Colmn)After AI Becomes Universal, People Split Into Two Paths

(Archive Record / Based on Inference, Not Prediction) This is not a prophecy. This text is an inference based on observable usage patterns as AI tools become universal. It is not written to persuade or warn. It is recorded for future verification. Published: 2026-01-13 Note: This record intentionally excludes events that have already occurred. It focuses on forward-looking inference only. Phase 1: AI Is Already Open to Everyone AI is no longer a scarce technology. Barriers of access, cost, and performance have already collapsed. At this stage, the difference is no longer who uses AI, but how it is used. Two patterns are clearly observable: People who use AI as a search replacement People who use AI as a thinking and structuring tool This is not a matter of preference. It is already becoming a difference in outcomes. Phase 2: Usage Patterns Begin to Shape Returns (2–3 Years) Those who use AI like search engines → gain minor improvements in speed. Those who use AI as a thinking tool → change the structure of decision-making itself. This difference soon appears in returns. Same information Same time Same tools Yet: One group only reduces repetitive labor The other reduces the cost of judgment itself Within 2–3 years, this gap solidifies into measurable performance differences at the individual, team, and organizational levels. Phase 3: Leadership Misinterpretation Becomes a Risk (3–5 Years) This is where the real problem begins. Many leaders frame AI like this: “If people use it, productivity will increase.” In reality, the opposite often happens. Organizations that use AI without thinking structures end up repeating wrong decisions faster. Meetings get shorter, but decisions become lighter Reports increase, but accountability diffuses Data grows, but direction weakens At this stage, if leaders do not examine their decision structures, AI becomes not a competitive edge, but a risk amplifier. Phase 4: Surface-Level Equality, Internal Polarization (After 5 Years) On the surface, everyone uses AI. It appears in resumes, organizational descriptions, and strategy documents. Internally, however, the divide is clear: Groups that use AI before judgment Groups that use AI after judgment This difference is subtle, but eventually irreversible. At that point, the issue is no longer effort, discipline, or diligence. It is a matter of how thinking itself is structured. Purpose of This Record This text is: Not a warning Not a call to action Not a demand for change It simply records a fork that is already forming. It may appear insignificant today. But with time, it will read as: “This pattern was already observable back then.” Final Line (For Archive) People understand only after the fact. Records exist to wait for that moment. 원하면 다음도 바로 가능해: Leader-only version (more direct) Technical version for experts Time-locked release notes (2-year / 5-year re-publication)

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Column)The First Divergence After AI Becomes Ubiquitous

Phase 1 — The First Divergence After AI Becomes Ubiquitous As of 2026, AI is no longer a tool reserved for a small technical elite. It has already achieved ubiquitous accessibility. At this stage, the critical change is not driven by AI’s performance, but by how people position AI within their decision-making process. Based on observation, an initial and clear divergence has emerged. 1. People Who Use AI as an Extension of Search This group interacts with AI in a familiar way: They ask a question Receive an answer Use that answer directly or in summarized form The pattern is not fundamentally different from using a search engine. AI functions as a faster search interface, while judgment remains largely intuitive and human-driven. Characteristics of this approach include: Fast responses Low cognitive load Immediate conclusions Responsibility remains fully individual This method is efficient for short-term task execution. However, the decision-making structure itself remai...
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기억, 망상, 그리고 종이 위의 글씨

LEDEUXIONS COLUMN 기억은 흔들리고, 기록은 남는다 “그때 열심히 했다”는 문장만으로는 부족한 시대가 됐다. 오늘 내가 저장한 한 장의 PDF는 2017년의 시간을 ‘설명’이 아니라 ‘사실’로 고정했다. 권태완 · 르듀종 • 기록 1 1) 기억과 망상은 생각보다 가까운 이웃이다 기억은 종종 예의 없이 사라진다. 어제까지 선명하던 장면이 오늘은 흐려지고, 누군가의 “기억 안 난다” 한 마디가 내 시간을 가볍게 만든다. 그때부터 사람은 마음속에서 작은 재판을 연다. 그 시간이 실제였는가, 내가 만들어낸 것이었는가. 그래서 나는 “기억”과 “망상”을 감정의 문제가 아니라 구조의 문제로 본다. 기억이 나를 설득하지 못하는 순간, 필요한 건 더 강한 주장(말)이 아니라 외부에 남아있는 근거(물건)다. 2) 종이 위의 글씨는 ‘체온’까지 남긴다 키보드로 쓴 문장은 수정이 쉽다. 그래서 흔들리기도 쉽다. 반면 손글씨는 수정이 어렵다. 틀리면 지우기보다, 틀린 흔적 위에 겹쳐 쓴다. 그 과정 자체가 기록이 된다. 오래된 노트의 삐뚤한 필체는 내용뿐 아니라 그때의 속도와 체온까지 남긴다. 기록은 정보를 보관하는 행위가 아니라, 시간을 고정하는 방식이다. 3) 한자(漢字)의 압축처럼, 기록은 의미를 ‘새긴다’ 한자는 압축의 언어다. 한 글자에 의미가 겹겹이 들어간다. ‘記(기록할 기)’는 단순히 적는 것이 아니라 새긴다는 감각에 가깝다. 그래서 기록은 정보가 아니라 의지가 된다. 내가 무엇을 믿었고, 무엇을 남기려 했는지의 흔적이다. 4) 한 장의 PDF가 만...
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