<div id="readability-page-1">Money Banking &amp; Insurance Where Does Your AI Come From? And Can You Count On It? ByLouis Gritzo ,  Contributor.  Forbes contributors publish independent expert analyses and insights. Dr. Louis Gritzo is the chief science officer for FM Follow Author Sep 25, 2025, 11:15am EDT Share Save Comment  ASHBURN, VA - MAY 9: People walk through the hallways at Equinix Data Center in Ashburn, Virginia, on May 9, 2024. (Amanda Andrade-Rhoades for The Washington Post via Getty Images) The Washington Post via Getty Images Artificial intelligence is achieving utility‑like status. Virtually every company, organization and institution is adopting AI to some degree in their business processes. As reliance on AI grows, so does the need for companies to ensure their AI is as resilient as the rest of their critical infrastructure.  Behind every chatbot, image generator or enterprise application lies a vast, mostly invisible network of data centers, power plants, transmission lines, batteries, cooling systems and security layers. With such complexity, how can businesses and society avoid losing their new, increasingly critical capacity to be more efficient, and sometimes even “think” smarter?  Power is the Biggest Constraint In a country already at risk of electricity shortfalls, U.S. data center power demand is projected to double or triple by 2028, driven by AI’s soaring computing needs. That raises big questions. As developers build increasingly large data centers, how will they be powered? Traditional offsite generation and long-distance transmission to the point of use will clearly be strained, potentially at the expense of current power needs for businesses and homes.  Co-locating power generation facilities (whether hydrocarbon-fueled, renewables with battery storage, or even small modular nuclear reactors in the future) has the advantage of eliminating the need for transmission. But it places data center operators (or their partners) in the position of operating as, or developing direct partnerships with, power generators.  This trend introduces risk. More than half of data center operators surveyed in 2024 had outages in the past three years – most often from power failures and cooling issues. Downtime is expensive: business interruptions can cost as much as $9,000/minute – up to $5M/hour in critical sectors. Both the power and computing aspects of AI are also vulnerable to the same hazards affecting every business: flood, fire, storms and cyberattacks.  MORE FOR YOU AI resilience is too important to ignore. Governments worldwide – including the U.S. – are framing AI leadership as a national security issue, further intensifying global competition and rapid growth.  So how can we collectively ensure our AI tools will work when needed?  Where Your AI Comes From Your AI comes from two different types of data centers: training data centers and inference data centers.  Training data centers are where large language models are schooled on enormous volumes of data so they can later respond to user prompting. They often use the most powerful chips (GPUs and other accelerators) and house large, concentrated clusters of 24/7 power demand. Since consumers never interact with models during training, there’s little benefit in distributing these centers closer to users.  Inference data centers are where chatbots and AI tools generate real‑time answers using the often trillion different parameters developed when the models were trained. Like conventional cloud data centers, inference data centers need to be dispersed geographically to improve the response time for end users.  Training requires a massive upfront surge of electricity, while inference sustains a continuous but lower‑level draw. Interestingly, there’s a tradeoff: The more resources you invest in training the model, the less you need for inference. And vice versa.  In addition to large amounts of power, all data centers need water (for effective cooling and fire protection) and robust network connections. These factors, plus the ever-present specter of natural hazards, complicate efforts to bring inference data centers close to users. And given the substantial resource demand, some communities are questioning their value and limiting new data center construction.  Points of Failure—And What You Can Do Engineering teams like those at FM help data center owners design for and operate with resilience. Here are some key risks and resilience strategies for owners and operators. They constitute key requirements for any business assessing the resilience of their AI provider:  Power interruptions: Strategies include onsite backup systems, alternate providers, and disaster recovery plans.  Heat and fire risks: Mitigate risk with compartmentalized fire barriers, water‑based cooling, sprinklers or water mist systems, and thermal runaway-detection tools for batteries.  Network limitations: AI response requires instant bandwidth. Invest in high‑speed, redundant connections.  Physical hazards: Wildfires, floods and severe weather can damage AI infrastructure. Use hazard maps to understand those that pose a risk at vital locations and take measures to reduce the consequences. These might include low-flammability construction, hail‑resistant and tiltable solar panels, ample water supply for fire protection, or flood protection such as flood barriers and stormwater management.  Cybersecurity: AI is only as secure as its weakest link. Conduct thorough physical and digital security audits and harden defenses accordingly.  Human error: Even with automation, people remain in the loop. Operators need rigorous training, clear priorities and regular drills to ensure continuous operations.  Revenue loss: Disruptions are costly. Protect investments with adequate insurance from design and construction through operation.  The Bottom Line Although AI may sometimes feel like magic, it is more like a utility that relies on complex infrastructure with built‑in risks and extraordinary pressure to scale.  If your business depends on AI, whether you’re a user or provider, prioritize understanding and ensuring resilience. Companies that prepare now will be best positioned to thrive, sustaining continuity that supports growth, profitability and investor confidence – while others are left waiting for their systems to return to normal, or a response from their prompt.  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<p>這篇文章討論了對人工智能日益依賴的現象，以及企業確保其人工智能基礎設施韌性的必要性。文章強調，由於人工智能的計算需求，到 2028 年美國數據中心的電力需求預計將翻倍或三倍，這引發了對電力供應和潛在停電的擔憂。文章概述了訓練數據中心和推理數據中心之間的差異，強調了電力、冷卻和網絡連接的重要性。它還建議數據中心運營商採取韌性策略，以減輕因電力中斷、熱量、火災和自然災害帶來的風險</p>

Where Does Your AI Come From? And Can You Count On It?

- 人工智能的使用日益普及，企業需確保其 AI 系統的韌性。  
- 數據中心的電力需求預計到 2028 年將翻倍或三倍，增加了供電壓力。  
- 關鍵基礎設施的風險管理對 AI 的持續運作至關重要。  

你的 AI 來源於何處？你能信賴它嗎？