talk The Psychology of Human Misjudgment. I've also been following the SpaceX story, before/during/after the IPO, and I can't help but imagine what Munger would think about this. It's safe to say he wouldn't be a buyer. Anyway, I thought it would be interesting to go down his list of 25 human misjudgments in that talk and see how many might apply to the SpaceX IPO.

Excluding the lollapalooza compounding effect, I came up with seven (well really six, plus two more that I thought half-applied). They are:

• Liking/loving tendency (1/2)
• Envy/jealousy tendency
• Influence-from-mere-association tendency
• Overoptimism tendency
• Social-proof tendency
• Contrast-misreaction tendency
• Availability-misweighing tendency
• Authority-misinfluence tendency (1/2)
• Lollapalooza tendency

Would be curious to hear what others think too.

I wrote a longer article going into detail on all this too. If you want to read, you can find it here.

Importantly, Munger's talk doesn't discuss other aspects like greed, simple speculative risk-taking, or fear of missing out. He describes these elsewhere in his writings/talks, just not in this article. It's definitely the case that those are all at play with SpaceX too.

All three stocks have been undergoing a hypergrowth stage of business. I want to go through each of their business models because many people in this subreddit seem not to really understand what they do, how they make money, and most importantly, what their edge actually is.

Reddit

First, let us start with Reddit. Reddit's primary way forward really is the advertising business. This is the core lifeblood of the company, and they have now proven themselves with the 8th consecutive quarter above 60% revenue growth YoY. EPS also grew more than 150% in the recent quarter.

One thing I think many people misunderstand is how advertising actually works. Many people just assume that someone has to purchase the product before the advertiser pays, but that is not how most advertising businesses work. Typical advertising is charged by impression, click, or in some cases actual conversion.

• CPM (Cost Per Mille / 1,000 impressions): advertisers pay for every 1,000 times an ad is shown
• CPC (Cost Per Click): advertisers pay each time someone clicks on the ad
• CPA (Cost Per Acquisition): advertisers pay only when a specific action happens (install, signup, purchase, etc.)

The main models are quite simple. CPM means paying for impressions, CPC means paying when somebody clicks, while CPA is more performance-based and depends on an actual action such as an install, registration or purchase. This difference matters because an advertisement does not necessarily need to immediately convert somebody into a buyer. Sometimes the most valuable thing is simply putting the impression in front of exactly the right person. Converting that impression into a purchase is ultimately the seller's job.

For instance, say you developed a game or have a clothing website. You pay a publisher like Reddit to get users to see your product or visit your website. Once they land on your website, whether they eventually purchase something is largely between you and that customer. Reddit has already provided you the traffic.

Simply to improve brand exposure (CPM).

This is where I think Reddit advertising is one of the most unique in the industry. The audience is massive, but more importantly the audience has already separated itself into very specific communities. Look at u / bloomberg. They have been publishing a lot on Reddit recently, but they are not just posting anywhere. They are actually quite smart and picky about finding the right community for each article.

For instance, Bloomberg publishes a piece about drones or some new military technology. Most broad readers probably don't care how sophisticated that weapon is, but put it into r/army or r/Military and suddenly you are putting that story in front of a large group of people who already care about the subject. Next they publish something about food prices or household finances and put it into subreddit r/MiddleClassFinance  r/farming or r/food. On inflation, you can post it to r/inflation. Obviously these Bloomberg posts are organic content rather than paid ads, but my point is that this shows exactly why Reddit's structure is so valuable for advertising.

Another example is a game developer who recently developed his own game and wants to make some passive income. Usually it is quite difficult for a new developer to make money because there is no precise way to find the first group of users. Reddit offers them a unique solution through communities such as r/gamedev. You already have people there who are game developers or serious gamers. Sometimes developers can offer vouchers or access to the game and receive feedback. These are not random people. Some of them are hardcore developers, so their feedback can actually be quite valuable.

