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NewsStructural4 min readDisconfirmAI Editorial

Aviation Journalist Jeff Wise: The 'More Efficient' the AI Air Traffic Control System, the Thinner the Safety Redundancy May Become

In a long-form WIRED article, Jeff Wise argues that the US Federal Aviation Administration (FAA) is reducing its target number of air traffic controllers from 14,633 to 12,563 by citing AI tools. However, deterministic neural networks may produce dangerous outputs in novel situations not encountered during training, creating a paradox where using AI to solve labor shortages actually requires more human oversight.

2026-11-14 19:00 GMT+8

Air traffic control is undergoing a paradox: using AI to address staffing shortages may create new risks that require even more personnel to mitigate. Aviation journalist Jeff Wise traces this logical chain in a long-form investigation for WIRED. In May 2026, the US Federal Aviation Administration (FAA) lowered its target for active controllers from 14,633—set in 2024—to 12,563, citing "modern staffing models and scheduling tools." In September of the same year, the FAA deployed the SMART system, developed by Boston-based company Air Space Intelligence—a cloud platform that uses AI to assess flight schedules, weather, airport capacity, and airspace conditions to predict traffic flows and identify conflicts in advance. The contract totals $875 million over 12 years.

Wise points out that this logic is particularly dangerous in aviation. The industry has long been technologically conservative, not because it is backward, but because when human lives are at stake, time-tested reliability is preferable to cutting-edge unpredictability. Mary Schiavo, former Inspector General of the US Department of Transportation and an aviation lawyer, states plainly: certification standards and airworthiness requirements prove and demonstrate safety, which usually means incremental rather than radical change. Yet AI is penetrating the sector at an unprecedented pace. In September 2026, Joby Aviation completed a 3,200-mile cross-state autonomous flight—from Concord, California, to Dare County, North Carolina—navigating busy airspace and dodging storms without any human touching the controls. A safety pilot remained on board throughout, but never needed to intervene.

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The technical risks are more subtle than they appear. Current research focuses on deterministic neural networks—models with frozen weights after training, which do not produce "hallucinations" like large language models. Wise cites a warning from Darren Cofer, a researcher at Collins Aerospace, in a 2021 paper: when such systems encounter situations not present in their training data, they generate "unexpected (and potentially dangerous) outputs." This is not a hallucination problem, but an out-of-distribution generalization issue—the model does not admit ignorance in scenarios it has "never seen," but instead provides uncalibrated outputs.

Political pressure is accelerating this process. A recently retired FAA employee, speaking anonymously to Wise, said: "We're introducing all this new technology to improve the system, and it's going to be a disaster. It's literally a Pandora's box we will come to regret." He attributes this to the "Trump/DOGE/Musk effect"—pushing through new technologies without fully understanding the complexity of the national airspace system. On October 13, nearly two dozen Democratic members of Congress wrote to the US Government Accountability Office (GAO), requesting a review of SMART due to concerns that it might "hallucinate safety-critical information."

On November 13 (Friday), the FAA issued a nationwide ground stop order, instantly throwing global airline schedules into chaos. The US government quickly stated there was no evidence of a cyberattack and no indication that SMART was linked to the communication failure. However, when asked if he could rule out an AI failure, US Secretary of Homeland Security Markwayne Mullin replied only: "There’s nothing certain right now."

Two parallel systems will emerge in the future: one driven by human decision-making, the other by AI—in selected corridors near drone hubs and eVTOL vertiports. Michael McCormick, a professor of air traffic management at Embry-Riddle Aeronautical University, explains why traditional ATC cannot cope: "If you try to handle that volume and complexity of aircraft movements, the National Airspace System would be overwhelmed." But Wise’s conclusion is cautious: any computer system can be hacked; any AI system creative enough to devise novel solutions could also hallucinate crucial details. Without a human in the loop, seemingly obvious errors might go unnoticed until it is too late.

Wise documents a unfolding causal chain: the promise of AI efficiency is used to justify workforce reductions, which in turn weaken the oversight capacity needed to guard against AI failure modes. He quotes the retired FAA employee as the underlying tone of the entire piece—"We're introducing all this new technology to improve the system, and it's going to be a disaster"—and notes that in aviation, "efficiency" calculations can never look solely at labor costs: when a single error can endanger dozens of lives, redundancy is not waste, but safety itself.

Sources

  • wired.comWIRED↗
  • techcrunch.comTechCrunch↗
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