Waterdrip Capital: left-handed BTC right-handed AI computing power — gold and oil in the digital age
By Jademont, Evan LuWaterdrip Capital
Looking forward to the long AI cycle in the future, the turbulence of 2025
A new round of industrial revolution: computing power becomes the engine of economic operation
“There are very few people in this world who can inadvertently start an era that changed human history like Edwin Drake... His drill pipe, which went deep underground, touched not only black liquid, but also touched the artery of modern industrial civilization.”
People surrounded Colonel Edwin Drake (Edwin Drake) in the mud of Pennsylvania in 1859. At that time, the world's lighting still depended on increasingly scarce whale oil, yet Drake was convinced that underground “naphtha” could be mined on a large scale. This was widely regarded as a madman's fantasy at the time. Until the first black liquid spewed out, no one would have imagined that the advent of petroleum would not only replace whale oil as a lighting energy source, but would even become the cornerstone behind human society's seizure of voice in the next 200 years, and also restructured global power and geopolitics for the next 100 years. Human history has also reached a turning point: old wealth depended on trade and shipping, while new wealth was rising with the advent of railways and energy (oil).
In 2025, we are in a very similar game. However, this time, what is surging furiously is the computing power flowing in silicon wafers, and this time “gold” is a code engraved on the chain; the “gold” and “oil” of the new era are reshaping our entire consensus on productivity and stored value assets. Looking back at 2025, the market experienced severe shocks that exceeded expectations. Trump's aggressive tariff policy forced the global supply chain to relocate, triggering a huge inflationary rebound; gold historically rose above $4,500 amid geopolitical uncertainty; and the crypto market welcomed the epic benefits of the GENIUS (Genius) Act at the beginning of the year, but experienced bursting pain in early October due to clear leverage.
In addition to the hustle and bustle of macroeconomic fluctuations, industry consensus on the field of AI computing power is rapidly fermenting: the total market value of “AI water seller” Nvidia reached a landmark of 5 trillion US dollars in October. Furthermore, the three giants of Google, Microsoft, and Amazon have already invested nearly 300 billion US dollars in AI infrastructure during the year. For example, the completion of xAI's million-level GPU cluster by the end of the year indicates computing power. Musk's xAI took less than half a year to build the world's largest AI data center in Memphis, and plans to expand to an impressive scale of 1 million GPUs by the end of the year.
The Age of Digital Intelligence: The Main Theme of the Next Industrial Revolution
Ray Dalio (Ray Dalio), founder of the Bridgewater Fund, once said, “The market is like a machine. You can understand how it works, but you can never accurately predict its behavior.” Even though the macro environment is random and unpredictable, it is undeniable that AI is still the most important long-term growth channel for the US stock market. In the next decade, AI technology will become the most critical core cog in the market machine; and will continue to influence all aspects of governments, businesses, and individuals.
Although the market debate about the “AI bubble” has never stopped, many institutions warn that the AI investment boom is already bubbling: Morgan Stanley research indicates that in 2025, the increase in investment in the AI sector led to a sharp rise in technology stock valuations, and productivity growth was not obvious, and this divergence was also compared to a bubble during the Internet boom in the 90s of the last century.
But it's an unavoidable fact: the AI-driven productivity revolution has gradually entered a period of substantial monetization. Judging from investment logic, AI is no longer just a story about tech giants. The efficiency dividends and cost limit optimization it brings are the main driving forces driving profits and productivity improvements for non-tech companies. But the costs behind this also correspond to extremely harsh employment rate swaps. There is no doubt that AI will replace the labor force, especially the white-collar class. The most direct manifestation is the exponential reduction of entry-level jobs; basic coding, accounting and auditing, or elementary management consulting and legal practice positions may all become the first batch of AI replacements.
As the application of AI deepens, the risk of unemployment in the healthcare, education, and even retail industries is piling up. Recently, a cruel joke has been popular in the US investment community: software engineers will be like current “civil engineers” in the future; I'm afraid the future will replace everyone's jobs, as Elon Musk emphasized in the interview. However, it also heralds the advent of a new industrial era belonging to AI. This era is called the “digital intelligence era.”
