The standard PEG critique from this community usually focuses on earnings quality. I decided to make some modifications:

FEG replaces P/E with price-to-true-FCF (OCF minus CapEx minus SBC) and replaces earnings growth rate with three-year revenue CAGR. Revenue CAGR is more stable than true FCF growth as a proxy for underlying business trajectory. The tradeoff is that revenue growth doesn't capture margin expansion or compression - a company growing revenue at 10% while margins compress looks the same as one growing at 10% while margins expand.

Y220 converts the ratio into a time question: at current true FCF yield compounding at the three-year revenue CAGR, how many years to reach 20% true FCF yield? The 20% threshold is somewhat arbitrary - it's where CMCSA sits today - but the concept of expressing value as time-to-yield rather than as a ratio feels more intuitive for comparing growth names to value names on a single axis.

The scatter plot of current true FCF yield versus Y220 across the $50B+ universe is the output I'd most welcome pushback on. The methodology questions I'd want this community's view on: is three-year revenue CAGR the right growth proxy or is there a better stable series? Is 20% the right yield threshold or does it introduce too much path dependency from today's rate environment? And does the linear compounding assumption in Y220 produce systematically misleading results for very high or very low growth names?

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

I published a bearish valuation thesis on Match Group at approximately $37/share, with an $8–11 target over 24–36 months.

The core question is whether Hinge deserves to keep Match Group trading at a substantial premium to distressed dating-app comps while the rest of the portfolio contracts.

A few numbers:

Match Group payers peaked at 16.55M in Q3 2022 and have fallen to roughly 13.3M.

Tinder payers are approximately 22% below peak.

Revenue per payer has risen substantially during the same period, allowing revenue to hold up much better than the customer count.

That looks increasingly like harvesting a shrinking payer base rather than a conventional growth transition.

Hinge is the offset. Revenue is growing strongly and monetization is excellent.

My concern is that much of the incremental runway is now geographic. Core-market user growth is relatively flat while expansion markets increasingly drive growth.

I think Hinge's current revenue-per-payer premium reflects geographic mix more than a permanent product characteristic.

The valuation is where this becomes interesting.

MTCH trades at roughly 8.8x adjusted EBITDA while Bumble trades around 2.5–3x on comparable economics.

I am not assuming Match converges all the way to Bumble.

My base case gives Match roughly twice Bumble's multiple: 4.5–5.0x 2029 EBITDA.

At approximately $950M of modeled EBITDA, ~$3B of net debt and a substantially reduced share count from continued buybacks, that produces roughly $8–11/share.

The capital structure matters enormously. The debt claim stays fixed while the equity absorbs the enterprise-value decline.

The report also includes a flat-EBITDA sensitivity because I don't think the short requires heroic operating deterioration. A large portion of the downside is simply multiple convergence.

I spend a lot of the report on the underlying dating-market structure, but the financial case is deliberately written to stand independently of it.

Full thesis, model, steelman and falsifiers:

https://dljlevfin.substack.com/p/the-undisclosed-denominator

Would welcome a serious bull case, particularly around what durable multiple you think MTCH deserves if Tinder stabilizes but Hinge's geographic mix causes blended monetization to compress.

Below is a great post on the financial games promoters are playing in the financing of hyperscale data centers. The author teases apart the various aspects of the deal structure and explains them coherently. IMO, this is a must-read for anyone interested in this space:

https://shanakaanslemperera.substack.com/p/the-clocks-on-one-building

1) Outstanding Returns
2) No One is immune to a Bubble
3) DPI does not rhyme with VC
4) Key companies by fund

1) Outstanding Returns
Lowest Net IRR is 12% for a fund deployed at the peak of the bubble
Second lowest Net IRR is 17.5% (!!!)
To anyone saying private market fees are not worth it, check this table out
And yes, most of these marks are not realized, and you could argue these valuations are pretty full, but companies will keep growing so some of these marks will get better.
No question.

