Why is P(Doom) such a bad argument?

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The Ghost in the Equation: Why "P(Doom)" is a Mirage

Imagine being told there is a precise 12.7% chance you will get into your dream college, or a 38.2% chance of rain on a day with no clouds. In the world of Artificial Intelligence, scientists and philosophers frequently debate **P(Doom)**—the probability that advanced AI will eventually wipe out humanity. But here is the catch: this seemingly scientific number is actually a mathematical illusion. ## The Illusion of Precision In statistics, a true probability requires a track record. We know the chance of a coin flip landing on heads is 50% because we have flipped coins millions of times. This is called *frequentist probability*. With AI doom, we are dealing with a one-time, future event that has never happened before. Therefore, P(Doom) is not a calculation; it is a subjective guess disguised as math. As prominent AI researcher [Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun), Chief AI Scientist at Meta, has pointed out, calculating P(Doom) before we even have Human-Level AI is like calculating the probability of a boiler explosion before the steam engine was even invented. ## The Psychological Trap of "Mathiness" By assigning a specific percentage to our fears, we fall into a trap that economists call "mathiness"—using the language of mathematics to make unproven ideas look like hard science. When people state a P(Doom) of 10% or 50%, they are not using a formula. They are expressing a gut feeling of anxiety. Renowned AI critic and researcher [Margaret Mitchell](https://en.wikipedia.org/wiki/Margaret_Mitchell_(scientist)) has argued that focusing on sci-fi scenarios of human extinction distracts us from the very real, measurable harms AI is causing right now, such as algorithmic bias and copyright theft. In his book [*The Precipice: Existential Risk and the Future of Humanity*](https://en.wikipedia.org/wiki/The_Precipice:_Existential_Risk_and_the_Future_of_Humanity), philosopher Toby Ord discusses how we estimate these risks: > "These subjective probabilities are not assertions of dogmatic certainty... They represent our best estimate of the strength of the evidence, given everything we know." However, when "everything we know" is based on speculation rather than data, the resulting percentage is more like creative writing than science. P(Doom) treats a complex, human-controlled future as if it were a roll of the dice, ignoring our own ability to regulate, program, and shape how AI develops. ## Keep Exploring To dive deeper into how we predict the future of technology, consider these questions: 1. If P(Doom) is a bad metric, how *should* we measure and prepare for the long-term risks of technology? 2. How does the psychological phenomenon of [automation bias](https://en.wikipedia.org/wiki/Automation_bias)—our tendency to trust automated systems—make us more likely to believe flawed mathematical predictions? 3. If we shift our focus away from extinction-level events, what are the most urgent AI ethical challenges we need to solve today?

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