Looking ahead to 2026, demand for AI will continue to expand
The 4 stages of investment in the AI industry
As the AI boom moves from concept to spread throughout the industry, where is the next wave of AI growth under the premise that the market has fully priced its MAG7 (the 7 biggest US stocks)? The “four-stage AI investment model” proposed by Goldman Sachs stock strategist Ryan Hammond indicates the next path: AI investment will go through four stages in sequence: chip, infrastructure, revenue empowerment, and productivity improvement.
Four-stage AI investment model, reference source:https://www.goldmansachs.com/insights/articles/ai-infrastructure-stocks-poised-to-be-next-phase
At present, the AI industry has just reached the intersection of “infrastructure expansion” to “application implementation”, that is, the transition period from phase 2 to phase 3. The demand for AI infrastructure is exploding:
Global data center electricity demand is projected to increase 165% by 2030
From 2023 to 2030, the US data center power demand will grow at a compound annual rate of 15%, which will increase the share of data centers in total US electricity demand from 3% today to 8% by 2030.”
Cumulative global spending on data centers and hardware is expected to reach $3 trillion by 2028.
Goldman Sachs forecasts electricity demand for US data centers, image source:https://www.goldmansachs.com/pdfs/insights/pages/generational-growth-ai-data-centers-and-the-coming-us-power-surge/report.pdf
At the same time, the generative AI application market is also exploding and will grow to $1.3 trillion by 2032. In the short term, the construction of training infrastructure will drive the market to grow at a compound annual growth rate of 42%; in the medium to long term, the growth momentum will gradually shift to large-scale language model (LLM) inference equipment, 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 stated in its latest macro-outlook for 2026:2026 will be a “year to realize” the return on investment (ROI) of AI, and AI will substantially reduce costs for 80% of non-tech companies in the S&P 500. That is, to verify whether AI can actually achieve a qualitative shift from “potential” to “performance” on an enterprise balance sheet.
Therefore, in the next 2-3 years, the focus of the market will no longer be limited to a single tech giant, but will spread further: dig deeper into AI infrastructure (such as electricity, computing power hardware, and data centers) and look upward for generalized industry companies that successfully turn AI into profit growth.
AI computing power is the “new oil” and BTC is the “new gold”
If AI computing power is the “new oil” of the digital intelligence era, driving exponential shifts in productivity, then BTC (Bitcoin) will be the “new gold” of this era, acting as the ultimate underlayer for value anchoring and credit settlement.
As an independent economic agent, AI doesn't need a human banking system; the only thing it needs is energy. BTC, on the other hand, is pure “digital energy storage.” In the future, AI will be the “fuel” of the economy, while BTC is the “anchor” behind economic value. The issuance of BTC depends entirely on proof of work (PoW) based on electricity consumption, which fits perfectly with the nature of AI (power converted to intelligence).
Second, as an expendable productivity asset, the core cost of AI computing power comes from electricity, and its value output depends on the efficiency of algorithms; as a decentralized stored value asset, BTC is essentially a monetized expression of energy, and naturally has a “reservoir” function to balance the unevenness of global computing power in time and space. AI requires continuous and stable electricity, and BTC mining can digest waste electricity generated by unequal time and space in the power grid. In other words, BTC mining stabilizes the power grid through “demand response (demand response)”: when there is excess electricity (such as peak wind energy and light energy), computing power can be absorbed as a load; when electricity is scarce (peak AI computation), mining computing power can be shut down instantaneously, releasing electricity to higher-value AI clusters.
GENIUS Act: the starting point of the convergence of stablecoins + RWA + computing power on the chain
With the passage of the GENIUS Act in 2025, the US dollar is also preparing to gradually complete digital transformation, and stablecoins are included in the federal regulatory framework and become an “on-chain extension” of the US dollar system. This bill not only injects a trillion-dollar on-chain liquidity pool into US debt, but also provides a model for important global jurisdictions (such as the European Union, the United Kingdom, Singapore, and Hong Kong) to design stablecoin regulation systems.
The establishment of this compliance framework first injected strong institutional impetus into the RWA (Real World Assets) market: with the help of regulated stablecoins to enhance global liquidity and support efficient cross-border settlement and transactions, RWA issuance and circulation will be more convenient, and stablecoins have become the main means of payment for on-chain RWA investments in real estate, bonds, art, etc., supporting rapid global cross-border settlement.
