Table of Contents
Artificial Intelligence Translates the Language of Animals
Kim Kyung-jin, Attorney at Law
The Story of AI Learning to Listen to Whales, Dolphins, Birds, and Bees
Twelve chapters on how AI listens to dolphins, sperm whales, humpback whales, birds, and bees to find rules in their sounds, and what this technology means for its risks and for animal rights. Written in simple sentences a child can read, with verified sources in every section.
Table of Contents
AI Deciphers Ancient Scripts
Kim Kyung-jin, Attorney at Law
Ancient Records Revived by AI
In twelve chapters, this book explains how AI revives records once unreadable, from burned scrolls and wooden slips buried in mud to broken clay tablets. It covers virtual unrolling at Herculaneum, virtual collation of oracle-bone texts, reading Silla wooden tablets, computational analysis of undeciphered scripts, and multispectral archives, with verified references for each chapter.
Table of Contents
Artificial Intelligence for New Materials Design and Rocket Propulsion Engineering
Kim Kyung-jin, Attorney at Law
AI Potentials, Self-Driving Laboratories, and Physics-Informed Machine Learning (PIML)
Ten chapters on how artificial intelligence is changing new materials and rocket propulsion: atomic simulation, generative models, self-driving labs, high-temperature alloys, metal 3D printing, combustion, cooling design, and engine diagnosis and control. Written without equations, with verified sources in every chapter.
AI Library
The Age of Autonomous Scientific Discovery
Kim Kyung-jin, Attorney at Law
AI Scientists and Self-Driving Labs
This book follows how AI scientists and self-driving labs are changing the way science generates and verifies claims. It covers literature-based discovery, natural-language protocols translated into robot commands, multi-agent research systems, closed-loop laboratories, materials search, the verification gap, chains of evidence, research harnesses, journal ethics, and legal responsibility.
AI Library
A New Era of Life Sciences Opened by Artificial Intelligence
Structural Proteomics, Genomic Foundation Models, Autonomous Laboratories, and Global Governance
Kim Kyung-jin, Attorney at Law
This book is a research volume compiled with artificial intelligence. A human selected the materials and structured the work, while AI models drafted the sentences and cross-checked the facts.
AI Library
The Double Structure of Digital Sovereignty
Europe’s Departure from Palantir and the Chains of American Big Tech
Kim Kyung-jin, Attorney at Law
This is a record of 2026, when European intelligence agencies and defense ministries began removing analytics tools from America’s Palantir. It covers the replacement decisions made by France’s General Directorate for Internal Security (DGSI), Germany’s Federal Office for the Protection of the Constitution (BfV), and the Netherlands Ministry of Defense; the incident in which US export controls severed an ally’s ac…
New English Edition
Artificial Intelligence in Horticulture
Kim Kyung-jin, Attorney at Law
Across five chapters and ten sections, this book examines computer vision for crop diagnosis, harvesting robots and autonomous field systems, smart greenhouses and digital twins, precision irrigation and supply-chain quality control, high-throughput phenotyping, and predictive breeding.
New English Edition
Artificial Intelligence in Food Crop Agriculture
Kim Kyung-jin, Attorney at Law
Across six chapters and eighteen sections, the book examines digital agricultural infrastructure, remote sensing, crop diagnosis, yield forecasting, precision irrigation, genomics, molecular breeding, agricultural robotics, climate-smart agriculture, and global food security.
New English Edition
The Future of Forestry and Agroforestry
Kim Kyung-jin, Attorney at Law
Driven by Artificial Intelligence and Digital Innovation
Across five chapters and fifteen sections, the book follows satellites, drones, LiDAR, digital twins, forest-specific language models, wildfire and pest forecasting, forestry robotics, agroforestry, timber traceability, and forest carbon markets.
New English Edition
Smart Livestock Farming: AI Enters the Barn
Kim Kyung-jin, Attorney at Law
Sensors listen, cameras watch, and artificial intelligence helps farmers decide.
Across five chapters and fifteen sections, the book follows precision livestock farming from animal health and reproduction to robotic milking, virtual fencing, digital twins, methane reduction, welfare, and data ownership.
Table of Contents
Han Dong-hoon, Busan Buk-gu Gap: A Record of the 100 Days Before and After the Election (Mar. 26-Jul. 3, 2026)
Kim Kyung-jin
Table of Contents and 13 sections
From March 26 to July 3, 2026, this record follows the spring after expulsion, the Busan Buk-gu Gap by-election, victory as an independent, and the first bill submitted in the National Assembly.
Table of Contents
Artificial Intelligence and Medicine
Kim Kyung-jin, Attorney at Law
AI in clinical care, hospitals, education, and research
AI in medical imaging, risk prediction, treatment planning, hospital operations, education, and research, with patient safety, privacy, and accountability.
