Trust the Capex, Follow the Spend

Minmax77 · 2026-06-03T10:51:48.202Z

When a monstrous cash-printing machine like $GOOGL says its own cash flow, already absurd at ~$174B TTM, is not enough to fund the AI race... That shows absolute conviction. They took on over $85B of debt and are still raising another $80B: $40B ATM, $30B offerings, $10B Berkshire Google is not speculating; their internal data shows that AI demand has no end in sight. Cloud +63% YoY. Backlog nearly doubled. And those growth numbers were likely still capped by compute constraints. They have the moat, distribution, TPUs, data and customer relationships. They are not some startup trying to survive. Google sees an unprecedented opportunity so large that it is willing to move away from its ultra-clean balance sheet and ask shareholders for more capital..after years of returning massive cash through buybacks. I think they wanted to raise it quickly before the market gets more crowded with mega IPOs. And this is part of the moat: no mid-tier company can realistically match this level of capital, compute and infrastructure. By investing in $GOOGL, you are betting that today’s capex turns into massive future returns, which I believe will be rewarded. 💡But we don’t have to only bet on the companies on the spending spree. 💡 AI capex has to flow somewhere. So follow the money. Who gets paid today while hyperscalers build for tomorrow? The obvious names are easy to spot. The more interesting part is connecting the second-order paths. 𝐎𝐛𝐯𝐢𝐨𝐮𝐬 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 / 𝐟𝐢𝐫𝐬𝐭-𝐨𝐫𝐝𝐞𝐫 𝐛𝐞𝐧𝐞𝐟𝐢𝐜𝐢𝐚𝐫𝐢𝐞𝐬: $NVDA $AMD $AVGO GPUs, accelerators, custom silicon, networking. $TSM $ASML One builds the chips. The other sells the tools needed to build them. Memory / storage: $MU SK Hynix / $HY9H Samsung $SNDSK AI is brutal on HBM, DRAM and storage. What used to be a cyclical market is becoming a structural bottleneck. 𝐓𝐡𝐞𝐧 𝐭𝐡𝐞 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐥𝐚𝐲𝐞𝐫: Servers / racks: $DELL $HPE $SMCI Buyers do not just buy chips. They need full rack-scale deployment. Energy / grid: Siemens, GE Vernova, Prysmian / $PRYMY AI data centers need power, cables, grid upgrades and deployment speed. Even a “boring” cable company like Prysmian is up around 170% in 1Y. 𝐓𝐡𝐞 𝐞𝐯𝐞𝐧 𝐦𝐨𝐫𝐞 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠 𝐩𝐚𝐫𝐭 𝐢𝐬 𝐬𝐞𝐜𝐨𝐧𝐝/𝐭𝐡𝐢𝐫𝐝-𝐨𝐫𝐝𝐞𝐫 𝐜𝐚𝐩𝐞𝐱: Following the bottlenecks. Yes, it gets much riskier. But the upside can also be much bigger. Examples: AI chips need advanced packaging, HBM integration, assembly and testing. If TSMC cannot do everything fast enough, OSATs and testing suppliers can benefit from overflow. $TSM → advanced packaging/testing bottleneck → OSATs + test equipment → $ASX $AMKR $AEHR GPUs/TPUs need to talk to each other faster, with less power and lower latency. At some point, copper becomes a bottleneck and optics move closer to the chip. AI clusters → copper/power limits → optical / CPO / silicon photonics → $MRVL $COHR $LITE Then the more speculative path: Nvidia photonics push → Ayar Labs optical I/O → Sivers $2DG laser arrays Ayar is backed by Nvidia, Sivers is partnered with Ayar on high-precision lasers Not a direct $NVDA → Sivers link, but it is a logical second-order path. There is real potential in mapping where the capex flows, who removes the bottlenecks, and who gets paid before the final AI returns even show up. But the deeper you go, the more risk you take. Smaller names can move violently both ways. A lot of future success can get priced in early, and one lost design win or customer shift can cut them out of the chain completely. The key is not chasing every AI label. It is finding the real bottlenecks, real partners, and real paths to revenue while managing risk. NFA.

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