Ten investment theses I actually hold, with the kill criteria written down upfront so I can't quietly move the goalposts. Every weekday my pipeline pulls the prices, fundamentals and news, and an AI scores the evidence — it publishes whether it agrees with me or not. You decide what you believe.
my honest ordering: today's evidence strength first, and each pick's edge over its benchmark since it was named. it will be wrong sometimes — that's what the receipts are for.
One email each week: which theses gained or lost evidence, what tripped a flag, what the receipts caught. 10 theses tracked, scored against the market, published unedited.
The theses aren't independent bets — they share drivers. A green reinforce edge means two theses ride the same underlying force from different angles; a red tension edge means the same bet taken from opposite sides. $TSLA, for example, sits on both T2 (Physical AI, long) and T6 (the Musk premium, short): if Optimus ships the premium is earned, if it doesn't the premium compresses. Node color is today's evidence, size is how strong the read is.
Intelligence becomes free; value flows to applications with proprietary data and workflows, not model labs
VLA models turn robots into software platforms; public markets underprice it because leaders are private
Enterprise AI is hard; value flows to integrators and AI-literate services while models commoditize
AI bottleneck shifted from compute to data movement; CPO/photonics wins whether the buildout continues or rationalizes
Deficits get inflated away; scarce assets reprice. Position built during the stress test, not the party
The Musk empire trades on narrative, not cash. Tesla's auto cash cow is cracking — Q2'26 operating profit fell 57%, free cash flow went negative, and regulatory credits (once ~2pts of margin) collapsed as the EV credit expired — yet it's priced at ~145x earnings; the private crown jewels are only reachable at steep premiums to NAV. The bet: the premium compresses toward the cash the cows actually produce.
Companies generating outsized revenue and profit relative to their market cap — high margins, real free cash flow, low multiples — are mispriced. The market treats durable profit machines like they're stagnant; as the cash keeps compounding, they re-rate upward.
The AI buildout's binding constraint is shifting from chips to electricity. Datacenters are signing decade-long power deals faster than anyone can build generation; firms that own or equip gigawatts capture pricing power for years
AI turned memory from a cyclical commodity into the binding constraint — HBM is sold out years ahead, conventional DRAM and NAND supply is being cannibalized to feed it, and pricing power has shifted to a three-player oligopoly for the first time in decades
Per-seat software loses its unit of account when agents do the work. Mid-market SaaS with seat pricing and thin data moats gets repriced first: seat counts stall, net revenue retention slips, and the multiple compresses. The bet: these names underperform the software index
Prices and relative performance daily (vs each thesis's benchmark — QQQ, SMH or SPY), company fundamentals (growth, margins, valuation, cash flow), and fresh headlines collected per mechanism signal — the things each thesis actually depends on.
Every weekday Grok reads the day's evidence and scores each signal −2…+2 against the thesis mechanics, with a one-line explanation. I publish both unedited — even when it disagrees with me. That's the tape.
I wrote the kill and double-down criteria before taking the positions — they're shown verbatim on every page. Sustained negative evidence puts a thesis into kill review. No quiet goalpost-moving.
A one-person experiment in accountable investing. Nothing here is investment advice or a recommendation to buy or sell anything. Do your own work — that's the point of showing the data.