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 13. Generational Division and the Difficulty of Political Agreement
Artificial Intelligence and the Reshaping of Society
Chapter 13. Generational Division and the Difficulty of Political Agreement
Kim Kyung-jin
1. When One Generation Sees AI as a Tool and Another Sees It as a Threat
In April 2026, Gallup released the results of a survey of 1,572 people between the ages of 14 and 29. The data tracked shifts in how Generation Z feels about AI, and compared to a year earlier, a striking reversal had taken place. In 2025, the emotion this generation most associated with AI was anxiety, but excitement and hope followed close behind. There was balance. Tension about a new technology coexisted with anticipation of its possibilities. By the 2026 survey, anger had surged to 31%, excitement had dropped 14 percentage points to 22%, and hope had sunk to 18%.
The same Generation Z, looking at the same technology, drew a completely opposite emotional map in just one year.
One key helps make sense of this change. According to a 2025 London School of Economics (LSE) survey, 83% of Gen Z was using AI at work, compared to 73% of millennials, 60% of Gen X, and 52% of baby boomers. The generational gap in usage was roughly 30 percentage points. But OpenAI's Sam Altman captured the real nature of this gap in a single sentence at the 2025 AI Ascent event. Older people use ChatGPT as a Google replacement, he said. People in their twenties and thirties use it as a life advisor. College students use it as an operating system. Same tool, but the depth of use differs by generation. And the deeper the use, the higher the dependence, the greater the terror when it disappears or threatens to replace you.
If older generations remember the era when software came in shrink-wrapped boxes, AI feels like a standalone tool you pull out when needed. When it stops being useful, you put it back in the drawer. But for a generation that treats AI as an operating system, being told that operating system is coming for your job is a different matter entirely. In March 2026, Fortune magazine cited a report from Ireland's Department of Finance with a striking figure: between 2023 and 2025, employment among young workers fell by 20%, while employment among workers aged 30 to 59 actually rose by 12%. Researchers at Stanford University confirmed the same pattern in the United States. In the top 10% of industries with the highest AI exposure, employment among workers aged 22 to 25 had declined since 2021, while employment among older workers was growing.
Here lies the irony. The generation most skilled at using AI is the first to be pushed out by it. Boris Cherny of Anthropic, the developer of Claude Code, said the job title "software engineer" itself could disappear by the end of 2026. That title was the most basic junior position at Big Tech companies. The first rung of the ladder is being pulled out entirely.
And so the reaction of Gen Z workers has flowed in an unexpected direction. According to Fortune's April 2026 reporting, so-called "sabotage" behavior, where young employees actively obstruct their company's AI adoption, has been emerging. Thirty percent of workers who admitted to sabotage cited fear that AI would take their jobs as the reason. A November 2025 KPMG survey found that 4 in 10 workers overall feared AI would take their jobs. Yet in a bitter twist, employees who refuse AI adoption are actually more vulnerable to being fired. Sixty percent of executives said they were considering cutting staff who resist AI.
A D2L survey of 3,000 American workers reveals the texture of generational anxiety in sharper detail. Fifty-two percent of Gen Z workers said they worried about being replaced by a colleague who is better at AI, while only 33% of Gen X said the same. Gen Z's fear isn't AI itself. It's the other person who uses AI better than they do. They don't fear AI as a tool; they fear the new rules of competition being built on top of that tool. These are two entirely different kinds of dread.
A February 2026 Data for Progress survey of 1,228 voters shows the political dimension of this divide. A majority of voters over 45 viewed AI unfavorably (negative 10 percentage points), while a majority of younger voters viewed it favorably (positive 25 percentage points). The generation that uses AI the most likes it the most while also fearing it the most. The generation that barely uses AI dislikes it the most but doesn't lose sleep over it.
Seojoon Kim, CEO of Hashed, identified the bottom layer of this phenomenon precisely. "The generational divide is no longer determined by age," he said, "but by how long you lived before a particular tool appeared." The twenty-year-old developers he observed, graduates of specialized coding high schools, envy elementary school kids younger than themselves. They had to pass through at least some of the old grammar before arriving here, but those children will enter the AI era with emptier slates. A twenty-year-old envying a ten-year-old. It's funny and unsettling at the same time. Can this gap be reconciled at the ballot box?
