Our latest analysis lays out how we plan to navigate the rest of this bear cycle, along with the technical roadmap to $2 million — a view further supported by what we're seeing across the U.S. dollar, Treasuries, and gold, which together point to a challenging macro backdrop that could ultimately turn favorable for Bitcoin.

In our last broad market report on June 18th, we presented a case for caution, presenting numerous warning signs that we tend to see before a correction, ranging from institutional positioning to numerous divergences within the broad market. The one data point that concerned us most had last appeared in 2024, just before the AI trade saw its biggest correction since the uptrend began in 2022: “The economically sensitive Transportation sector is also flashing the same warning. It’s down about 10% while semiconductors are up 43%.  The only other time these two sectors diverged this sharply was July 2024. That divergence marked a one-year top in semis and gave way to a 40% drawdown into the April 2025 low.”

Big Tech’s AI Capex has stomped estimates for multiple years and analysts are now calling for capex to surge to $1 trillion in 2027. However, hyperscalers have long battled investor concerns around whether billions in spending would translate to the transformative growth they claimed. Recent hyperscaler results show that the needle is beginning to move. Top cloud providers all saw strong growth accelerations in Q2 and noted that demand is outstripping capacity.

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.