Big Tech capex is the driving force behind the AI infrastructure trade, yet Wall Street has repeatedly underestimated the sheer scale of the buildout. Currently, in 2026, the guidance for $732.5 billion is now 158% higher than forecasts issued two years ago.  Last week, the market received confirmation that Microsoft, Meta, Amazon and Google are expected to deploy approximately $432 billion in the second half of 2026 alone. This means the flashy forecast that capex will reach $1 trillion in 2027 is now firmly within reach. 

The reality of AI demand growth has shattered early estimates for token processing, yet expectations continue moving up and to the right. In the second installment of our token processing series, we examine what exploding inference demand means for the AI infrastructure market. While many parts of the AI stack are positioned to benefit, we focus on three specific areas where the implications are significant; compute, networking, and power; highlighting notable companies along the way. 

Total annual token processing is no longer measured in billions or trillions of tokens, but in the quadrillions and beyond. As annual token processing is now tracked in units with 15 trailing zeros, it is becoming more evident that management teams in the AI supply chain and researchers have drastically underestimated the pace of growth. Forecast revisions that once seemed dramatic have since been eclipsed years ahead of schedule, with one of the clearest pieces of evidence coming from a top Nvidia partner. Below, we outline how token processing growth has been greatly underestimated and why expectations may need to move higher once again. 

Nvidia is moving its next-generation Rubin GPUs from 4nm to 3nm, yet Google’s latest TPUs are already on N3 and are expected to remain there. Meanwhile, a growing number of AI CPUs from Nvidia, Amazon, Microsoft, and Arm are converging on the same node. This creates an opportunity for Intel given that it's Arizona capacity is coming online whereas TSMC’s new fabs largely will not arrive until late 2027.

Taiwan Semiconductor (TSMC) is the single, most important company to the AI industry. However, to compete with the incumbent, Intel does not need to beat TSMC at leading-edge manufacturing. It only needs to solve a problem that TSMC may not be able to solve quickly enough.

Big Tech’s AI revenue is accelerating, but free cash flow is moving sharply in the opposite direction. Across Google, Microsoft, Meta and Amazon, capex is rising much faster than operating cash flow as Big Tech races to build out AI infrastructure. Notably, the Big Tech company spending the most on capex in 2026 already turned free cash flow negative, and our estimates suggest it may not be alone by year-end.

The most pronounced difference between 2026’s tech rally compared to rallies in the past is which companies have been left out of it. The names most associated with the AI trade have hardly participated. Google leads Big Tech with returns of 18.7% YTD, followed by Amazon at 10.5% and Meta at 3.4%. Meanwhile, Microsoft has fallen 17.8%. What is most shocking is how much Big Tech has lagged in dollar terms. The combined market cap of these four firms rose just $426 billion this year to $11.93 trillion, just 3.7%. Compare this to Micron, a single supplier to the AI buildout, which alone added approximately $700 billion to its market cap in 2026.

This is Part 2 of our two-part series on AI inference economics. In Part 1 — Why Nvidia's Next AI Battle Is About Tokens per Watt, we laid out why tokens per watt has become the defining metric for inference profitability. Now, we turn from the why to the how and the who. Two architectural paths are competing to solve the KV cache bottleneck: Nvidia's proprietary CMX platform and the open, vendor-agnostic CXL standard. Although they tackle the same problem, each approach points to different sets of beneficiaries.