> ## Content Index > Fetch the complete content index at: https://www.altviewinsights.com/llms.txt > Use this file to discover other available public pages before exploring further. # [The Art of Prediction | 00 ] Prologue - URL: https://www.altviewinsights.com/publications/the-art-of-prediction-00-prologue/ - Published: 2026-08-08T12:02:42.000Z - Updated: 2026-09-04T06:17:31.000Z - Description: This book is not an abstract treatise on forecasting. It is written from the perspective of a working analyst who has spent his career analyzing and forecasting shipping and commodity markets, grounded in the real-world challenge of making decisions with incomplete information. - Author: Daejin Lee - Tags: #pub, theartofprediction, daejinlee, principles, analysts, AI, prologue, #en **The Art of Prediction** will soon be published in English. I truly appreciate all your support and look forward to sharing the book with you. SEOUL · SINGAPORE · DUBAI THE ART OF PREDICTION A field guide to the human judgment AI cannot replace · Daejin Lee --- PROLOGUE # Prologue ## How Will We Make a Living in the Age of AI? I once watched intuition beat the model at the decisive moment. In March 2021, I was preparing for a live BBC World News interview. The world was watching the *Ever Given* — the giant container ship wedged across the Suez Canal — and asking one question: when would it float free? Every model on the table dealt with the same variables — wind force, buoyancy calculations, and the friction of the seabed. I happened to mention the situation to my mother during a routine call to my parents in Korea. Without missing a beat, she said: My mother had grown up near a Korean fishing village and had read the tides her entire life. To her, the cycle of high water was not a model but an everyday intuition. Nowhere in the briefing materials I had stayed up all night preparing did the word *ahopmul* appear. Yet that single remark proved more accurate than every model — and a few days after the interview, exactly as my mother’s intuition had said, the ship floated free. \* Ahopmul: a traditional Korean fishermen’s term for the point in the lunar tidal cycle when the high tide rises to its fullest. There was nothing mystical about my mother's intuition. It had been built through decades of repeated exposure to a recognizable pattern, with feedback arriving every time the tide came in and went out. What looked like instinct was accumulated experience compressed into recognition — the very mechanism Gary Klein's research on expert decision-making describes. A model is built from data; intuition is built from data accumulated over a far longer span, outside the boundary of one's own awareness. That difference became one of the starting points for this book. ## Will There Still Be a Place for Me? These days I ask myself the same question almost daily: in the age of AI, how will we make a living? Entry-level hiring across parts of the global consulting industry has already been cut back or frozen. Only a few years after ChatGPT took the world by storm, countless AI programs have appeared — and names such as Gemini, Claude, Perplexity, and Grok have become more familiar to an analyst’s browser than the search bar itself. When you need it, AI explains a concept more clearly than most teachers, and on standardized technical work it shows expert-level depth. The information gathering and foreign-news summarizing I did so often as a junior employee, AI now does faster and more accurately; the work junior traders once absorbed over the shoulders of the head traders, AI now does better as well. The apprenticeship era — learning over the shoulder while doing the grunt work — is coming to an end. Soon, as physical AI and robotics converge, much manual labor will follow the same path. Is there really anything in which humans can stay ahead of AI? At every task — clearly not. The more important question is different: what capabilities should we — and our children — cultivate to remain useful, decide better, and survive in this uncertain world? I am not talking about extraordinary success. I am asking something more basic: when the things we have traditionally been paid to know and do can increasingly be done by machines, what will still be ours? While writing this book, I realized that I had been searching for an answer to that question all along. ## The Work That Remains After AI Over the past few years, while benefiting from AI more than almost anyone, I have found myself doing not less work but more new work. The hours I once spent collaborating and communicating with colleagues around the world — above all the data-science teams — on modeling, information gathering, and verification have dropped dramatically. Yet the range of sectors I must cover has widened, and the work of verifying and internalizing has only grown. Routine work has become easier. Judgment has not. As the burden of simple tasks has declined, more of my time has shifted toward judgments that resist standardization: noticing that something does not quite fit, questioning an apparently convincing result, connecting signals that appear unrelated, and turning those observations into a view others can understand and use. I had assumed that the faster AI processed vast information, the less work would remain for humans. That was wrong. The work has simply changed. At exactly that intersection lies **the Human Edge**. Where humans can stay ahead of tools like AI is not in sheer information throughput — that race is already lost. It is in the ability to decide what deserves attention, to read context, to doubt a result, to connect disparate signals — and, in the end, to take responsibility with one’s own judgment when no answer can be known with certainty. I call that territory **the art of prediction.** ## The Art of Prediction We cannot know the future. Yet life is a chain of predictions, and we cannot escape their consequences. Predictions are bound to be wrong. As the economist Ha-Joon Chang explains in *Economics: The User's Guide*, investment decisions depend heavily on expectations about the future — and those expectations are formed under uncertainty, including the 'unknown unknowns': the things we do not even know that we do not know. Because the unknowns we don’t even know we don’t know are so many, the future is always a region of fear and humility. But that is no reason to give up on prediction. We are always left with regret. “I should have gone with my first instinct.” “I shouldn’t have let the mood carry me.” “I should have paid more attention to that signal.” “Ah, I knew it would turn out this way…” Looking back, most regrets cluster around similar mistakes. We trusted the wrong source; we ignored an uncomfortable piece of evidence; we followed the consensus because everyone else seemed convinced; we mistook noise for signal — or dismissed a signal because it did not fit what we already believed. And when we examine those misjudgments one by one, it occurs to us that perhaps they could have been avoided — or predicted, at least, a little better. In business, almost every decision rests on some