[AI Library] Chapter 7. Energy and the Environment
Artificial Intelligence and the Reshaping of Society
Chapter 7. Energy and the Environment
Kim Kyung-jin
1. The Explosion in Data Center Power Consumption: A Country Ranking Fifth in Global Energy Use
The barren plains of Abilene, Texas. Even at midnight, a massive building glows bright. It is the first data center of OpenAI's Stargate Project. This single building swallows 1.2 gigawatts of power, enough for 750,000 homes. And this is only the beginning. OpenAI has declared it will pour $500 billion over the next four years to build a total of 10 gigawatts of AI infrastructure, a capacity rivaling the entire electricity consumption of the Netherlands.
The numbers paint a harsh picture. According to a report published by the International Energy Agency (IEA) in April 2026, global data center power consumption was estimated at roughly 415 terawatt-hours (TWh) as of 2024, accounting for 1.5% of worldwide electricity use. The IEA projects this figure will more than double to 945 TWh by 2030, approaching the total annual electricity consumption of Japan. When you isolate AI-specialized data centers, the situation grows more urgent. The IEA reported that power consumption at AI-focused data centers surged 50% in the single year of 2025, far outpacing the growth rate of conventional data centers.
A single Google search uses 0.3 watt-hours of electricity. Ask the same question to ChatGPT, and 2.9 watt-hours vanish. Nearly a tenfold difference. This is why Sam Altman asked users to stop saying 'thank you' to AI. Every time hundreds of millions of people type 'thanks' each day, the AI burns electricity generating one more response. If all 9 billion daily searches shifted to AI text queries, the added annual power consumption alone would reach 10 TWh. But text is just the starting point. Emerging AI capabilities like video generation, reasoning, and agent-based tasks consume hundreds, sometimes thousands of times more energy than text generation.
The flow of money reveals the scale of this frenzy. Capital expenditure (CAPEX) by the five biggest tech companies, Amazon, Alphabet, Meta, Microsoft, and Oracle, surpassed $400 billion in 2025. It is projected to exceed $600 billion in 2026, more than double the 2024 figure. The bulk of this money goes into AI chips, servers, and data center infrastructure. The IEA noted that these five companies' capital spending has already surpassed total global investment in oil and natural gas production. A world where more money flows into building energy-consuming infrastructure than into extracting energy itself. That is the reality of 2026.
The Brookings Institution's analysis goes a step further. By the end of 2026, global data center power consumption could reach 1,050 TWh. If data centers were a country, they would rank fifth in the world for energy consumption, slotting between Japan and Russia. The words of AI journalist Karen Hao come to mind: 'AI empires are exactly like real-world empires in their voracious consumption of resources.' We have looked away for too long from the physical cost hidden behind the convenience of the digital world.
2. The Crossover Point Between Energy Saved and Energy Consumed by AI
Kim Seo-jun, CEO of Hashed, offered this projection in his memo 'Thirty Cracks Approaching': 'Power grid AI will shave off peak demand.' Technology that predicts weather, industrial operating rates, and household power usage patterns in real time, adjusting electricity distribution second by second, has already entered the demonstration phase. Data is accumulating that optimal operation of energy storage systems (ESS) alone can cut peak demand by a significant margin. Kim estimated a 70% probability that this will become reality within three years.
AI is indeed delivering results on the energy-saving side as well. Google applied AI to its data center cooling systems and succeeded in reducing cooling power by 40%. At offshore wind farms in Denmark, AI reads weather data in real time to adjust turbine blade angles, boosting generation efficiency and stabilizing the power grid. Smart buildings, grid optimization, new materials development: AI clearly plays a role in reducing carbon emissions. The IEA itself presented results from an 'exploratory analysis' suggesting that AI could offset additional data center emissions by improving efficiency in other sectors.
The problem is whether that offset is actually happening in reality. The IEA added a sobering note in the same report: 'Sufficient momentum does not yet exist for AI adoption to realize such emissions reductions.' Expectations that AI will save energy are real, but the pathway from expectation to actual reduction remains uncertain.
The Jevons Paradox strikes precisely at this point. The 19th-century paradox where improved steam engine efficiency led to increased coal consumption is repeating itself in the age of AI. The more efficient algorithms become, the more people use them. Faster text generation leads to attempts at video generation. Once video works, people hand entire complex tasks over to agents. At each step, energy consumption jumps by orders of magnitude. As efficiency gains trigger explosive growth in usage, the savings get swallowed by the increase.
Whether AI becomes a savior that solves the climate crisis or a catalyst that accelerates energy depletion, the answer depends on the race between the speed of efficiency improvements and the speed at which AI demand expands. The trend so far shows demand winning by a wide margin. That is why the warning from Alex de Vries, data scientist at the Dutch Central Bank, remains valid: 'AI is energy-intensive, so deploying it for all kinds of tasks that don't actually need it is pointless waste.'
3. Water Consumption and Carbon Emissions: The Hidden Environmental Cost of AI Training
Data centers built in the middle of the Arizona desert gulp water as greedily as they gulp power. To cool the heat that server accelerators throw off, massive volumes of water must evaporate through cooling towers, and this water never returns to its source. The key word is evaporation. Most of the water used for cooling simply disappears into the air.
Consider the scale. Looking at Texas alone, a joint study by the Houston Advanced Research Center (HARC) and the University of Houston found that data center water consumption will reach 49 billion gallons in 2025 and 399 billion gallons by 2030. The 2030 figure is equivalent to lowering the water level of Lake Mead, America's largest reservoir, by roughly 5 meters in a single year. And that is just one state.