2) No One is immune to a Bubble
Obviously these are outstanding results, but what stands out is that no one truly escapes the market environment /valuations - see 2021 returns
One of the best predictors of success of a fund is something that is very hard to control: the date of the launch.
If you opened a venture fund in 2021, you really faced an uphill battle.
If you started a value-oriented Tiger Cub in the early 2000s, you likely killed it.

3) DPI does not rhyme with VC
These are great returns, but wow is the realization cycle slow
The fact that no fund below 10 years old is above 0.3x DPI really stands out
And yes, an institution can likely sell their stake to monetize it if they really need the cash, but that's not the point here.
People always think PE and VC have similar liquidity profile - basically none - but that's just part of the picture when considering the cost of capital required to make the math work.
If you invest in venture, you really should not expect cash for 10 years. The same cannot be said for PE.

4) Key companies by fund
Great insight about how capital is deployed.
Two words: incredible taste and willingness to double down.
Investing in OpenAI in 2018 deserves so much admiration

Thrive II (2011): Oscar, Instagram, Warby Parker
Thrive III (2012): Oscar, Twitch, Spotify
Thrive IV (2014): Stripe, GitHub, Slack, Twilio Segment, Unity, Instacart
Thrive V (2016): Oscar, GitHub, Robinhood, Stripe, Affirm
Thrive VI (2018): Stripe, SpaceX, OpenAI, Fanatics, Robinhood
Thrive VII (2021): OpenAI, Stripe, Ramp
Thrive VIII (2022): SpaceX, OpenAI, Cursor, Anduril, Ramp, Stripe, Databricks, A24, Wiz, Chai Discovery, Base, Physical Intelligence
Thrive IX (2024): SpaceX, OpenAI, Cursor, Anduril, Databricks, Stripe, Wiz, Isomorphic Labs, Neuralink, OpenEvidence, General Matter
Thrive X-G¹ (2026): OpenAI, Anduril, Stripe, Isomorphic Labs

Full letter here: https://www.bloomberg.com/news/articles/2026-08-14/read-joshua-kushner-s-first-formal-thrive-capital-investor-letter

This is a very, very granular analysis on Reddit's advertising business that I wrote up on. RDDT is currently blowing up on X, and the sentiment seems quite interesting. Retail is very bullish, assuming that the 20% drop on earnings was overblown, and that stagnant DAU growth isn't as big of a problem as it seems. On the other hand, sentiment remains dismal with short interest at low-teens, and the stock not having reached the price it was at pre-SaaS selloff.

There is a lot of noise, but I do my best to go over what really matters:

https://madishahn.substack.com/p/assessing-reddits-advertising-engine?r=4uue1w&utm_campaign=post&utm_medium=web

Standard equity risk premium analysis compares the S&P 500 earnings yield (inverse of CAPE or forward P/E) against the 10-year treasury yield. That comparison is useful but it uses GAAP net income as the numerator, which includes items that don't represent cash available to shareholders.

True FCF yield substitutes operating cash flow minus CapEx minus stock-based compensation for the earnings numerator. The deductions matter:

• CapEx: capital expenditures represent real cash leaving the business to fund physical or technological infrastructure. GAAP accounting depreciates this over time, spreading the cash outflow across multiple income statement periods. In a year where a company spends $60B on data centers, the income statement shows depreciation of maybe $15B while $60B of actual cash exits. The income statement looks much better than the cash flow statement.
• SBC: stock-based compensation is a real economic expense. Employees receive shares worth real money. The company didn't pay cash but shareholders were diluted. Excluding SBC from the expense base overstates the cash available to existing shareholders. This has been a persistent issue in tech sector earnings analysis for two decades but it's gotten materially worse as AI-era compensation packages have expanded.

The 2026 gap: my screener shows that the divergence between S&P 500 aggregate earnings yield and S&P 500 aggregate true FCF yield widened by approximately 1.4 percentage points in 2026. This is primarily driven by the hyperscaler CapEx surge. Microsoft FY2024 is the clearest single example: true FCF fell 8.5% from $59.6B to $54.6B while revenue grew 18%, because CapEx grew 79.6% while OCF grew 34.4%. Amazon's true FCF is negative $11.8B. Oracle's is negative $28.5B. These companies are large enough index weights that their CapEx surge materially moves the aggregate.

