Ran a true FCF yield screen across the entire AI value chain: hyperscalers, chip designers, TSMC, memory (Micron), ASML, semi equipment (Lam/KLA), materials suppliers (MKSI/ENTG/UCTT), and a control group of "beaten-down SaaS" names the market has priced as AI casualties (ADBE, CRM, FDS, VEEV, NOW).

Methodology: true FCF = (OCF - CapEx - SBC) / market cap. Flagging a data quality issue upfront since I think it matters for credibility: TSMC's true FCF yield in my dataset is overstated because of a known tag mapping issue with their 20-F IFRS filing in my XBRL pipeline. Actual CapEx runs $30-35B/year; the zero showing in my raw pull is a pipeline bug, not a real number.

Findings: most of the hardware/hyperscaler chain trades below the risk-free rate on true FCF yield, which is defensible given real growth and reinvestment (Qualcomm at 5.50% is the outlier worth a look, priced well below Nvidia's 1.82% but probably for good reasons). The SaaS layer is the anomaly: yields above the risk-free rate with still-decent growth, which by my discipline (I usually see 6-20% yields with something visibly wrong).

Also ran a Porter's Five Forces pass on the model layer itself (treating a frontier lab as the business under evaluation), since none of the fundamental moats visible everywhere else in the chain (fab, patent, export license) show up there. Scores halfway decent on 1 of 5 forces.

Full writeup with the FCF/yield dual-axis charts and net income vs. true FCF breakdowns: https://cavemanscreener.substack.com/p/early-isnt-wrong-pricing-the-ai-trade