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“How do you use artificial intelligence?” I asked a class full of executives. Some answers you’ve heard before: health professionals use them to read medical images; Managers use it to draft emails; A retail company used it to take notes in meetings before abandoning it when it realized the AI was getting confused and had no understanding of context. And then, gem. There’s almost always a gem.
“I use chatbots as a fortune teller,” said a middle-aged Asian woman wearing a beige jacket and white sneakers. She later learned that she had built a multi-billion dollar empire. A nervous rustling spreads throughout the room as people shift uncomfortably in their seats. “Just like we used to read tea leaves, you can ask AI about the future, and it can be surprisingly accurate. For example, it recently correctly predicted a 2% rise in the stock market,” the student said, shaking her head and looking around the room while her classmates avoided eye contact.
Today’s ruling fortune tellers are no longer astrologers, astronomers, sociologists, or even economists; They are computer scientists, data analysts, and engineers. Algorithms are the new tea leaves, animal guts, and stars through which we hope to glimpse the future.
We tend to associate predictions with knowledge, but often they are closer to the realm of power. Prophecies are the boxing ring in which fights take place about the future. Our expectations cause the social world to tend towards our expectations. When someone predicts that the world will be a certain way, he commands others to obey his desires and make that world happen. Although we have been using predictions for thousands of years to make some of the most important decisions in our lives, we have not given much thought to the deeper questions about prophecy. Thousands of books have been written about how to forecast, but none have been written about the ethics of forecasting.
Forecasting has become a major industry. Take, for example, platforms like Polymarket, which collect public predictions about future events, collect massive amounts of data and create influence. If 58% of users think the Oklahoma City Thunder will win the NBA Championship, why bet against the majority? But betting on these platforms extends much further sports Or even reality TV. It has turned political instability, natural disasters and human suffering into spectacle, dehumanizing the real victims and making life a game.
today, Predictions They have evolved into weapons of power that justify value-laden decisions under the pretext of facts, but expectations are never facts. Facts belong to the present and the past. An affirmation of the future can be many things – an estimate, a wish, a warning – but never a reality.
What makes the future the future is that it has not happened yet. What does not happen does not exist, and there are no facts about what does not exist. However, we are using forecasting more than ever with artificial intelligence, prediction markets and experts talking about the future.
Imagination defeats uncertainty
Pierre-Simon Laplace He had a dream, often referred to as Laplace’s Devil. It occurred to him that with enough data and calculations, it would be possible to achieve complete knowledge. If you knew the exact position and momentum of every particle in the universe, as well as all the laws of nature, you would be able to predict the future with complete accuracy. Uncertainty will eventually be defeated. As Laplace said:
If for one moment an intelligence were given which could comprehend all the forces by which nature is moved and the particular situation of the beings of which it is composed—an intelligence sufficiently broad to subject these data to analysis—it would include in the same formula the motions of the greatest bodies in the universe and the motions of the lightest atom; Because nothing will be uncertain and the future, as well as the past, will be present in his eyes.
AI proponents may not put it in those words, but what they seem to be suggesting when they enthuse about the power of machine learning combined with massive amounts of data is that these technologies are bringing us excitingly closer to realizing Laplace’s Demon. If we could collect every data point, the idea goes, and we could build enough computing to analyze that data, we could predict what was previously unpredictable. Such predictive power promises to revolutionize all areas of knowledge, from medicine to climate change and politics.
Based on this fantasy, measuring devices track your every move; Comprehensively recording, tabulating and analyzing your pleasures and vices; Torture your data until you scream in confession. You are tracked while driving, searching online, exercising, having sex, drinking alcohol, using drugs, traveling, sleeping, talking to your friends and family, spending time on social media, going to the doctor’s office, playing online games, reading, watching TV and breathing.
We manage and discuss our fears in quantitative terms: the possibility of cancer, of being robbed, of earthquakes, of another pandemic, of climate change that makes our world uninhabitable, of another world war.
The unbridled optimism to overcome uncertainty through artificial intelligence is understandable. Computers, data, and statistics have made amazing breakthroughs. the Computer bomb Breaking the Nazi puzzle code. In medicine, regression analysis has been effective in identifying risk factors for diseases. Mainframe computers provided new insights into business; Centralized data processing brought real-time transaction processing and scalability. Manufacturing companies have gained the ability to monitor production efficiency across entire supply chains, identify bottlenecks and optimize resource allocation.
Personal computers appeared in the 1980s. The 1990s and 2000s saw the advent of the Internet and cloud computing, which increased the availability of data and processing power. The 2000s marked a turning point with the practical application of deep learning, supported by big data and improved hardware such as graphics processing units. Advances in algorithms have paved the way for machine learning – prediction machines.
Artificial Intelligence and Prediction: The Power Game
With prophecy comes all the types of prophecy and power that blanket our history books. The difference is that AI is prediction on steroids, and we use it not just on the battlefield and in the doctor’s office but everywhere, from the office to the classroom, the courtroom, our roads, our love lives and beyond.
Machine learning algorithms are predictive machines. And that is all they do, whether they are engaged in regression, classification, or language. When a machine learning system translates text, it predicts the most likely translation based on millions of examples of previous translations. When it recognizes wolves in images, it does so by predicting the likelihood that a given image will contain a wolf, based on patterns it has learned from thousands of wolf- and non-wolf-tagged images. When a large linguistic model answers a question, it predicts what a human would say instead, based on statistical analysis of books, online forums, social media, and so on.
No wonder “oracle” is a technical term in the context of machine learning. Oracle represents the best possible performance that can be achieved; It is a perfect function that always provides perfect predictions.
The victory of machine learning is much more a corporate victory than a scientific one. Idealists may find it anticlimactic, even depressing. Someone who wants to put it bluntly might say that we simply threw money at the problem.
What’s most remarkable about the success of machine learning is how natural it appears. “The disappointing thing is that it didn’t happen as a result of a scientific breakthrough,” Michael Wooldridge, professor of artificial intelligence at the University of Oxford, told a group of MBA students. He looked around the room to make sure the weight of his words had eased.
From the 1960s until the early 2000s, the results of neural networks were not impressive. the Symbolic artificial intelligence The gang was winning the race and the grants – until it wasn’t. Something changed: we got more data and more computing, and machine learning took off. Within a few years, Machine translationfor example, went from being unusable to being understandable, and then good enough to help clueless tourists find their way without knowing the local language. Now it’s good to admit that I sometimes preferred machine translation to the suggestions of a professional translator who had a weakness for verbosity.
The amazing things machine learning can do did not happen because of greater understanding. It didn’t need any genius. The picture is even bleaker than an uninspiring lack of creativity. The means by which this brute force of data and computation was obtained theftExploitation of the weak, a Aggressive use of natural resources And constructive Mass surveillance structureFor example, but not limited to some sins.
We may be centuries away from the fortune tellers and astrologers who predated algorithms, but prediction is still mostly about power. Power is how you get predictive algorithms, and more power is what they give you in return.
From Prophecy: Foretelling, Power, and the Fight for the Future, from Ancient Seers to Artificial Intelligence by Carissa Velez. Reprinted with permission from Doubleday, an imprint of Knopf Doubleday Publishing Group, a division of Penguin Random House LLC. Copyright © 2026 by Carissa Velez.