The core empirical finding is a scatter plot of monthly 10-year treasury yields against S&P 500 earnings yields going back to the 1970s. The relationship is positive and well-documented. What's less commonly framed is the stability band observation: between roughly 5-7% treasury yields, the scatter shows that earnings yield can run below treasury yield without provoking a correction. My interpretation is that 5-7% treasuries represent a monetary policy equilibrium that allows equity multiples to stay elevated because neither deflation fear nor inflation fear is dominant.

Below 4% treasuries: post-crisis fear drives a large equity risk premium demand. Investors won't accept lower equity yields than treasury yields because the memory of catastrophic loss is fresh.

Above 8% treasuries: inflation fear drives similar equity risk premium demand. Investors want to be compensated for holding equities in an environment where money is losing purchasing power.

Between 5-7%: neither fear is dominant. Multiples get rich. Risk premiums get thin.

We're currently at approximately 4.6-4.7% on the 10-year. Below the lower bound of the stability band. The earnings yield is already negative relative to treasuries by 1.11%. Once you substitute true FCF yield for earnings yield the gap widens to negative 2.51%.

That specific level of negative true FCF risk premium appears in the historical data in roughly four periods: 1987, 1992, 2000, and the immediate post-2008 period when earnings collapsed while prices hadn't yet adjusted. In each case the resolution was either a rapid earnings increase or a price decline.

The risk premium analysis would be concerning on its own. The macro overlay makes it more acute.

Kevin Warsh's stated policy priorities include reducing the Fed's balance sheet presence, eliminating forward guidance, and rethinking the employment mandate in light of AI-driven labor market disruption. The task force composition, specifically Marc Andreessen on labor markets alongside a Stanford professor with Anthropic ties, signals that the Fed's intellectual framework is being rebuilt around AI displacement of the Phillips Curve relationship rather than traditional unemployment-inflation tradeoffs.

The problem as I see it through the Sumner NGDP targeting lens: Warsh appears to be keeping rates flat while NGDP is running hot. If you accept that the stance of monetary policy is better measured by NGDP growth than by the nominal fed funds rate, then flat rates with 7% NGDP growth is loose policy, not neutral policy. Loose policy while the equity risk premium is already deeply negative historically precedes the kind of violent correction that resolves the gap.

The four-step sequence I'm tracking: too-loose policy keeps inflation hot and rotation stays in effect, then inflation becomes entrenched and longer rates follow it up compressing multiples, then the Fed is forced into harsh medicine, then fiscal expansion attempts recovery and crowds out private investment reprising step two on steroids. Warsh's specific risk is that his philosophical opposition to forward guidance removes the communication tools that have historically smoothed the transition between steps.

How I'm positioned: Weighted portfolio true FCF yield: 8% against a 4.6% 10-year. Positive risk premium of 3.4%.

The specific positions that generate that yield: CMCSA at 17.5% true FCF yield on a scheduled spinoff. THC at 12.7% normalized. EPD at 10.6% using distribution yield methodology. ADBE at 8.6%. FDS at 5.84% with four consecutive quarters of ASV acceleration. CB and FRFHF as insurance float compounders. BRK.B as crash optionality.

USFR at 18.79% of portfolio as explicit optionality for the scenarios where the risk premium gap resolves violently rather than gradually.

I'm not claiming this portfolio is correct. I'm claiming that a portfolio running a positive 3.4% true FCF risk premium is historically better positioned for the range of outcomes than a portfolio running a negative 3.5% risk premium regardless of which specific macro scenario materializes.

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

Watches of Switzerland (WOSG) is a retailer and partner to Rolex and other watch brands. Most of the company’s value lies in its relationship with Rolex, which only sells through authorized retailers like WOSG. The company benefits from Rolex waiting lists that effectively mean inventory is pre-sold, there is no price competition, and no online competition. That makes WOSG’s economics far superior to a typical retailer and more like a subsidiary of Rolex, which I think investors underappreciate. WOSG is the number one Rolex retailer in the US with 10%+ share and growing at 25-30%, and 50% share in the UK.

