Waterdrip Capital: BTC in One Hand, AI Computing Power in the Other—Gold and Oil in the Digital Intelligence Era

Original Authors: Jademont, Evan Lu, Waterdrip Capital

Reviewing the Volatile 2025 and the Future AI Long Cycle

A New Industrial Revolution: Computing Power Becomes the Engine of Economic Operation

"In this world, only a very few people can be like Edwin Drake, inadvertently opening an era that changes human history... His drill rod, penetrating deep into the earth, touched not only the black liquid but also the artery of modern industrial civilization." 

In 1859, in the mud of Pennsylvania, people gathered around Colonel Edwin Drake, laughing mockingly. At that time, the world's lighting still relied on increasingly scarce whale oil, but Drake firmly believed that the underground "rock oil" could be extracted on a large scale. This was widely considered a madman's delusion. Until the first gush of black liquid erupted, no one could have imagined that the emergence of oil would not only replace whale oil as a lighting source but would even become the cornerstone behind the struggle for discourse power in human society for the next two hundred years, further reshaping global power and geopolitics for the following century. Human history also reached a turning point: old wealth depended on trade and shipping, while new wealth was rising with the advent of railroads and energy (oil).

In 2025, we find ourselves in an extremely similar game. However, this time, what is gushing forth is the computing power flowing through silicon chips, and this era's "gold" is the code inscribed on the blockchain; the "gold" and "oil" of the new era are reshaping all our consensus regarding productivity and value storage assets. Looking back at 2025, the market experienced unexpectedly severe volatility. Trump's aggressive tariff policies forced global supply chains to relocate, triggering significant inflationary rebounds; gold historically surged past $4,500 amidst geopolitical uncertainties; the crypto market welcomed the epic bullish news of the GENIUS Act at the beginning of the year, only to experience the painful liquidation brought by leverage unwinding in early October.

Beyond the noise of macroeconomic fluctuations, an industrial consensus regarding the AI computing power sector is rapidly fermenting: Nvidia, the "AI water seller," reached a milestone total market capitalization of $5 trillion in October. Furthermore, the three tech giants—Google, Microsoft, and Amazon—have invested nearly $300 billion in AI infrastructure within the year. For instance, xAI's impending completion of a million-GPU cluster by year-end signals the arrival of computing power. Musk's xAI built the world's largest AI data center in Memphis in less than half a year and plans to expand it to a staggering scale of 1 million GPUs by the end of the year.

The Digital-Intelligence Era: The Main Theme of the Next Industrial Revolution

Ray Dalio, founder of Bridgewater Associates, once said: "The market is like a machine. You can understand how it operates, but you can never precisely predict its behavior." Even though the macro environment is random and unpredictable, it is undeniable that AI remains the primary long-term growth channel for the US stock market. In the next decade, AI technology has become the most crucial core gear in the market machine, continuously impacting governments, enterprises, and individuals in every aspect.

Although debates about an "AI bubble" have never ceased, with many institutions warning that the AI investment boom shows signs of bubble formation—Morgan Stanley research pointed out that in 2025, investment growth in the AI field led to soaring valuations of tech stocks while productivity gains were not yet evident, a divergence likened to bubble signs during the 1990s internet boom.

However, an unavoidable fact is: The AI-driven productivity revolution is gradually entering a substantive monetization phase. From an investment logic perspective, AI is no longer just a narrative for tech giants; the efficiency dividends and extreme cost optimization it brings are the main drivers for boosting profits and productivity in non-tech enterprises. But the corresponding cost behind it is an extremely brutal displacement of employment rates. AI's replacement of labor, especially for the white-collar class, is unquestionable. The most direct manifestation is the exponential reduction in entry-level positions; basic code writing, accounting and auditing, as well as junior management consulting and legal practice roles, may become the first targets for AI replacement.

With the deepening of AI applications, unemployment risks are accumulating in industries like healthcare, education, and even retail. Recently, a harsh joke has become popular in the US investment circle: software engineers in the future will be like "civil engineers" today. As Elon Musk emphasized in an interview, AI will likely replace everyone's jobs. But this also heralds the arrival of a new industrial era belonging to AI, an era we call the "Digital-Intelligence Era."

Looking Ahead to 2026: Demand for AI Will Continue to Expand

The 4 Stages of AI Industry Investment

As the AI boom transitions from concept to diffusion across all industries, and with the market having fully priced in the MAG7 (Magnificent 7 US stocks), where is the next wave of growth for the AI theme? The "Four-Stage Model of AI Investment" proposed by Goldman Sachs equity strategist Ryan Hammond points the way forward: AI investment will sequentially go through the stages of chips, infrastructure, revenue enablement, and productivity enhancement.

