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AI and Water

As discussed in Part 1 of this series, the lack of liquidity in water futures could serve as a catalyst for increased corporate demand for an instrument to hedge water price volatility. AI systems such as ChatGPT or Perplexity consume significant amounts of water, primarily through data center operations. For example, ChatGPT uses approximately a quarter gallon of water for every 40-100 queries, while training large language models like GPT-3 required over one million gallons of water according to Bluefield Research.

Cooling Systems

• Data centers rely heavily on cooling systems to maintain optimal operating temperatures.

• A typical 1-megawatt data center consumes approximately 25.5 million liters of water annually for cooling purposes.

• During the cooling process, water evaporates, rendering it unavailable for reuse.

Major tech companies have also reported dramatic increases in water usage. Between 2021 and 2022, Microsoft’s water consumption rose by 34%, reaching 1.7 billion gallons, while Google’s usage increased by 22% to 5.56 billion gallons. On a global scale, AI-related water consumption is projected to reach between 4.2 billion and 6.6 billion cubic meters by 2027.

Beyond data center operations, semiconductor manufacturing is another highly water-intensive process. The materials used in chip production, such as silicon, germanium, and gallium, all require substantial amounts of water. Producing a single 300mm wafer demands approximately 2,200 gallons of water, including 1,500 gallons specifically for ultra-pure water. According to a report from S&P Global, semiconductor manufacturing facilities are already consuming as much water as the 7.5 million residents of Hong Kong.

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Implications for Water Futures

The original catalysts discussed in Part 1 of this series on water futures were heightened climate risks and the growing demand from users seeking to hedge against water price volatility. Water futures could see increased activity, particularly for corporate hedging, as the growth of data centers becomes more widespread.

Recursive System Illustration

AI system as discussed previously are now increasingly used to predict water consumption and manage resource scarcity, but their own water-intensive operations amplify demand for this critical resource. This creates a feedback loop where AI-driven water demand increases stress on water supplies, making water futures more attractive for hedging and speculation, while also reinforcing the reliance on AI for resource forecasting and market insights.

• Step 1: As AI adoption grows, it drives greater water consumption, increasing stress on water resources.

• Step 2: This stress amplifies the importance of predictive tools like AI, which highlight the need for mechanisms to manage water scarcity, including water futures.

• Step 3: The rising demand for water futures markets feeds back into the financial system, potentially incentivizing more speculative activity and investment.

• Step 4: Enhanced market activity could provide further data and signals, influencing resource allocation and water-related policymaking.

Heightened awareness of water scarcity makes water futures more attractive to businesses and investors seeking to hedge against price volatility or scarcity risks. As more entities engage in water futures trading, the market generates additional data and insights, influencing policymaking and resource allocation, which can further drive AI adoption to optimize water use.

Demand from Corporations

Corporations operating major data centers are undoubtedly aware of the price risks associated with using water for cooling. However, the need to manage this risk through a regulated market may be mitigated by grants provided by various states. For example, in 2019, Google requested or was granted over 2.3 billion gallons of water for data centers across three states, as reported in public records and legal filings. Nevertheless these subsidized offerings may not always be easy to obtain so an instrument like NQH2O could prove useful in the long run.

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