expectation of the future: companies invest because they expect demand; traders take positions because they expect prices to move; shipowners order vessels because they expect tomorrow’s freight market to justify the cost. And whether those forecasts succeed hinges not only on numbers but on human emotion, politics, and the currents of society. Everyday life is no different. When lightning flashes, we brace for the thunder to follow; as summer vacation approaches, we book flights and hotels early, expecting prices to rise. We choose careers, buy homes, invest money, change jobs, and move countries based on some view — explicit or implicit — of what will happen next. We are already predicting, judging, and acting all the time. The art of prediction is not the art of getting the future exactly right. It is the art of judging, under uncertainty, what to look at, what to doubt, and which signals to weigh more heavily — and of knowing when new evidence should force you to change your mind. From real estate, stocks, and commodities to the U.S. presidential election and Bitcoin, capable forecasters share principles that run through it all. This book is an attempt to unpack those principles in the language of the field. ## Principles Learned in the Field This book is not an abstract treatise. It is written from the practical viewpoint of a working analyst who has done actual forecasting and analysis on the front lines of shipping, shipbuilding, and the commodity trade. My career began on a frontline sales team, moved through futures trading, and led into analysis through specialized industrial consulting. My analytical work has always been rooted in execution rather than in academic economics. Many principles of prediction exist on their own — but in real work they operate all at once. So while each chapter of this book stands independently, they are ultimately interconnected. Drawing on years of mentoring research-team members at home and abroad, I have set down, one by one, the checks that must run whenever prediction and analysis are performed. Who has the power to make this decision? What do they stand to gain or lose? Which sources deserve more trust? What is missing from the data? Am I looking at a genuine signal, or simply following the crowd? What would have to be true for my forecast to be wrong? And when the evidence changes, am I willing to change my mind? The main text ranges across the real-estate market, commodity supply chains, geopolitical crises, the GIGO trap — garbage in, garbage out — in the age of AI, and how to find an honest signal inside a news flood saturated with noise. I have tried to record the failures alongside the successes. The field cases and terminology needed to understand commodity markets and the shipping-and-freight structure more deeply are gathered separately in the appendix, “Commodities Lesson 101.” Concepts essential to the main text are unpacked on the spot as far as possible, but readers curious about more industry background may read the appendix alongside. A successful forecast can easily make us overconfident; a failed forecast, examined properly, teaches far more. A prediction is not completed by a correct result alone. Only when we also examine why it was wrong, what was missed, and what question to ask next does it finally become a skill. I hope these pages will be of some use to the analysts, managers, traders, and executives who make decisions every week in the field — and to readers who want to learn the principles of investing from an analyst’s perspective. The first reader of this book was my wife. When she told me she had loved the first draft, I asked her why. "Because now I finally understand what you're actually thinking when you decide something," she said. That is what I hope this book offers every reader — a look inside an analyst's head — and her answer gave me the confidence and the courage to believe it would help. Above all, I want to thank my wife — the hidden force behind every one of these decisions, who each time believed in my possibilities before I did, and cheered them on. And to my two beloved daughters, who will live their lives alongside artificial intelligence, I dedicate this book. The future will never become fully predictable. That is precisely why learning how to predict matters. *Daejin Lee* *Dubai, Spring 2026* **PART 1** **What to Look At — Reading the Market’s Signals** --- [Contents](https://www.altviewinsights.com/publications/the-art-of-prediction/) · [Next: Follow the Interests of the Decision-Makers →](https://www.altviewinsights.com/publications/yeceugyi-gisul-weoncig-1-sijang-gyeoljeonggweonjadeulyi-iigeul-bora-3/) © 2026 Daejin Lee · *The Art of Prediction*. All rights reserved. **Contents**THE ART OF PREDICTION *PART 0* ### Foreword [00PrologueIn the age of AI — what will we make a living from?Read sample →](https://www.altviewinsights.com/publications/the-art-of-prediction-00-prologue/) *PART 1* ### Reading the Market's Signals [01Follow the InterestsIdentify the decision-makers first · Write down their gains and losses · Judge whether incentives are aligned or in conflictRead sample →](https://www.altviewinsights.com/publications/yeceugyi-gisul-weoncig-1-sijang-gyeoljeonggweonjadeulyi-iigeul-bora-3/) 02 #### Weighted Trust — GIGO in the AI Era Classify your sources — primary data, secondary citation, or AI summary? · Check the interests behind them · Ask about skin in the game 03 #### The Consensus Trap Short, medium or long term — which are you looking at? · Check second-order effects · Measure the gap between consensus and fundamentals 04 #### A Balanced Perspective Deliberately write one line of opposing data · Doubt the smoothest story first · Give methodology for numbers, ranges for language 05 #### Idea Meritocracy Separate ideas from people · Evaluate decisions apart from outcomes · Keep a live-testing journal of assumptions at decision time 06 #### Cycles — History Repeats Identify where you are in the cycle · Find the closest historical rhyme · Write down the decision that goes against everyone *PART 2* ### Tools for Thinking 07 #### Intuition as Data AI Cannot See Don't let a signal slip — write it down in one line · Ask whether it is an asset or a trap · Check it against other eyes 08 #### Being Wrong but Successful — Bayesian Thinking Record your priors · Update the moment new information arrives · Is it a one-way door or a two-way door? 09 #### Prepare for the Black Swan Cut sunk costs · Check for true diversification · Build reserves on the far side of efficiency — just-in-case 10 #### Beyond the Summary Ask for assumptions before conclusions · Trace every word and every number · Treat the absence of data as data 11 #### When Context Beats Logic Identify the cultural context · Stretch the view across 40, 50, 100 years · Measure the gap with rational judgment 12 #### It's Still a Human Call Leave data collection to AI · Check weights and adjustments with domain knowledge · Stake your own reputation *PART 3* ### Closing 13 #### Epilogue Closing notes from the desk — on turning a corner 14 #### Appendix Commodities 101 · Glossary · Notes