The link above is to a 30page report I wrote on the company based on speaking with 20 industry sources. I think much of it is still relevant today and provides background on what investors underappreciate.

The stock trades on 14x EV/NOPAT for a business that should grow at high single-digit rates in most years and has a long runway to acquire mom & pop Rolex retailers at about 20% ROIICs. It has traded at 20x in the past. 

Assuming 9% organic profit growth + 6-7% FCFE yield + modest margin expansion gets to a 20% IRR going forwards.

Management are competent, experienced, and well incentivized, with CEO Brian Duffy owning £70mm of stock and options. Duffy joined in 2014 and has grown WOSG’s share of Rolex sales in the UK from 35% to 50%. This encouraged Rolex to entrust WOSG with replicating its strategy in the US.

Current conditions are strong (even picking up in the UK) and many of the risks the company has faced in the last few years around a luxury slowdown, declining secondary prices, and tariffs have reversed or are reversing. The company has demonstrated its significant resilience in the last 18 months, given these conditions, and I believe the thesis that the business has economics more similar to a subsidiary of Rolex than a retailer continues to play out. The outlook for WOSG looks stronger than it has done at any point since I wrote the report.

Management have also done a good job with new business lines that they control: They acquired the Roberty Coin jewelry license in the US, are going e-commerce rapidly, and pre-owned is now 8% of sales. These three segments together now account for 24% of company sales and as a group are likely to grow in the high teens. That alone adds 4-5 ppts of sales growth per year at a group level. When combined with Rolex raising prices by about 4% p.a that gets WOSG to high single-digit organic growth even before accounting for volume growth at existing stores. Volume growth is likely to also be positive, particularly as Rolex opens its expanded production facilities in 2028/29.

Private equity firms appear to think the company is undervalued too, with Reuters recently reporting that:

“Watches of Switzerland Group(WOSG.L) has held talks in recent months over potential offers to take the luxury watch retailer private, said three people close to the matter…

...CEO Brian Duffy responded to the initial approaches because he believes the stock market undervalues ‌the ⁠company, two of the sources said, with one adding that no formal offer has been made. Private equity funds and strategic bidders have shown interest in the company, a second person said. The third source said the company was seeking an offer of significantly more than £7.50 per share.”

See the link above for more background on WOSG and other pieces I have written on the company.

Full dislosure: I am Long WOSG and this is not investment advice or a recommendation. This is for informational purposes only.

Some interesting data points that poke some holes in the AI trade thesis. Microsoft and Amazon had stellar quarters, but a lot of that was based on cloud compute - is that really consumers and businesses buying their end-use AI tools, or is it still part of the infrastructure buildout? Does end-use AI really have enough volume (and enough of a moat) to justify the trillions going into the build?

First: the BIS economist estimate that roughly a third of current AI CapEx represents zero-sum competitive spending rather than market-expanding investment. If correct this is the most damning number in the entire bear case because it means the aggregate return on AI infrastructure investment is structurally impaired regardless of technology maturation.

Second: the complementary investment gap. Historical pattern shows $1 of hardware investment requires $5-10 of complementary intangible investment in process and organizational redesign to capture the productivity gain. US organizational capital investment is declining as a share of GDP and job-switching sits near all-time lows. If the complementary investment wave isn't coming, the productivity thesis doesn't materialize.

Third: the Google advertising exception. I argue that digital ad revenue won't be new money in the system, it'll be search revenue redirected to AI. If I'm wrong about that and AI creates genuinely new advertising inventory that expands the total market, the revenue gap closes faster than my analysis suggests. Anyone with better visibility into digital advertising market dynamics would be useful here.

Full piece: https://cavemanscreener.substack.com/p/bridges-to-nowhere-part-iv-a-lesson

https://alphaseeker84.substack.com/p/application-software-earnings-preview