From 70 Years to 70 Days: How AI is Fast-Tracking Energy Solutions

In 1950, Alan Turing, who used to chain his tea mug to radiators and cycled in a gas mask, gathered his colleagues at Manchester University (Hodges, 2014).

"I believe machines can think, and we can make one in two months," he said, adjusting the string that held up his trousers. His fellow scientists, who had seen his brilliant mind crack the unbreakable Enigma code, listened (Hodges, 2014).

However, when his ideas reached beyond the university walls to newspapers, industrialists, and politicians, the world went "pfft."

Those two months turned into 70 years. Today, that long wait has transformed into a sprint. A single AI system can process millions of data points daily, turning raw information into insights faster than ever (MIT Technology Review, 2024).

Like teaching a child to walk, each step in AI came with plenty of falls. The 1970s brought a long "winter" when funding dried up. The 1980s saw small victories. However, AI has already proven its worth in the industry, particularly energy. Shell began deploying AI solutions in 2013, achieving $1 billion in cost savings by 2019 and reducing emissions by 130 kilotons in a single LNG asset, equivalent to taking 28,000 vehicles off the road (Shell, 2023).

Between November 2020 and November 2022, everything changed. Google DeepMind’s AlphaFold2 cracked the code on protein structures, and ChatGPT showed the world that AI wasn’t sitting there counting beans; it could prove mathematical theorems, make breakthroughs in medicine, generate images and videos from text, and even write better computer code than humans.

Artificial intelligence hit like a category-five hurricane. This technology transformed months of analysis into minutes, and what took decades now happens in 30-minute coffee breaks (McKinsey & Company, 2024).

So fast that this content might be outdated by the time you read it.

Always Watching

A space ballet unfolds high above Earth, around 400 miles in the sky. Maxar's satellites pirouette at this altitude, their high-resolution cameras watching over our power grid like high-tech guardian angels. Down below, AI conducts the dance of electrons (Maxar Technologies, 2024).

As wind turbines sway to the rhythm of the Moonlight Sonata, AI orchestrates its symphony of signals from orbital watchers, weather patterns, and historical stats. AiDash's system scans the entire network from space, spotting everything from rebellious trees to tired power poles before they cause trouble - like a health checkup from space, but for power lines.

The technology catches drama before it starts. Traditional methods might miss a tilting pole or an overeager oak tree, but AI’s space-powered vision spots these troublemakers instantly. National Grid’s US operations saved $2 million by letting AI play tree trimmer from orbit.

This intelligence keeps the grid singing when traditional networks fail in remote areas. AI reads the power pulse of entire regions, directing hydrogen production like a maestro. It knows when to store energy and when to release it. When the grid hiccups, it redirects rivers of electrons in milliseconds while humans reach for the manual (Zand, 2024).

Production Gets a PhD

Tech-driven efficiency is transforming hydrogen production.

For decades, engineers fine-tuned electrolyzer parameters through trial and error. Now, machine learning models detect inefficiencies that would have gone unnoticed.

At Chungbuk National University, teams achieved a 20% boost in electrolyzer efficiency using predictive algorithms that optimized reaction conditions (Bloomberg NEF, 2024). When market demands shift, processes adjust automatically, reducing waste by up to 30%.

Experts predict another 25% efficiency gain over the next three years, with hydrogen manufacturing exceeding $2 billion by 2025 (MIT Lincoln Laboratory, 2024; Nature Energy, 2024).

A Virtual Hydrogen Lab, Powered by AI

Beyond optimizing individual electrolyzers, AI is now being used to simulate entire hydrogen production systems before a single watt of energy is spent.

By mirroring physical hydrogen plants in a virtual environment, companies can predict failures, fine-tune efficiency, and test different energy inputs without disrupting real-world operations. Every aspect - from electrolyzer performance to renewable energy integration - can be modeled and optimized.

A McKinsey & Company (2024) report found that integrating this technology into hydrogen megaprojects could improve efficiency by up to 20%, cutting operational costs and reducing waste. Instead of trial and error, companies now have a real-time hydrogen lab where AI runs experiments before anything is physically built.

With simulation-driven intelligence, predictive analytics, and self-correcting systems working in sync, hydrogen production is moving closer to full automation, where efficiency gains are, not only incremental, but exponential.

The Energy Paradox & The New Kid on the Block: Can AI Save More Than It Gorges On?

