I’m Buying Every Share I Can (Investors Aren’t Ready)

The single biggest argument against AI stocks just died. Wall Street analysts have been saying the same thing for years now. AI is a bubble and the entire tech sector will crash as soon as spending slows down even a little. But three major developments just put an end to that argument and kicked off what could be the next phase of the AI era. My name is Alex and I spent eight years as an electrical engineer and AI researcher at MIT. And I’ve never seen chip companies move this fast. Let me show you what’s going on and how I’m investing in it. Your time is valuable, so let’s get right into it. The biggest case against AI has always been that spending will eventually slow down. And when it does, every stock that lives and dies by that spending will go down with it.

Table of Contents

1. Key Takeaways
2. Hyperscaler CapEx Keeps Climbing
3. Data Center Infrastructure Is a Zero-Sum Game
4. AMD’s Answer: Model-Etched Chips
5. Cerebris’s Answer: Wafer-Scale Chips
6. Nvidia’s Answer: GPUs as Investable Assets
7. Where AI Spending Goes Next

Key Takeaways

• The “AI bubble” argument is losing its foundation: the biggest hyperscalers are going free-cash-flow negative and still raising capital to expand AI spending.
• AI infrastructure is a zero-sum game, so the more likely risk isn’t a spending stop — it’s a shift to the next bottleneck, whether memory, networking, or power.
• AMD is betting on model-etched, hyper-specialized chips (via Talus) that can be swapped out in about 60 days when newer models arrive.
• Cerebris is betting on wafer-scale chips that eliminate network cables and switches, moving data dramatically faster on a single massive chip.
• Nvidia is pushing to turn GPUs into investable collateral assets, with financing platforms that could bring pension and sovereign-wealth money into AI data centers.

It’s only a matter of time. Hyperscalers like Google, Amazon, Microsoft, and Meta platforms report their CapEx budgets every quarter. And every quarter, it’s been growing like crazy. In fact, their AI spending has been compounding at over 60% per year since 2023, and it’s actually expected to speed up. These four companies are currently on course to spend over $700 billion on data centers this year alone, compared to the $375 billion they spent in 2025. That’s a 90% increase year over year, and it’s coming at a huge cost. Amazon’s free cash flow went from $18 billion to negative $7.6 billion over the last 12 months. But just a few weeks ago, Amazon increased their CapEx budget for 2026, from $200 billion to $220 billion due to the price of memory chips.

That same week, Alphabet added $15 billion to its CapEx budget while reporting that their free cash flow turned negative for the first time since Google went public in 2004. And Meta’s free cash flows are down by 91%. You’d think that spending would slow down once the biggest companies driving it burn through all their cash flows, and then some. But instead, they’re borrowing money and issuing new shares. Amazon sold $37 billion worth of bonds in quarter one. Alphabet issued over $65 billion worth of bonds so far this year and announced an $85 billion dollar equity raise in June, the biggest raise in American corporate history. Debt covered 9% of hyperscaler capex in 2024. Today, it covers 32%.

It’s important for investors to understand why spending isn’t slowing down and why these companies keep investing in AI at all costs. Data center infrastructure is a zero-sum game. There’s only so much land and grid-connected power in the first place, and every acre and gigawatt you don’t get is one that your competition does. A big new data center can wait anywhere from two to six years for a grid connection. And even when it’s all bought up, spending will just shift to the next big bottleneck. Cooling, compute density, network and memory speeds, all of which are zero-sum games too, since every single part of the AI stack is currently supply constrained. So the question isn’t whether AI spending will slow down, but where it will shift to next.

And three of the biggest ai chip companies on earth nvidia cerebrus and amd all have very different answers let’s start with amd amd’s answer is memory an ai model is made up of billions of numbers called parameters and the actual value of each parameter comes from the patterns that it learned during training those values are called weights and each weight takes up about 16 bits or 2 bytes of memory so a model with 400 billion parameters needs around 800 gigabytes of memory a single nvidia h200 gpu has 141 gigabytes of memory so you’d need about six of them just to ask the model a single question today those weights live in memory that sit outside of the processor and the processor spends most of its time waiting for data to arrive that wait time is the bottleneck on august 6th