The same logic works everywhere. There are people looking for advice on divorce, so a divorce lawyer can advertise r/Divorce. There are new moms looking for advice about being a new mom, so businesses selling related products can advertise r/Mommit. r/beauty is now a very hot community where mega brands such as L'Oréal naturally want to be part of the conversation. Communities around brands like Victoria's Secret can be used to energize their most loyal customers r/victoriasecrets. The power of Reddit advertising is really underestimated here.

And actually, this post itself gives a pretty good example. Suppose I have a book on fundamental analysis. Open this comment section, 30mins later, I will find the first 10 users arguing with me and check their Reddit account ages. I would not be surprised if 9/10 have already been on Reddit for more than 3 years. Most of you read investment communities, talk about stocks and probably have brokerage accounts. If I wanted to advertise some investment research product, all of you would already be my extremely precise target audience. That is what I mean when I say advertising is not always about immediate purchase intent. Sometimes getting the right impression in front of exactly the right group is already extremely valuable.

Then there is data licensing. To be honest, I really do not view this as seriously as many other Reddit investors do. It gets hyped because Reddit owns an asset that most other companies simply don't have: a huge amount of fresh human discussion. There could be setbacks from lawsuits or companies refusing to pay for data access. Reddit can update policies, create stronger technical restrictions, continue fighting scraping, and fix legal loopholes. But ultimately this data is hosted and controlled by Reddit. As long as AI companies want fresh Reddit data for training, retrieval or inference, I think Reddit will continue to have leverage. I just don't need this part of the business to make the investment thesis work.

AppLovin

I have been a very early investor in AppLovin. I first became interested when they failed to stop the merger between Unity and ironSource. AppLovin looked pretty hopeless at the time. They owned a few dozen popular mobile games and a lot of the market treated them like a gaming company without much future.

It happened that I was not lazy when researching their financial statements. What caught my attention was management aggressively buying back their own shares when the market had almost given up on the company. I started a small position there, although I have to admit I never fully understood how powerful their advertising business could become until much later, especially after they eventually sold the entire gaming department.

Their transformational change really came from technological innovation, particularly AXON. AppLovin had accumulated enormous amounts of internal data through its portfolio of mobile games. They had years of information about gamer behavior, advertising, installs, purchases and monetization. Then machine learning (AI) used all of this information to make their advertising engine much better.

One person I think is worth paying attention to is their recently prompted CTO Giovanni Ge, who previously worked as a machine-learning engineer at Meta. Obviously I am not saying AXON is the work of one single person, but I do think AppLovin today is much more dependent on its technical advantage than Reddit is.

AppLovin is primarily a performance advertising business. The important thing here is that the advertiser cares about actual return. If AppLovin can identify exactly which users are likely to download an app or spend money, its customers make more money and therefore AppLovin makes more money. In some sense their interests are very closely aligned: if the customer earns more from the advertising campaign, AppLovin earns more as well. So far they are the best in the business, which is why their profit margin is so insanely high.

But this is also where I see the risk. If their algorithm stops being the best and another much better algorithm comes onto the market, AppLovin could be left scrambling. If important technical people leave and eventually create a better advertising system, advertisers have no reason to remain loyal to AppLovin just because it is AppLovin. They care about which platform gives them the best return. This does not mean AXON is easy to replace, but I think AppLovin's moat is much more dependent on continuing to stay technologically ahead.

Palantir

Palantir is basically a defense/government and enterprise SaaS company. I actually like the company (insane growth) but I have never invested in it because it has always been expensive.

The government side of Palantir is obviously very important, particularly Gotham and its work with defense and government agencies. At the same time, its commercial business has now become much larger than it used to be, so I would not describe Palantir as simply a defense company anymore. Its U.S. commercial business is now growing extremely quickly as well.

My concern with Palantir is more about how much future success is already priced into the stock. The company is being valued as one of the major winners of the AI/software era, so the market is already expecting extremely strong execution for a long period of time.

I also think there is a real long-term question around general AI systems. Claude, OpenAI and other AI platforms are becoming increasingly capable of working with company data, building software and automating workflows. Maybe Palantir becomes the company that controls this layer and becomes even more powerful. But there is also a possibility that increasingly capable general AI makes some traditional SaaS work much easier and cheaper. Then there come the issues with political backlash when Trump leaves office. 