The Four-Stage Model of AI Investment. Reference source: https://www.goldmansachs.com/insights/articles/ai-infrastructure-stocks-poised-to-be-next-phase

Currently, the AI industry is just at the intersection transitioning from "infrastructure expansion" to "application deployment," i.e., the period moving from Stage 2 to Stage 3. Demand for AI infrastructure is in an explosive phase:

• It is predicted that by 2030, global data center power demand will increase by 165%.
• From 2023 to 2030, the compound annual growth rate (CAGR) of US data center power demand will be 15%, raising the proportion of data centers in total US power demand from the current 3% to 8% by 2030.
• It is estimated that by 2028, global cumulative spending on data centers and hardware will reach $3 trillion.

Goldman Sachs' forecast for US data center power demand. Image source: https://www.goldmansachs.com/pdfs/insights/pages/generational-growth-ai-data-centers-and-the-coming-us-power-surge/report.pdf

Meanwhile, the generative AI application market is also experiencing explosive growth, projected to grow to $1.3 trillion by 2032. In the short term, the construction of training infrastructure will drive market growth at a 42% CAGR; in the medium to long term, growth momentum will gradually shift to inference devices for large language models (LLMs), digital advertising, professional software, and services.

Bloomberg: Generative AI Growth Forecast for the Next 10 Years. Data source: https://www.bloomberg.com/company/press/generative-ai-to-become-a-1-3-trillion-market-by-2032-research-finds

This judgment will be validated in 2026. Goldman Sachs' latest 2026 macro outlook points out: 2026 will be the "year of realization" for AI return on investment (ROI). AI will have a substantive cost-reduction effect on 80% of non-tech companies in the S&P 500 index. This will verify whether AI can truly achieve a qualitative shift from "potential" to "performance" on corporate balance sheets.

Therefore, the market's focus over the next 2-3 years will no longer be confined to singular tech giants but will further diffuse: digging deeper into AI infrastructure (such as power, computing hardware, data centers) and seeking out generalized industry companies that have successfully transformed AI into profit growth.

AI Computing Power is the "New Oil," BTC is the "New Gold"

If AI computing power is the "new oil" of the Digital-Intelligence Era, driving exponential leaps in productivity, then BTC (Bitcoin) will be the "new gold" of this era, serving as the ultimate underlying layer for value anchoring and credit settlement.

As an independent economic entity, AI does not need the human banking system; the only thing it needs is energy. And BTC is a pure "digital energy storage device." In the future, AI will be the "fuel" of the economy, while BTC will be the "anchor" behind economic value. The issuance of BTC depends entirely on Proof-of-Work (PoW) based on electricity consumption, which perfectly aligns with the essence of AI (transforming electricity into intelligence).

Secondly, AI computing power, as a consumable productive asset, has its core cost derived from electricity, and its value output depends on algorithmic efficiency; whereas BTC, as a decentralized store-of-value asset, is essentially the monetization of energy, naturally possessing the "reservoir" function of balancing the temporal and spatial unevenness of global computing power. AI requires continuous and stable electricity, while BTC mining can absorb waste electricity generated by grid imbalances due to time and space. That is, BTC mining stabilizes the grid through "Demand Response": when there is a power surplus (e.g., during wind or solar peaks), computing power can act as a load to absorb excess electricity; when power is scarce (e.g., during AI computation peaks), mining computing power can shut down instantly, releasing electricity to higher-value AI clusters.

The GENIUS Act: The Convergence Point of Stablecoins + RWA + On-Chain Computing Power

With the passage of the GENIUS Act in the US in 2025, the US dollar is also preparing to gradually complete its digital transformation. Stablecoins are incorporated into the federal regulatory framework and become the "on-chain extension" of the dollar system. This act not only injects a trillion-dollar-level new on-chain liquidity pool into US Treasury bonds but also provides a reference paradigm for designing stablecoin regulatory systems in other important jurisdictions globally (such as the EU, UK, Singapore, and Hong Kong).

The establishment of this compliance framework first injects strong institutional momentum into the RWA (Real World Assets) market: with regulated stablecoins enhancing global liquidity and supporting efficient cross-border settlement and transactions, the issuance and circulation of RWAs will become more convenient. Stablecoins have become the primary payment method for on-chain investments in RWAs like real estate, bonds, and artwork, supporting fast global cross-border clearing.

Among these, AI computing power assets, due to their high investment costs, stable returns, and heavy-asset nature, and naturally meeting the requirements for on-chain digital management, are gradually being viewed as a standardized RWA: whether it's GPU cloud computing, AI inference resources, or the operational capacity of edge computing nodes, parameters such as pricing methods, lease cycles, load rates, and energy efficiency ratios can all be quantitatively mapped through on-chain smart contracts. This means that future businesses like computing power leasing, revenue splitting, transfer, and collateralization will fully migrate to on-chain financial infrastructure for trading, settlement, and refinancing. Furthermore, computing power can achieve real-time insights into equipment operation and returns through on-chain data, ensuring transparent and verifiable returns. Simultaneously, computing power supply can be flexibly scheduled on-demand, reducing the risks of capital lock-up and resource idleness inherent in traditional heavy-asset models, thereby ensuring the stability and transparency of returns.

