The race to get artificial intelligence to market has raised the risk of a Hindenburg-style disaster that shatters global confidence in the technology, a leading researcher has warned.
Michael Wooldridge, a professor of AI at Oxford University, said the danger arose from the immense commercial pressures that technology firms were under to release new AI tools, with companies desperate to win customers before the products’ capabilities and potential flaws are fully understood.
The surge in AI chatbots with guardrails that are easily bypassed showed how commercial incentives were prioritised over more cautious development and safety testing, he said.
“It’s the classic technology scenario,” he said. “You’ve got a technology that’s very, very promising, but not as rigorously tested as you would like it to be, and the commercial pressure behind it is unbearable.”
Wooldridge, who will deliver the Royal Society’s Michael Faraday prize lecture on Wednesday evening, titled “This is not the AI we were promised”, said a Hindenburg moment was “very plausible” as companies rushed to deploy more advanced AI tools.
The Hindenburg, a 245-metre airship that made round trips across the Atlantic, was preparing to land in New Jersey in 1937 when it burst into flames, killing 36 crew, passengers and ground staff. The inferno was caused by a spark that ignited the 200,000 cubic metres of hydrogen that kept the airship aloft.
“The Hindenburg disaster destroyed global interest in airships; it was a dead technology from that point on, and a similar moment is a real risk for AI,” Wooldridge said. Because AI is embedded in so many systems, a major incident could strike almost any sector.
The scenarios Wooldridge imagines include a deadly software update for self-driving cars, an AI-powered hack that grounds global airlines, or a Barings bank-style collapse of a major company, triggered by AI doing something stupid. “These are very, very plausible scenarios,” he said. “There are all sorts of ways AI could very publicly go wrong.”
Despite the concerns, Wooldridge said he did not intend to attack modern AI. His starting point is the gap between what researchers expected and what has emerged. Many experts anticipated AI that computed solutions to problems and provided answers that were sound and complete. “Contemporary AI is neither sound nor complete: it’s very, very approximate,” he said.
This arises because large language models, which underpin today’s AI chatbots, rattle out answers by predicting the next word, or part of a word, based on probability distributions learned in training. It leads to AIs with jagged capabilities: incredibly effective at some tasks, yet terrible at others.
The problem, Wooldridge said, was that AI chatbots failed in unpredictable ways and had no idea when they were wrong, but were designed to provide confident answers regardless….
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