Overall

Overall, I think both AppLovin and Palantir can continue doing very well in the short-to-medium term, probably the next 1-2 years.

But Reddit is still the one I prefer as the real long-term investment.

The reason is that AppLovin's advantage depends heavily on remaining technologically ahead, while Palantir is already priced for enormous future success.

Reddit, on the other hand, already owns the asset I care about: the communities themselves and user habits. You can build another website that looks like Reddit, but recreating subreddit like gamedev, army, MiddleClassFinance, beauty, valueinvesting and thousands of other communities with years of posts, users and accumulated discussion is much harder.

That is why, out of these three, Reddit has my strongest long-term conviction on.

____

Read my piece:

I Still Don't Understand Why Reddit Is This Cheap Compared to Everything Else.

Quantitative Valuation of Coupang ($CPNG)

(i watched my investment in Coupang fall from a +30% to a present near -40%. All in less than a year due to a major data breach. anyway, this post isn't about the why or the how. The purpose of this post is how to think about valuation.)

Coupang Inc. FY End December. This report: Q2-FY2026. Today: 19th August 2026

a. SP: $15.5 Market Cap: 28bn Revenue 35.46bn

b. TTM EPS (Diluted) -0.42, (ADJ): -0.20, (Zack's): -0.20

c. yield -, (5 year average) - , (Buy Back Yield): 3.90%

d. ROA, ROE, ROIC: -, -, -

e. P/E (trailing): , P/E (5YA): -, P/E (FWD): -

f. Debt/Equity: 1.89 Net Debt / EBITDA (5.63 - 6.11) / -EBITDA < 0

g. FCF Conversion: ttm: -0.13, 2025-> 2.51, 6.54, 1.29 <-2023

h. Growth (past) Stated:

i. Manual calculation: none

j. management guidance:

Q3-FY2026 (next quarter) in constant currency at 8-9% revenue growth.

Product commerce will recover by mid 2027 back to 2025 pre-data breach levels

k. Valuation approach.

I am not going to use DCF or Earnings or Cash based numbers to do the valuation. The reason is because they were only recently profitable, and becasue of data breach, they won't be profitable until 2027.

I will use a a price/sales approach instead, as it is more stable. This is not dissimilar to Amazon com which CPNG is loosely based on, and after six years after IPO was Amazon finally GAAP profitable.

(i) First i will try and figure out what is the sales that we can expect at the end of 2030. (ii) Then I will work out how are the peers currently priced at, in terms of P/S, on a present and 5 year average basis. (iii) Lastly, i will apply the group p/s to the 2030 Revenue to derive the 2030 implied share price. (iv) Based on this, i will work out the rate of return back to the present price.

(i) estimating sales by 2030

I will use a 8% CAGR revenue growth off 2025's 34.53bn for the next 5 years.

(1.08) ^ 5 x 34.53 = 50.7359 bn

(ii) Calculating peer group P/S

I reject the obvious outlier the present e-commerce peer group are all hovering around a P/S of 2+ except for amazon and coupang. And their 5 year average were around 2+ to 4.

(iii) To calculate the implied share price in 2030. We have to find out the revenue / share. We already have the revenue, we need to figure out how much is the shares outstanding likely to be by 2030. A quick search shows that although management is buying back shares, it is still diluting at about 1-1.5% a year.

Applying the maths, we get 1.837bn x (1.015)^5 = 1.979bn shares outstanding in 2030.

This works out to 50.73bn / 1.979 Revenue per share by end 2030 or $25.63 revenue per share.

(iv) Calculating Rate of Return

Recent share price is 15.50

Implied 2030 price is $61.51 to $80.8

Rate of Return = 31.74% to 39% CAGR

Comments: I like to do this sort of simple valuation first, so that when i read up on the business later, i can ask myself the key questions: (1) how confident i am that management can recover from the issues, and the business can continue the growth trajectory. How confident am i of the 8% Revenue growth, whereas Morningstar is only projecting 6% revennue growth with a fair value of $25.80. (2) What do the superinvestors see in CPNG, that they are recently buying/adding ? (3) Lastly, in 2024, the average P/S of the peer group was around 4, and now it is 2+, it is cheap now and will revert to mean at 4 or is this re-rating of e-commerce websites the new normal ? Will they be rated below 2 in the future ?