Among them, AI computing power assets are gradually being regarded as a standardized RWA due to their high investment costs, steady income, and asset-heavy attributes, and naturally meet the requirements of on-chain digital management: whether it is GPU cloud computing, AI inference resources, or the operating capacity of edge computing nodes, parameters such as pricing method, rental cycle, load rate, and energy efficiency ratio can all be quantitatively mapped through on-chain smart contracts. This means that future computing power leasing, revenue splitting, transfer, and collateral services will be fully migrated to the on-chain financial infrastructure for transactions, settlement, and refinancing; in addition, computing power can also be used to achieve real-time insight into equipment operations and revenue through on-chain data to ensure transparent and verifiable returns; at the same time, computing power supply can be flexibly scheduled as needed, reducing the risk of capital occupation and idle resources under the traditional asset-heavy model, and ensuring the stability and transparency of earnings.
What is even more impressive is that, like the oil exchange that appeared on Wall Street 200 years ago after the discovery of oil, AI computing power can be expected to achieve innovative financial operations such as on-chain financing, trading, leasing, and dynamic pricing after using RWA as a financial asset that can be standardized; a new generation of RWA-based “computing power capital markets” will have more efficient value circulation channels and unlimited application space.
New opportunities under the “Double Consensus”
In a new era where AI is fully integrated into our lives, computing power will be the consensus for efficient productivity, and extreme liquidity along with efficient productivity — BTC will become the new definition of stored value consensus.
Well, if companies can grasp one side of “productivity” and “assets” in the future, they will become the most valuable entity in the future cycle, and cloud service providers are at the intersection of the “BTC storage consensus” and the “AI production consensus.” If computing power is the high-energy fuel that drives the high-speed operation of the digital economy, then cloud services are smart conduits 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” (hyperscalers). Their main business is mainly IAAS (Infrastructure as a Service Infrastructure as a Service) for general needs. Although they have a large pool of computing power resources, they may be inefficient in situations where computing resource scheduling is required. Hyperscalers is also the most advanced AI computing power service. It holds the vast majority of computing power resources on the market, and is still continuously developing the computing power infrastructure layout:
Microsoft (Microsoft): Launched the $100 billion “Stargate” (Stargate) program to build a million-level GPU cluster to provide extreme computing power support for OpenAI's model evolution.
Amazon (AWS): Committed to investing 150 billion US dollars over the next 15 years to accelerate the deployment of its self-developed chip Trainium 3, and decouple computing power costs from external supply through hardware autonomy.
Google (Google): Annual capital expenditure remains high at $80 to 90 billion. Relying on the high energy efficiency advantages of self-developed TPU v6, it is rapidly expanding AI Regions (AI Regions) around the world.
Meta: Zuckerberg made it clear during the earnings conference call that Meta's capital expenditure (Capex) will continue to grow, and the 2025 guidelines have been raised to 370-40 billion US dollars. Through liquid cooling technology upgrades and 600,000 H100 equivalent computing power reserves, the world's largest open source AI computing power pool will be built.
xAI: With “Memphis Speed,” Colossus, the world's largest single supercomputing cluster, was launched, aiming to sprint to the scale of 1 million GPUs, demonstrating extremely aggressive and efficient infrastructure delivery capabilities.
Other emerging cloud service providers such as CoreWeave, Nebius, etc. are called NeoCloud, and their main business has expanded to IAAS+ PAAS (Platform as a Service Platform as a Service) compared to general cloud platform services provided by giants. Neo Cloud focuses on high-performance computing platforms for AI training and inference, which not only provides more flexible computing power rental solutions, but also provides computing power scheduling solutions specific to AI training and inference needs, with faster response and Lower latency.
At the same time, stock up on top GPUs (H100, B100, H200, Blackwell, etc.) and build our own high-performance AIDC, pre-install the complete unit, liquid cooling, RDMA network, and scheduling software, and quickly deliver it to customers with flexible leases for the whole machine or the entire park+daily fee.
The leading player in Neo Cloud is undoubtedly Coreweave; as one of the most notable technology stocks in 2025, Coreweave's current core business is cloud computing and GPU-accelerated infrastructure services that favor AI training and inference scenarios. Of course, CoreWeave is not the only new type of company that can rent out computing power; Nebius, Nscale, and Crusoe are all strong competitors.