Even more imaginatively, much like the oil exchanges that emerged on Wall Street after the discovery of oil two hundred years ago, once AI computing power, with the help of RWA, becomes a financial asset that can be standardized, traded, collateralized, and leveraged, it is expected to enable innovative financial operations on-chain such as financing, trading, leasing, and dynamic pricing. A new generation of "computing power capital markets" based on RWA will possess more efficient value circulation channels and application spaces with limitless potential.

New Opportunities Under the "Dual Consensus"

In the new era where AI is fully integrated into our lives, computing power will serve as the consensus for efficient productivity, while BTC, accompanying the extreme liquidity of efficient productivity, will become the new definition of the store-of-value consensus.

Therefore, companies that can master either the "productivity" or "asset" end in the future will become the most valuable entities in the coming cycle. Cloud service providers are precisely at the intersection of the "BTC Store-of-Value Consensus" and the "AI Production Consensus." If computing power is the high-energy fuel driving the high-speed operation of the digital economy, then cloud services are the intelligent pipelines that carry and distribute this power.

Global AI Cloud Service Market Size Forecast. Data source: Frost & Sullivan

This includes several giants: Microsoft, Amazon, Google, xAI, Meta. They are also known as "Hyperscalers." Their main business is primarily IaaS (Infrastructure as a Service) catering to general needs. Although they have large computing resource pools, they may be inefficient when it comes to computing resource scheduling. Hyperscalers are also the most upstream providers of AI computing power services, controlling the vast majority of computing resources in the market and continuously laying out computing power infrastructure:

• Microsoft: Launched the $100 billion "Stargate" project, aiming to build a million-GPU cluster to provide extreme computing power support for OpenAI's model evolution.
• Amazon (AWS): Committed to investing $150 billion over the next 15 years, accelerating the deployment of its self-developed Trainium 3 chip, achieving decoupling of computing power costs from external supply through hardware autonomy.
• Google: Maintains annualized capital expenditure at a high level of $80-90 billion, leveraging the high energy efficiency advantage of its self-developed TPU v6 to rapidly expand AI-dedicated clouds (AI Regions) globally.
• Meta: Zuckerberg explicitly stated in the earnings call that Meta's capital expenditure (Capex) will continue to grow, with 2025 guidance already raised to $37-40 billion. Through liquid cooling technology upgrades and a reserve of 600,000 H100-equivalent computing power, it is building the world's largest open-source AI computing power pool.
• xAI: With "Memphis speed," completed the world's largest single supercomputing cluster, Colossus, targeting a scale of 1 million GPUs, demonstrating extremely aggressive and efficient infrastructure delivery capabilities.

Other emerging cloud service providers like CoreWeave and Nebius are referred to as NeoClouds. Their main business expands to IaaS + PaaS (Platform as a Service). Compared to the general-purpose cloud platform services offered by giants, NeoClouds focus on providing high-performance computing platforms for AI training and inference. They not only offer more flexible computing power leasing solutions but also provide computing power scheduling solutions specifically tailored for AI training and inference needs, featuring faster response times and lower latency.

Simultaneously, they stockpile top-tier GPUs (H100, B100, H200, Blackwell, etc.) and build their own high-performance AIDCs (AI Data Centers), pre-installing entire server racks, liquid cooling, RDMA networks, and scheduling software, delivering them quickly to customers with flexible leases based on entire racks or entire campuses + daily billing.

The leading player among NeoClouds is undoubtedly CoreWeave. As one of the most eye-catching tech stocks in 2025, CoreWeave's core business currently leans towards cloud computing and GPU-accelerated infrastructure services for AI training and inference scenarios. Of course, companies eyeing the computing power rental market are not limited to CoreWeave; Nebius, Nscale, and Crusoe are all strong competitors.

Unlike the heavy-asset computing power cluster scale competition of NeoClouds like CoreWeave in European and American markets, GoodVision AI represents another possibility for the globalization of computing power—through intelligent scheduling and multi-computing-power-user management, building rapidly deployable, low-latency, cost-effective AI infrastructure in emerging markets with relatively weak power and infrastructure, achieving democratization of computing power. Furthermore, on one hand, giants are building million-GPU clusters in places like Memphis for training larger-parameter models; on the other hand, GoodVision AI addresses the "last hundred miles" latency response problem for AI application deployment through modular inference computing power nodes distributed in emerging markets like Asia.

It is worth mentioning that most top-tier AI computing power service providers share a clear characteristic: their founding teams or core architectures are deeply rooted in crypto mining. Transitioning from mining to