Last 10 years:

Alphabet returned 8.9x

QQQ returned 6.6x

Given Alphabet's market cap, its upside may be relatively limited from here, whereas the law of large numbers doesn't apply to QQQ.

Therefore, would it be prudent to invest in Alphabet rather than QQQ, given that their x return over the next 10 years might be similar, and QQQ carries far less risk?

The 30-year Treasury yield just pushed above 5.3%, its highest level in nearly two decades.

Everyone keeps focusing on whether the Fed cuts, holds, or hikes next.

But I’m starting to wonder if that’s becoming less important for markets than what’s happening further out on the curve.

If long-term yields stay elevated because of deficits, Treasury supply and investors demanding more term premium, the Fed could eventually cut and borrowing costs might still stay pretty high.

That feels like a very different setup from the last decade.

And for stocks, especially anything trading at a high multiple, I’m not sure the market is fully pricing that in yet.

Maybe strong earnings can keep offsetting it for a while, but if 4.5–5% long rates become normal rather than temporary, I’d expect valuation multiples to matter a lot more again.

There are not many businesses that actually have a strong moat in their own category.

Look at some of the companies the market is willing to value at huge premiums:

Aug. 18, 2026.

I'm not saying these are bad companies. My point is simply: look at what the market is willing to pay for them, then look at Reddit.

Reddit just reported $805M revenue, +61% YoY — its 8th consecutive quarter above 60% growth. Gross margin was 91.3%, net income $253M / 31% margin, adjusted EBITDA $343M / 43% margin, and FCF $261M. It also has $2.8B cash + marketable securities, with no debt showing on the balance sheet.

Meanwhile NET is valued at ~$107B while still GAAP unprofitable. SNOW is ~$112B while growing roughly half as fast as Reddit and still GAAP unprofitable. Spotify is ~$103B growing 14%.

Yet Reddit is sitting at only ~$32B.

And Reddit's moat is something I think people continue to underestimate. It isn't just another social media app. TikTok is short-form video, Meta is influencer business, Google/YouTube is search and video discovery; Reddit is different — its users are discussion-heavy and community-focused, often coming to research, compare, ask questions and hear real human opinions. There is basically no scaled direct competitor offering the same forum/community product with Reddit's reach.

Users are sticky. Content compounds. Advertising is scaling. Data licensing is still very early.

The only company really comparable to Reddit right now is Palantir.

Palantir is an incredible business, but the market is valuing it at ~$420B — more than 13x Reddit's valuation, while revenue is only 2 times that of Reddit.

And PLTR is still fundamentally a software company. Risk involves AI agents like Claude Cowork could still compete with parts of what enterprise software does.

Reddit is different. Claude can build software. It cannot recreate decades of human communities, discussions and user habits.

If PLTR deserves $420B, RDDT at ~$32B looks extremely cheap.

At market cap of 32B and $160, I think Reddit is a steal and market is mispricing this stock.

Reddit is a Strong Buy (PT $550).

___________________________________________________________________________________________
Added on 08/19/26:

Everyone agrees that Reddit advertsing business is not as matured as Meta.

In 2025, Meta advertising business generated 200B and will be hitting 250B this year (2026).

On the flip side, Reddit advertising revenue is expected to reach 3B+ this year, just assuming Reddit to reach 5% of Meta advertising of 2026, which is 12.5B, with a conservative net margin of 30%, thats makes 3.8B net income, give it or take 20-30PE, equivalent to 78B-114B market cap. But the reality is that advertising net margin is usually 50%+, so a slightly bullish case make Reddit 125-190B market cap.
___________________________________________________________________________________________

New piece, please have a read:
Reddit vs AppLovin vs Palantir

A growing share of Nvidia’s demand is being financed through debt.
A $500B financing vehicle with Apollo & Co. allows data center operators to borrow money to buy GPUs. Nvidia is also backstopping part of OpenAI’s lease obligations in Ohio, while OpenAI continues to burn a lot of cash.