Unlike Neo Cloud's asset-heavy computing power cluster-scale games such as CoreWeave in the European and American markets, GoodVision AI represents another possibility of globalizing computing power — building rapidly deployed, low-latency, and cost-effective AI infrastructure in emerging markets where electricity and infrastructure are relatively weak to achieve computing power equalization. Furthermore, on the one hand, giants are building million-level GPU clusters in Memphis and other places to train models with larger parameters; on the other hand, GoodVision AI solves the “last 100 kilometer” delay response problem of AI application implementation through modular inference computing power nodes distributed in emerging markets such as Asia.
It is worth mentioning that most of the top AI computing power service providers have clear characteristics, and their founding teams or core structures are deeply rooted in the crypto mining industry. The shift from mining to AI computing power is not an interbank, but a strategic reuse of core capabilities. BTC mining and AI high-performance computing are highly isomorphic in the underlying logic, and both are extremely dependent on large-scale power acquisition, high-power central deployment, and 7x24 hours of extreme operation and maintenance. These companies have accumulated experience in managing cheap power channels and hardware in their early years, and have become the scarcest premium assets under the AI wave.
As demand for AI computing power grew exponentially, they naturally switched these existing infrastructures from “mining stored value assets (BTC)” to “outputting productivity computing power (AI)”. And as the “two-way switching” technology matures, BTC can balance the problem of uneven distribution of energy in time and space. As a result, entering the digital intelligence era, the “fuel” driving the shift in productivity will change from petroleum to computing power, and the “underlying asset” that anchors its value will also evolve from gold to BTC.
Combining blockchain technology to bring computing power to the chain, as an RWA asset, it can not only achieve verifiable records of computing power sources, usage efficiency, and operating benefits, but also establish smart contract settlement mechanisms across regions and time periods, thereby reducing credit risk and intermediary costs, and expanding its application scenarios in DeFi and cross-border computing power leasing. For example, edge computing power nodes can achieve PoW proof by providing parameters such as load rate and energy efficiency ratio through intelligent scheduling, and smart contract quantification can make edge inference computing power a standardized financial product that can be circulated and secured, and an “on-chain computing power market” can be realized. The combination of computing power and RWA will further enrich the types of assets on the chain and open up a new liquidity space for global capital markets.
Linking Productivity and Stored Value: Towards the Future of Monetization of Computing Power
This is a realistic proof of the “double consensus” logic we proposed earlier: BTC is the top-level value anchor for energy, and AI is a productive application of energy. From this perspective, the era of “computing power is money” came far faster and more disruptive than imagined. As humans enter the age of digital intelligence, the “fuel” that drives productivity leaps is shifting from oil to computing power, and the “underlying asset” that supports its value consensus is also evolving from gold to BTC.
Right now, like onlookers standing in the muddy land of Pennsylvania in 1859, it's hard to imagine how a drill pipe deep underground would usher in a new era of industrial civilization. Today, an optical cable that extends to data centers around the world is quietly building the arteries of a new era. And those pioneers who first bet on computing power and BTC will also play the role of new “oil tycoons” in this transformation, redefining the distribution of wealth and power in the new cycle.
References:
John S. Gordon [US]: “The Great Game: The Rise of a Wall Street Financial Empire”
Daniel Yekin [US]: “The Big Oil Game”
Goldmansachs: AI infrastructure stocks are poised to be the next phase of investment
https://www.goldmansachs.com/insights/articles/ai-infrastructure-stocks-poised-to-be-next-phase
Goldmansachs: AI, Data Centers and the Coming US Power Demand Surge
Bloomberg: Generative AI to Become a $1.3 Generative Market by 2032, Research Finds
KPMG: Bitcoin's Role in the ESG Implications
https://kpmg.com/kpmg-us/content/dam/kpmg/pdf/2024/bitcoins-role-esg-imperative.pdf
Square: Bitcoin is Key to an Incurable, Clean Energy Future
Arthur Hayes: Bitcoin Will Be the Currency of Artificial Intelligence
https://www.theblock.co/post/238311/bitcoin-ai-currency-arthur-hayes
36Kr: CoreWeave: In the age of computing power, holding a “golden shovel”
https://36kr.com/p/3501795977632640
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