2. The Impossibility of Policy Consensus: Same Ballot Box, Different Realities
In 1833, when Benjamin Day began selling a one-penny newspaper in New York, his purpose was to gather readers' attention and resell it to advertisers. The birth of the attention economy. Two hundred years later, that logic has returned armed with algorithms. The data analysis system PRISM fragments populations into lifestyle clusters, constructing separate consumer realities for each. People who exercise the same right to vote but live inside entirely different information systems served up by algorithms. This is why reaching common social agreements keeps getting harder.
Numerator's April 2026 generational AI consumer trends report shows what this fragmentation looks like in practice. In July 2025, 38% of millennials said they worried about AI replacing jobs. By December of the same year, the figure had jumped to 49%. In just five months, nearly half had become anxious. And yet the same millennials were using AI for meal planning, shopping list creation, and budget management more actively than any other generation. A split psyche, enjoying a technology's convenience while dreading the destruction it might bring.
To see how badly this split complicates policy consensus, follow the numbers. In a December 2025 YouGov survey, 35% of American adults said they had used AI at least once a week over the past year. Among Gen Z, 51% used it weekly, compared to 29% of Gen X and 25% of boomers. Thirty percent of the total had never used AI at all. Those 30% and the people who use AI every day must vote for the same candidates and weigh in on the same AI regulation bills.
The problem is that attitudes toward AI don't split along generational lines alone. The Data for Progress survey showed a clear gender gap as well. Men viewed AI favorably by positive 16 percentage points, while women viewed it unfavorably by negative 10. Race produced another split. White voters were unfavorable by negative 3 percentage points, but Black voters were favorable by positive 29 and Latino voters by positive 10. By party, Republicans were favorable by positive 11 percentage points, while Democrats were unfavorable by negative 3 and independents by negative 5. With gender, race, generation, and party all pulling in different directions, reaching consensus on a single question, "How should we regulate AI?", is a near-impossible task.
The wealthy enjoy public services in private spaces, while the poor find even their private lives exposed to the outside. As physical divides and digital divides overlap, more and more citizens live in the same city yet experience completely different realities. When AI personalizes search results, custom-builds news feeds, and recommendation algorithms separate the very pathways through which people consume and access information, the precondition of democracy, a set of "common facts," collapses.
Item 30 on Seojoon Kim's list of 30 fault lines foreshadows the next stage of this problem: the prediction that regulatory-forecasting agents will reshape the lobbying market. Once AI tools emerge that synthesize legislation, public statements, and opinion data to predict regulatory changes, the distance between predicting regulation and creating it shrinks. Four AI models assessed the probability of this happening within three years at only 40%, but the low probability itself doesn't measure the magnitude of the risk. If it happens even once, it could fundamentally alter how democracy functions.
The 2024 U.S. presidential election offered a glimpse of this possibility through the performance of the prediction market Polymarket. While major polls called the race a toss-up, Polymarket held Trump's odds at 65 to 67% and came far closer to the actual result. Biden's reelection probability plunged below 10% the day after the TV debate. While the press cautiously floated "fitness concerns," the market had already rendered its verdict. When the news writes the autopsy of an event, prediction markets read its EKG. If tools like these start being applied to regulatory forecasting, information asymmetry will widen to extremes. The democratic promise that those who have information and those who don't carry equal weight at the ballot box; what can that promise mean in the age of AI?
3. The Structural Mismatch Between the Speed of Technological Evolution and the Speed of Institutional Response (Policy Lag)
On March 26, 2026, the opening sentence of a report by the technology analysis firm k4i.com compressed the situation into a single line: "A fundamental shift in which the speed of silicon outpaces the speed of law." If 2024 and 2025 were years for building regulatory frameworks, 2026 is the year all the deadlines converge. The EU AI Act's compliance deadline for high-risk systems is August 2, 2026. Companies must be able to demonstrate why their AI models reached a given decision, but existing data centers were never designed to handle observability at that scale.