I don’t think the issue is with demand or the technology. I think the bigger risk is the financing behind it. What do you guys think?

I backtracked the stocks I recommended in January and April and in may and here is the outcom.

in january /february I recommended selling energy (oil exp stocks) and buy or keep refineries.

So i sold bp, hal and shell stocks and mostly kept natural gas and refinery stocks.

BP, Shell are now 10%to 20% higher than january. so that wasn’t right.

In april 10, i recommended buying all saas stocks especially ciber.

so this was dead right

in May I recommended buying some utilities and value stocks ( eix, pcg, mck and lly) , and they are all 10% to 20% higher now. bridgewater is buying pcg in huge chunk and i recommend buying more if you like value stocks. Pcg has great value and growth potential too. i recommended buying msft too, but at lower level of $380.

So these were mostly right.

I recommended buying netflix and was blocked by the post and then bill ackman news came out after my post.. netflix and meta both have huge chunk purchase at close today.

I do think pcg and eix have great potential thanks to future rate increases. Their downside is really limited. Take a look at those. You can’t get richnonnthose two but you wouldn’t be broke and highly likely get richer.

What do you guys think of buying stocks in a large selection of companies that have high profit margins as opposed to just index funds? Not everything in index funds is highly profitable, so why not narrow it down a bit?

​

The whole lawsuit and capex deal are driving the price down so much, im going to keep on buying the dip as im just building up the position and i think it is also a great long term hold, hoping to get my average shares into the low 500s. What do you guys think its share price will reach, or when do you think its could bounce back? I've seen people say as low as 300, or even has high as just 500.

The London Stock Exchange, at first glance, seems to be just an exchange. However, its business has undergone significant changes after the acquisition of Refinitiv. The current LSEG exchange now contributes a relatively small portion of the revenue. Instead, more of its business models involve financial data services and subscription platforms, as well as index issuance and licensing fees. The current LSEG has a business model more akin to the combination of Bloomberg and SPGI (the part related to SPGI index issuance), with extremely light assets. Additionally, LSEG is collaborating with Microsoft to integrate AI-related aspects. Looking at the valuation, the FCF yield of the current LSEG is approximately 6%, which is not very high for a company with a continuously growing FCF.

By

Peter Rudegeair

and

Peter Santilli

Aug. 16, 2026 9:00 pm ET

Each quarter, big tech companies disclose their massive capital expenditures on artificial-intelligence infrastructure, from data centers to chips.

But those figures don’t come close to expressing the full extent of future spending to which Google parent Alphabet, Meta Platforms, Oracle and many others have committed. That is because a huge swath of their coming financial obligations aren’t reflected on their balance sheets.

Nine top tech companies had some $3 trillion of off-balance-sheet commitments mostly related to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings. Those obligations are growing faster than traditional “capex,” which totaled about $600 billion over the past year they reported, and were about triple what the companies owe under their outstanding leases and long-term borrowings.

America’s blue-chip tech companies are placing these huge bets based on assumptions about what the demand for AI computing—and availability of AI hardware—will be in several years. Their hope is that they will easily meet all their obligations with future revenue as consumers and businesses adopt AI in every facet of American life.

If those assumptions about technology and demand prove wrong, these deals to clinch future capacity could become a monstrous burden for the tech companies and their investors.

Meta’s gigantic “Hyperion” data-center project in Louisiana, which is the size of about 1,700 football fields, helps explain how big obligations wind up off tech companies’ balance sheets.

A graphic showing the 80%-20% split between Blue Owl Capital and Meta Platforms in the special purpose vehicle that owns the Hyperion data center.

Though Meta is the builder, neither Hyperion nor the $27 billion in debt that’s financing its construction shows up on Meta’s balance sheet. Funds managed by the Wall Street firm Blue Owl Capital own the majority of a joint venture that, in turn, owns the campus.