Google used 6.1 billion gallons (approximately 23.1 billion liters) of water in 2023 alone, comparable to the annual consumption of a small city. In The Dalles, Oregon, Google's data center accounts for 25% of the area's total water use, and its consumption tripled between 2017 and 2022. Farmers abandon their fields, households lose tap water supply, yet the data center cooling towers never stop.
Carbon emissions are equally serious. According to a paper published in December 2025 by Alex de Vries's research team at VU Amsterdam, the carbon footprint of AI systems in 2025 ranges from 32.6 million to as much as 79.7 million tons, comparable to the annual emissions of New York City. The water footprint stands at 312.5 billion to 764.6 billion liters, approaching the total volume of bottled water consumed worldwide in a year. Even these numbers are likely underestimates. The researchers noted that data center operators do not separate AI workloads from non-AI workloads in their environmental reporting, making accurate measurement impossible.
The gap between corporate promises and reality is widening. Microsoft's 2025 Environmental Sustainability Report admitted that energy usage had increased 168% since 2020 and total emissions had risen 23.4%. Google voluntarily relinquished its title as 'the first company to achieve carbon neutrality' because carbon emissions kept rising due to AI expansion and data center buildouts. An investigation by The Guardian estimated that official emissions figures reported by Google, Microsoft, Meta, and Apple could be as much as 7.62 times lower than actual levels.
When you add in the chemicals and energy consumed in semiconductor manufacturing, plus the electronic waste pouring out from decommissioned servers, the environmental cost of AI is far larger than most people realize. Behind the abundance of knowledge that AI generates, the Earth's finite resources are being consumed. It is time to face this fact squarely, through comprehensive metrics that include not just the carbon footprint but the water footprint as well.
4. How the Geographic Concentration of AI Infrastructure Strains Power Grids
In Dublin, data centers consume 79% of the city's total electricity. The numbers make it look as if the city exists so server farms can use its power, not the other way around. Across all of Ireland, data centers claim 21% of the nation's electricity, and the IEA expects this share to surge to 32% by 2026. In Virginia, data centers already take 26% of the state's total power.
The problem is that power grids were never designed to handle demand growing at this speed. Grids are built on the assumption of gradual demand changes. AI data center demand, however, is not gradual. It is explosive. In the territory managed by PJM, the American grid operator, a solar power plant must pay $100,000 per megawatt (roughly 130 million Korean won) just to connect to the grid. Building new transmission lines takes over ten years when you combine planning, government permits, community consent, and actual construction. More than 2 terawatts of renewable energy projects are waiting for grid connection permits, but historically only 15 out of every 100 have been approved.
Gartner has predicted that by 2027, 40% of existing AI data centers will face power availability problems. The possibility of AI grinding to a halt because there is not enough electricity could become reality. The North American Electric Reliability Corporation (NERC) has also warned that more than half of U.S. territory faces the risk of power supply shortfalls over the next decade. Georgia revised its ten-year power demand forecast to 17 times its previous estimate because of AI data center construction.
In this context, something paradoxical is happening. AI, the technology of the future, is reviving coal, the energy source of the past. In Kansas, Nebraska, Wisconsin, and South Carolina, coal-fired power plants slated for closure are having their operations extended. This is a choice that collides head-on with the generational imperative of addressing climate change. At the same time, interest in small modular reactors (SMRs) is surging. The SMR construction pipeline expanded from 25 gigawatts at the end of 2024 to 45 gigawatts in 2026. The decision to restart the Three Mile Island nuclear plant was a symbolic declaration of willingness to accept environmental risk for the sake of AI.
Geographic concentration is also a geopolitical risk. If data centers clustered in a specific region go down due to natural disaster or geopolitical conflict, AI services worldwide could be paralyzed simultaneously. Everyone knows that distribution is the answer. But locations where power supply is reliable and network latency is low are not abundant on this planet.
Kim Seo-jun offered an intriguing proposal on this issue: 'We need to move toward an era of distributed generation. Create an environment where companies that consume large amounts of electricity generate their own. Government should step back from controlling everything. That way, the burden on the grid eases and the system runs more smoothly.' His bold concept of connecting the power grids of South Korea and Japan through undersea cables fits this same logic. If the two countries share power via submarine cable, a market for buying and selling surplus electricity forms, and both nations can avoid spiraling into high-cost societies.
In the end, AI's energy problem cannot be solved by corporate technological innovation alone. It is a compound challenge that spans grid design, power plant siting, community consensus, and international energy cooperation. As the IEA observed in its 2026 report: 'The speed of the AI revolution is increasingly out of step with the speed of the physical, social, and economic systems that must support it.' Building a data center takes two to three years. Building the power plants and transmission lines to supply that data center takes more than ten. Until this time gap closes, what governs the pace of AI's expansion will not be code. It will be physical reality.
Kim Kyung-jin
Attorney · Former Member of the National Assembly · AI Policy Researcher
© 2026 Kim Kyung-jin. All rights reserved.