Demis Hassabis of Google DeepMind estimates a 50% chance that AGI arrives within 5 to 10 years. Yet institutions are still stuck debating at the level of data privacy. Technology has already moved into the execution phase before regulators can even finish their definitions. This is the essence of policy lag.
The situation in the United States makes this lag visible. An executive order signed by President Trump in December 2025 aimed to block state-level AI laws that were incompatible with the federal policy framework. But executive orders lack the legal force to preempt state law. On March 20, 2026, the White House issued a National AI Policy Framework and asked Congress to establish a single federal approach to regulating AI use. The proposal called for guardrails covering child safety, freedom of expression, intellectual property, workforce impact, and national security. But this is a framework, not a law, and Congress has to act before it becomes one.
California, meanwhile, has been running on its own track. SB 53 took effect on January 1, 2026, requiring major AI companies to disclose safety protocols and protect whistleblowers. Colorado's AI Act was scheduled to take effect on June 30, 2026, but after August 2025 amendments, some provisions were postponed. With no comprehensive federal AI regulation in place, individual states are writing their own rules. Globally, more than 72 countries have proposed over 1,000 AI-related policy initiatives.
A January 2026 analysis by Unite.AI drew a comparison to Henry Ford. When Ford developed the Model T, he didn't focus on highway safety or traffic rules. Technological innovation has always outrun regulation. But with AI, the gap is qualitatively different. Cars move through physical space, and when accidents happen, you can see them. AI's decisions occur in invisible space, and even when harm results, pinpointing the cause is difficult. Boards of directors with insufficient technical understanding are repeatedly left to manage risks they can barely grasp.
The core observation in the k4i.com report goes like this: we are moving from a world where "humans are in the loop" to a world where "humans are on the loop," and in many high-speed infrastructure cases, to a world where "humans are at the end of the report." The moment the speed of decision-making exceeds the human capacity for oversight, the very concept of regulation needs to be redefined.
The EU recognized this problem and in November 2025 released its Digital Omnibus proposal, attempting to simplify the AI Act and delay the implementation dates for high-risk systems. They wrote a regulation, then found the regulation too complex, so they had to regulate the regulation. The law is sprinting to catch up with technology, and in the process, tripping over its own feet.
The fact that the EU AI Act excludes military applications of AI from its scope entirely is another symptom of this structural mismatch. Civilian AI gets meticulous scrutiny; military AI gets an exemption. South Korea's Framework Act on Artificial Intelligence, and both the United States and China, also lack general legislation regulating military AI. The most dangerous domain is the one with the loosest regulation. A bitter paradox.
In this environment, what actually works is not law but market pressure. In k4i.com's phrasing, regulatory compliance is shifting from a cost center to a distribution advantage. Companies offering "Reg-Ready" infrastructure with transparency tools, watermarking, and audit logs pre-integrated are winning in the enterprise market. The market is doing what the law cannot. Whether this is desirable, or whether it represents a failure of democratic control, remains an open question.
4. The Challenge of International Coordination in AI Governance: Clashing National Interests
In early 2026, three completely different regulatory paths are running simultaneously for the same technology. According to an April 2026 analysis by Xiaotong Sun, a researcher at the University of Turku, the EU exports structure, the United States pushes innovation, and China tightens control. Same technology, same risks, fundamentally different solutions.
The EU's strategy is built on the 'Brussels Effect,' a method of exporting its own regulatory standards as global norms. Multinational companies must follow EU rules to access the EU market, and those rules naturally spread to other regions. Brazil, Canada, and South Korea developing frameworks inspired by the EU AI Act is proof of this.
The United States' March 2026 framework points in the opposite direction. It explicitly opposed the establishment of a new federal AI regulatory agency. As the Stanford AI Index Report (2025) notes, the continued absence of comprehensive federal AI legislation reads as a deliberate choice to preserve industry autonomy. The U.S. views both the EU's heavy regulation and China's state-led model as challenges to its own technological leadership.