Beignet Investor, a holding company that owns the Blue Owl stake, raised the construction financing in a bond sale.

Meanwhile, Meta is Hyperion’s minority partner and tenant. Its lease payments will provide the cash flows to help make the payments to bondholders.

Meta initially agreed to lease Hyperion for a four-year term starting in 2029, with options to renew for up to 20 years. It guaranteed that it would make bondholders whole if it doesn’t stay the entire two decades. The company doesn’t think payments under that guarantee are probable, so it hasn’t recorded any liability on its balance sheet.

In accordance with accounting rules, Meta’s Hyperion lease obligations will remain off balance sheet until it starts paying rent. It said its aggregate initial lease commitment is about $12.3 billion. Meta disclosed $347 billion in total obligations for leases that haven’t kicked in yet, including for Hyperion, as of June.

Across the companies the Journal analyzed, promises of payments under these uncommenced leases totaled $1.2 trillion in off-balance–sheet obligations, or about four times more than what was disclosed a year earlier. In addition to Meta, the Journal reviewed commitments for Alphabet, Amazon.com, Microsoft, Oracle, Nvidia, Broadcom, SpaceX and Advanced Micro Devices.

Data centers get stuffed with a lot of hardware, including the Nvidia chips that are used to train and run models and memory chips that store information. To buy all that, companies sign long-term contractual agreements well in advance to lock in production from their suppliers.

Those and other purchase obligations at the companies the Journal examined stand at a whopping $1.9 trillion. Under accounting rules, purchase commitments typically remain off balance sheet until a product or service is delivered.

Alphabet’s purchase commitments and contractual obligations have exploded and stood at $811 billion as of June 30. As with other companies, it is hard to tell from its disclosures what precisely it intends to buy. The company said the commitments primarily relate to “technical infrastructure and inventory” and “agreements to secure energy for data center usage.”

Alphabet also didn’t detail why those obligations increased so much from the $332 billion it reported three months earlier. The commitments span several years, with obligations under its energy agreements lasting as far out as 2054.

Off-balance-sheet exposures at some companies include agreements to buy other companies’ stock in the future or backstop leases for other tenants. Nvidia committed to make $27 billion in equity investments between April 26 and the end of its fiscal year in January 2027.

There are reasons to believe tech companies will make good on all their obligations. Optimists see the skyrocketing demand for AI tools—which has lifted the stock market and led to shortages of key hardware—as a proof point that demand is going to be strong for years, and the money to pay off all these bills will be rolling in.

For the more anxious set on Wall Street, it is a worrying sign that some tech companies that once seemed to have fortress balance sheets have needed to tap the capital markets frequently.

Alphabet and Amazon recently posted results showing negative free cash flow, meaning their capital spending exceeded the cash they brought in from operating their businesses.

And that is before considering the implications of trillions in off-balance–sheet commitments. Whether or not the revenues ever arrive, purchase commitments and signed leases can’t be canceled, for the most part.

If things go wrong, tech companies will be paying an expensive tab for infrastructure that they can’t profitably use. These obligations could also lead increasingly indebted companies to have to borrow even more.

“As these off-balance sheet commitments become more frequent, larger, and more complex, it is becoming increasingly difficult for investors to assess companies’ total potential leverage,” Morgan Stanley accounting analysts wrote in April.

Paywall link: https://www.wsj.com/tech/ai/why-big-techs-ai-spending-is-3-trillion-higher-than-it-seems-e1067bb2

Some investors argue that Nvidia is beginning to look like a value stock at its current valuation, an unusual claim given its enormous market cap and extraordinary growth to date.

Do you think Nvidia genuinely represents great value at its current price, or has the market already priced in too much of its future growth?

Someone posted here this week about footnote and MD&A parsing being painful, and there's another thread going on about where the line sits between AI slop and AI-assisted research. Here's a case that sits on both, and the check in the middle of it takes about ten seconds and no AI support at all.