If something feels too good to be true, it is because it's too good to be true.

AI promises efficiency but gulps power like a toddler left alone with a milkshake. Training a single large model means running thousands of power-hungry GPUs for weeks, crunching through trillions of calculations. That process alone eats up as much electricity as 130 US homes in a year.

GPT-4 swallowed 50 times more. Imagine. "These models always have to be on. ChatGPT is never off," explains Dr. Vijay Gadepally from MIT Lincoln Laboratory (Heikkilä, 2025).

DeepSeek: AI on an Energy Diet

This is where DeepSeek struts in, making everybody nervous. Unlike other models that operate like brute-force engines, demanding ridiculous amounts of power, DeepSeek is built on an energy-efficient architecture that prioritizes low-power inference and adaptive learning (Reuters, 2025).

The Jevons Paradox: What If Efficiency Makes Things Worse?

History has a funny way of turning efficiency into an excuse for excess.

DeepSeek is excellent at reducing energy consumption per model. But if AI becomes cheaper and more accessible, companies will use more of it. Instead of lowering overall energy demand, AI could expand so fast that total energy use will climb the walls (Short, 2025).

From Pfft to Getting the Last Laugh

If Turing could see us now, he might adjust his makeshift belt, smile, and say, "Pfft."

Skeptics debated whether machines could think. In the meantime, they’ve gone from pure mathematical theory to becoming the rulers of the world.

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References

Blunt, K., & Hiller, J. (2025, February 6). Power stocks had been on an AI tear. Then came DeepSeek. The Wall Street Journal. Retrieved from https://www.wsj.com/tech/ai/power-energy-stocks-ai-deepseek-39a1e3be

FA News. (2025, February 10). DeepSeek’s energy efficiency counterintuitively bolsters AI energy companies’ outlook. Retrieved from https://www.fanews.co.za/article/investments/8/general/1133/deepseek-s-energy-efficiency-counterintuitively-bolsters-ai-energy-companies-outlook/41019

Heikkilä, M. (2025, January 31). DeepSeek might not be such good news for energy after all. MIT Technology Review. Retrieved from https://www.technologyreview.com/2025/01/31/1110776/deepseek-might-not-be-such-good-news-for-energy-after-all/

Hodges, A. (2014). Alan Turing: The enigma. Burnett Books.

Maxar Technologies. (2024). AI-powered satellite monitoring for energy infrastructure. Retrieved from https://www.maxar.com

McKinsey & Company. (2024). Digital twins: Capturing value from renewable hydrogen megaprojects. Retrieved from https://www.mckinsey.com/industries/electric-power-and-natural-gas/our-insights/digital-twins-capturing-value-from-renewable-hydrogen-megaprojects

MIT Lincoln Laboratory. (2024). AI-driven efficiency in hydrogen production. Retrieved from https://www.ll.mit.edu

MIT Technology Review. (2024). AI’s growing role in energy optimization. Retrieved from https://www.technologyreview.com

Nature Energy. (2024). Advances in machine learning for electrolyzer optimization. Retrieved from https://www.nature.com/nenergy/

Reuters. (2025, February 10). ESG watch: DeepSeek poses deep questions about how AI will develop. Retrieved from https://www.reuters.com/sustainability/sustainable-finance-reporting/esg-watch-deepseek-poses-deep-questions-about-how-ai-will-develop-2025-02-10/

Shell. (2023). AI-driven solutions in the energy sector: Achievements and environmental impact. Retrieved from https://www.shell.com/what-we-do/digitalisation/artificial-intelligence/can-digitalisation-and-ai-accelerate-the-energy-transition.html

Short, P. (2025, February 10). DeepSeek’s energy efficiency counterintuitively bolsters AI energy companies’ outlook. FA News. Retrieved from https://www.fanews.co.za/article/investments/8/general/1133/deepseek-s-energy-efficiency-counterintuitively-bolsters-ai-energy-companies-outlook/41019

World Economic Forum. (2024). Artificial intelligence and the energy consumption conundrum. Retrieved from https://www.weforum.org/agenda/2024/01/ai-energy-consumption/

Zand, M. (2024). AI-driven energy intelligence: Revolutionizing the energy sector through smart energy solutions. ResearchGate. Retrieved from https://www.researchgate.net/publication/380780398_AI-Driven_Energy_Intelligence_Revolutionizing_the_Energy_Sector_through_Smart_Energy_Solutions

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