China had enacted more than 50 standards in the AI sector by early 2026 and established an AI Standardization Technical Committee under the Ministry of Industry and Information Technology. A framework requiring AI systems to comply with core socialist values went into full effect in 2026. Through its 'Digital Silk Road' framework, China is expanding technological cooperation with developing countries while actively participating in UN-level initiatives.
In January 2026, the Atlantic Council analyzed eight ways AI is shaping geopolitics and summarized it this way: among the three digital powers, the United States, the EU, and China, the push to tighten control over AI infrastructure is evolving into a 'battle for the AI stack.' Computing power, cloud storage, microchips, and regulation are the key resources in this battle, and each power is taking an increasingly exclusive approach to these resources.
Meanwhile, in 2026, a UN-backed Global Dialogue on AI Governance and an independent International Scientific Panel were launched. For the first time, nearly every country had a forum to discuss AI risks, norms, and coordination mechanisms. But having a forum and having agreement are two different things.
Kim Seojun's analysis shows clearly why this international coordination is so difficult. South Korea's GDP is roughly 1.8 trillion dollars. China's is ten times that; America's is fifteen to twenty times larger. 'Just as we don't pay much attention to a country one-tenth our size, China has no reason to pay attention to us.' That holds true except in the special domain of semiconductors. Kim's diagnosis is blunt. In the WTO era, size didn't matter, but the WTO is over and it's not coming back. International rules ultimately come from power, and you need to be large enough to inflict costs on the other side before diplomacy or trade becomes possible.
His proposal is radical: South Korea-Japan economic integration. Combined, the two countries would have a GDP of roughly 6 trillion dollars, about one-third of China's. 'You need to be at least that size for the other side to take notice,' he argues. Taking the EU's inefficiency as a cautionary tale, where 27 member states make setting a single rule painfully slow, he envisions South Korea and Japan seizing the ruling core first to build an efficient bloc. Going further, he projects that once a 6-trillion-dollar bloc forms, Southeast Asian nations would naturally seek to join this economic sphere, making an Asia Union modeled on the EU possible.
Whether this proposal is realistic requires a separate discussion. But the question it raises is clear: in the international coordination of AI governance, will you remain a rule taker or become a rule maker? And to become a rule maker, what scale of economic bloc is needed?
The U.S.-China power competition will last for decades, and that competition defines the framework of AI governance. The U.S. prioritizes innovation, China prioritizes control, and the EU prioritizes norms. Between these three approaches, the remaining countries are forced to choose. The prisoner's dilemma that Mo Gawdat warned about is replaying itself at the national level. If all countries cooperated to regulate AI development, the world would be safer, but if even one country secretly loosens its regulations, that country captures a competitive advantage. This fear traps every nation in a vicious cycle where no one can be the first to tighten regulation.
There was a moment in November 2023 when 28 countries signed the Bletchley Declaration. A minimum shared understanding seemed to be forming. But the gap between declarations and implementation is wide. At the AI Action Summit held in Paris in 2025, the EU showed its determination not to remain a bystander in the global AI race, but the agreement that came out of the summit carried no legal binding force.
There is hope that AI can deliver Radical Abundance to solve shared human challenges like climate change and disease relief. But the reality is that a lack of trust between nations keeps everyone locked in a zero-sum frame. The very concept of digital sovereignty connects to the national instinct to exercise absolute control within geographic territory, much like the Roman concept of imperium.
In the end, international coordination of AI governance is not a question of technology but a question of politics. And if it is a political question, the answer must come from politics too. The problem is that the speed of politics cannot keep up with the speed of technology. This is the most dangerous structural mismatch of our time. In a world where legislation takes two years to pass, AI model performance doubles every six months. Can this gap be closed, or will it widen forever? It is too early to say. But one thing is certain: while the gap grows, the ones who suffer are always the weakest links, the smallest countries, and the youngest generations.
Kim Kyung-jin
Attorney · Former Member of the National Assembly · AI Policy Researcher
© 2026 Kim Kyung-jin. All rights reserved.