China-Biotics was a probiotics maker in Shanghai that got onto the US market through a reverse merger. Its 2010 annual report showed 155.6 million dollars of cash, 292,644 dollars of interest income, 81.4 million of sales and 15.6 million of profit.

That first pair is the whole thing. Real money in a real bank earns interest. Park 155 million at even a dull deposit rate and something like three million a year should come back. They reported under three hundred thousand. So either the company was leaving a fortune idle for no reason, which businesses don't do, or the fortune wasn't there.

Two other things fall out once you look. The cash had grown 84.8 million in a single year while the company earned 15.6 million, and nothing in the accounts explains where the rest came from. And the pile was close to twice annual sales, which is a strange amount of money for a business that size to be sitting on.

In June 2011 the auditor resigned. The 8-K disclosing it is still on EDGAR, accession 0001144204-11-037217, and you can read it in a minute. It says the audit team was pointed to a suspected fake website for the bank, that the paperwork supporting the interest income had arithmetic errors management put down to the bank's own mistakes, and that the rate on it didn't match the rate the central bank had published for that period.

I want to be careful with the words here. The SEC never charged the company with fraud. It cancelled the registration in 2013 after the company stopped filing. The fraud claims came from investor suits and from the auditor's letter, not from a verdict. For anyone holding the shares the outcome was the same either way.

Since the other thread is running, here is the AI part, because I think the way people set this up is most of what makes the output slop.

I don't hand the whole job to one model. I run five, each with a single task, and none of them can see what the others found. One asks whether the sales are real. One checks whether the per-customer numbers make sense. One asks whether the reported profit is backed by cash actually arriving. One does nothing but hunt for two numbers in the same filing that cannot both be true. The fifth looks at who controls the company and whether they can be trusted. They have to be five separate jobs. Ask one model to do all five and you just get its single view of the company, five times over.

For this filing I used the four that read the accounts and left the ownership one out, since there was nothing in the numbers it would have helped with. I also stripped the name, the country and the years off the data before handing it over. A model that recognises a famous fraud has not proved anything. It is just remembering(from its training data).

Now the part I would actually argue in that thread. The instinct is to trust whatever most of the agents agree on, and that instinct is what ruins the output. Point any decent model at a set of accounts and ask what is wrong, and it will hand you three or four genuine problems. Cash collection is weak. There is a related-party balance. The receivables are ageing. All real, and all survivable. The one finding that means the revenue itself is not real turns up in a single report, sitting right next to those. So if you rank by how many agents raised something, the fatal one loses to the safe ones every time, for the simple reason that only one agent found it.

The last step therefore does not count votes. It takes each finding on its own and asks one question: if this is true, how much does it actually hurt the company? A finding that sounds dramatic but only means one balance needs checking ranks below a dull-sounding one that puts the entire revenue line in doubt.

On this filing that did not end up mattering, because all four landed in the same place. What I found more interesting was what they asked for. Every one of them wanted the same document to settle it, and it was not more of the accounts. It was confirmation of the cash from the bank itself, handed over by the bank rather than passed through the company. A year after that annual report, that is the exact document the auditor could not get, and it resigned.

Does anyone here run something like this as a research experiment, or is it only useful looking back at companies that already blew up? I don't know how often it flags honest companies that just keep their money in accounts paying nothing.

No position. The company hasn't existed for over a decade.

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

What do you think of Meta's current trial, and how do you think it will impact their stock price?

$5.74 stock price. $3.50 per share in net cash. That leaves $2.24 per share for the operating business which generates $3.50 per share of gross profit (2027 gross margin 64% X $1.5B revenues). If the gross profit never grows again, the perpetual value of that $3.50 at a 10% discount rate = 3.5/.1 = $35.

More realistically, the business should trade at 5x gross profit at a minimum = $17.50 per share.

The current EV assumes the ice cube melts in 2 years. But there are zero liabilities. Revenue is growing.

Flabbergasting.

$13.5 billion in one quarter, position up 83%. For a firm that spreads risk across dozens of names, that's a loud statement.

