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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteAI predictions are not proof of what will happen. They are estimates that can influence people’s choices—and, in some cases, help shape the outcome. In her essay “From Tea Leaves to AI: Why Today’s High-Tech Predictions Are So Dangerous,” Carissa Véliz argues that forecasts should be judged not only by how they are produced, but also by who controls them, what decisions they affect and who bears the cost when they are wrong.
What Véliz means by treating AI like a fortune teller
Véliz opens with a classroom anecdote about an executive who uses chatbots as “fortune tellers.” One participant reportedly said a chatbot predicted a 2% rise in the stock market. The anecdote illustrates how a person may treat an AI response as a forecast; it does not establish that the system can reliably predict markets. The account gives no market, date or verification details.
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The distinction matters because a fluent answer can sound more certain than its basis warrants. A chatbot may generate a prediction in response to a prompt, but that output alone is not evidence that the predicted event will occur. The essay’s warning is about how people interpret and use predictions, not proof that every forecast is baseless or useless.
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Véliz argues that forecasts can shape expectations and decisions. Someone who believes a prediction may change what they buy, how they vote, whom they hire or where they invest. Those actions can then affect the eventual outcome, making a forecast partly performative rather than a detached description.
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This is different from saying that all forecasts bring about what they predict. A prediction can be wrong, ignored or outweighed by other forces. The useful point is that a forecast may become part of the conditions it claims merely to describe, especially when people or institutions act on it.
Machine learning makes predictions from patterns
The essay broadly frames tasks such as translation, image classification and language generation as prediction based on patterns learned from prior examples. That is a useful way to understand why machine-learning outputs can resemble forecasts: systems use information from data to produce an estimate or response for a new input. It is not a complete technical definition of machine learning, nor does it mean that every output predicts a future event.
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Véliz invokes Laplace’s demon as an image of the aspiration to eliminate uncertainty with complete information and computation. It is a historical thought experiment, not a realistic forecast about what modern AI can achieve. The essay uses it to question the desire to make the future fully calculable.
Why prediction is also a question of power
Véliz’s central argument is that predictive capacity is connected to power. Collecting data and computing resources can enable prediction; systems built from those resources can, in turn, extend the influence of the people and institutions that deploy them. A forecast used in a consequential decision is not just a number: it can affect people who may have little say in how the system works or how its output is interpreted.
That is an argument about the relationship between prediction and authority, not a claim that every predictive system has the same impact. To assess a specific forecast, consider what data and assumptions support it, who controls the system, what decision the output informs, and whether people affected can challenge or correct that decision.
Prediction markets and the stakes of turning events into bets
Véliz points to Polymarket as an example of prediction becoming an industry. She criticizes betting on political instability, disasters and human suffering, arguing that it can turn consequential events into spectacle. This is her ethical criticism; the essay does not establish the platform’s current market listings or verify a dated example.
The text also gives a 58% example for users expecting the Oklahoma City Thunder to win an NBA championship. It is an illustration in the essay, not a verified current probability or independently checked market record. The broader issue Véliz raises is how assigning odds can frame events—and the people affected by them—as objects of speculation.
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A practical way to examine a confident forecast
When an AI system, analyst or market presents a confident prediction, ask four questions. This checklist is a practical inference from Véliz’s argument, not a formal framework she names:
- Who made it? Identify the person or institution behind the forecast, and who can act on it.
- What supports it? Look for the data, assumptions and uncertainty behind the stated outcome. A confident tone is not a substitute for evidence.
- Whose interests does it serve? Consider who benefits from people believing or acting on the prediction, and who may be overlooked.
- What happens if people rely on it? Examine which decisions it could influence, who bears the consequences if it is wrong, and whether affected people can contest the decision.
What the essay establishes—and what it does not
The piece is commentary by Carissa Véliz, presented as an excerpt from her book Prophecy: Prediction, Power, and the Fight for the Future, from Ancient Oracles to AI. A repost identifies it as a CNET Alt View guest column dated April 23, 2026, and attributes the book to Doubleday. The available text is reproduced on reposting sites rather than an original CNET page, so the arguments should be understood as Véliz’s analysis, not independent verification of every claim.
The essay also attributes a remark about machine-learning progress to Oxford AI professor Michael Wooldridge, speaking to Véliz’s MBA students: “What’s disappointing,” said Michael Wooldridge, professor of AI at Oxford, to a group of my MBA students, “is that it didn’t happen as a result of a scientific breakthrough.” The reproduced text does not provide an interview date or an independently accessible transcript, so the quotation is best read in the context in which Véliz presents it.
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