Bitcoin’s role in the global asset allocation framework is undergoing a structural transition. Following the post-election rally, Bitcoin has increasingly shifted away from sentiment-driven trading toward a more rational, asset-like trajectory. Volatility has compressed, leverage has declined, and price behavior is now more closely tied to allocation flows rather than speculative excess. Notably, Bitcoin’s 30-day realized volatility has declined from roughly 65–70% in prior cycles to around 35–40% in the post-ETF regime, even as prices remain near cycle highs.

The 2024 halving reinforced cost discipline across the Bitcoin network rather than triggering immediate price dislocations. Instead of igniting another speculative cycle, the halving structurally raised the marginal cost of Bitcoin production. Electricity pricing, hardware depreciation, and capital intensity now impose a tangible economic floor on supply. Post-halving estimates indicate that efficient miners’ marginal production costs are materially higher than pre-halving levels, while network hash rate growth has slowed meaningfully compared with earlier cycles.

Despite these structural shifts, equity markets continue to value Bitcoin miners using legacy high-beta frameworks. Mining equities are still largely treated as leveraged proxies for Bitcoin price movements, overlooking the growing divergence in balance-sheet strength, risk exposure, and downside protection across operators. This is reflected in the persistent reliance on hash-rate- and earnings-based benchmarks, even as miners’ balance-sheet Bitcoin exposure has increased substantially.

This mismatch between fundamentals and valuation frameworks has created a systematic mispricing opportunity. As Bitcoin’s volatility declines and production costs rise, certainty—not optionality—has become the scarce and undervalued attribute. Asset-backed miners currently trade at significant discounts to their implied Bitcoin-backed net asset value, despite lower forced-liquidation risk and stronger balance-sheet resilience.

Against this backdrop, Cango has identified AI inference as its second growth curve, positioning it as a strategic extension of its computing infrastructure rather than a purely speculative venture. Through deleveraging and resource reallocation, the company aims to build a flexible, distributed computing platform that supports enterprise AI workloads while strengthening financial flexibility. With the establishment of EcoHash and the launch of AI-focused operations under a dedicated technical team, early pilot projects have validated plug-and-play deployment in traditional mining environments and achieved operational break-even, laying the groundwork for scalable growth through standardized nodes and a unified software orchestration platform.