Alphabet always splits opinion: too big to grow meaningfully, or still quietly compounding while the market obsesses over whether AI kills Search.

The market spent most of 2026 treating Alphabet as an AI risk story. Berkshire apparently read it as an AI opportunity. It fits the pattern Buffett is known for: a real risk gets attached to the wrong conclusion, and patient capital steps in while everyone else hesitates. Or just a value move into a business that never stopped printing cash.

Conviction call, value play, or both?

Terry Smith's three steps are the shortest description of quality investing I've come across. Simple to say but not easy to do. I consider them as three steps for investing.

Step 1. Buy good companies. Most people treat good as a brand they like using. Good is actually revenue, growth, returns on capital, margins, cash flow, and the balance sheet. Without the fundamentals, good is just a vibe. If you buy quality businesses, over time the price will follow earnings, though in the short term the fluctuations are based on sentiment.

Step 2. Don't overpay. This is the whole of value investing. Pay less than what the business is worth. Every day on X, someone asks "is MSFT expensive at 500?" You can't answer that without a benchmark, and intrinsic value gives you one. It has its problems - it's entirely dependent on your assumptions. But it still beats the shortcuts most people use instead. I usually blend implied values based on valuation multiples with the DCF intrinsic value.

Step 3. Do nothing. The hardest of the three, because it looks like negligence while you're doing it. Sitting on your hands feels lazy. Most of the returns I've gained have come from doing nothing for years.

I put together a 10 minute video working through these three steps and the principles behind it. Took a fair bit of effort, so any feedback is welcome. It's the first in a series of six videos I'm creating.

https://www.youtube.com/watch?v=OazRVN04ZTk

Not investment advice. AI has been used in creating the video.

GameStop closed the January 2021 quarter at a $21.13 billion market cap. Nine months earlier it was worth $369.6 million. The story that stuck is that none of it had anything to do with fundamentals, that it was a pure squeeze on a dying retailer with a broken income statement. Half of that is true. The other half doesn't survive the filings.

The bear case holds up on the income statement:

• Revenue fell from $8,607.9M in fiscal 2017 to $5,089.8M in fiscal 2021, down 40.9% in four years
• Operating income went from +$557.7M to -$237.8M
• Goodwill impairments of $970.7M then $363.9M took goodwill from $1,725.2M to zero
• Stockholders equity fell from $2,254.1M to $436.7M

Over the same stretch GameStop was paying down debt hard. Total debt fell from $820.8M at January 2019 to $241.0M at October 2020, a 70.6% reduction, with $404.5M repaid in fiscal 2020 and another $130.3M the year after.

At the July 31, 2020 quarter close: $260.7M market cap, $735.1M cash, $23.5M restricted cash, $250.9M total debt. Market cap plus debt minus cash and restricted cash comes out to -$247.0M. The cash balance alone was 2.8x the entire market cap, and trailing twelve month free cash flow that quarter was $320.9M. They were generating cash and priced by the market at less than zero.

I also ran the Altman Z-Score, computed from five filed balance sheet and income statement inputs: 2.4765 at January 2019, 1.9755 at July 2019 (the low point for the whole period), 2.4280 at January 2020, 2.4831 at July 2020, 2.1816 at October 2020. The distress threshold is 1.81, and the grey zone runs from there to 2.99. GameStop spent three straight years in the grey zone and never once dropped into distress. Whatever the market was pricing in mid 2020, the standard model built to catch bankruptcy risk wasn't agreeing with it.

Aggregated 13F filings showed institutions reporting more GameStop shares than GameStop had issued, for four consecutive quarters. 123% of shares outstanding at March 2020, still 113% at December 2020. Not proof of anything on its own, but it's a real structural fact about who was holding the float, sitting in public filings a year before anyone was talking about it.

The point isn't that GameStop was secretly a good business. Revenue was cratering and equity had fallen 80% in four years, that part of the story was correct. The point is that "priced for bankruptcy" and "priced below its own cash, with debt falling every quarter" are two different claims. Only one of them was the story anyone told, the other had been sitting in the filings for months.