More than a week before the Eaton Fire, Ariel Cohen already knew something was building.
Cohen is the National Weather Service’s (NWS) chief meteorologist for the Los Angeles office. In the days before January 7, 2025, she and her team watched the signals stack up in the weather models, these were the kind of pattern that, in her own words, pointed to “the possibility of a highly volatile fire weather event.” Their confidence didn’t spike overnight. It built, day by day, as the models converged on the same answer:
A historic Santa Ana wind event was coming, into a landscape that hadn’t seen meaningful rain in eight months.
By January 6, that confidence level had crossed a threshold that the NWS reserves for its most extreme warnings. Los Angeles was placed under a Particularly Dangerous Situation Red Flag Warning (PDS), this was an alert type used only a handful of times in the four years since it was introduced for Southern California fire weather.
NWS meteorologists describe it, plainly, as “about as bad as it gets.”
Southern California Edison’s (SCE) transmission circuits running through Eaton Canyon stayed energized anyway.
On the evening of January 7 a fire ignited in the canyon. Driven by the wind event Cohen had been tracking for a week, with wind gusting up to 100 mph, it tore into the town of Altadena, killing 19 people and destroying more than 9,400 structures.
It stands as one of the most destructive fires in California history.
Two signals, one decision
Cohen’s warning wasn’t a guess. It was the product of a week of rising model confidence, escalating through the NWS’s own alert hierarchy – from watch, to warning, to PDS – precisely because that escalation is designed to tell downstream decision-makers ..
This one is different.
Utilities like SCE run their own internal risk-scoring models to help decide when to call a Public Safety Power Shutoff, inputs include fuel moisture, live weather feeds, and vegetation conditions circuit by circuit. Those models exist because de-energizing a canyon full of transmission lines is expensive and disruptive, in other words not a call to make on every red flag day. Based on the output from its model –
SCE did not shut off the Eaton Canyon circuits, despite Cohen’s warning already being in effect.
The cause of the fire has not been formally determined. SCE’s own investigators, along with CEO Pedro Pizarro, have pointed to a working theory that an idle, decommissioned line in the canyon may have become unexpectedly energized, in other words not a fault on one of the active lines. Multiple lawsuits, including one from the Department of Justice, allege SCE equipment caused the fire; SCE hasn’t conceded liability, and the county’s investigation is ongoing.
The exposure is already real on the balance sheet. Edison International has recorded roughly a billion dollars in Eaton Fire liabilities through settlements and its compensation program, and Jefferies equity analysts estimate SCE’s total potential liability could run as high as $13.5 billion.
So was this a forecasting problem?
Not on Cohen’s side. Her team’s forecast held. The confidence she flagged a week out proved correct almost to the hour.
If there was a forecasting problem, it sat inside the localized, circuit-level models utilities like SC Edison use to decide when a Public Safety Power Shutoff is warranted. Canyons are brutal environments for this kind of statistical model: they funnel and accelerate wind in ways that are very hard to capture at the resolution most operational risk-scoring runs at.
That’s a real, and very difficult technical limitation. If we stop the story here we would come to the conclusion that a decision failure was caused by a technology failure ..
.. and that is the trap.
Whatever SCE’s internal model said that night, the NWS had already issued an independent, human-legible signal that conditions were historically dangerous; one of the rarest alert types NWS issues, for a reason. Two signals existed. One of them was a regional warning designed explicitly to say “we don’t need higher resolution to know this is bad.” The other was a circuit-level risk score.
The Illusion of Compliance: Guardrails vs. a Spine
In the wake of disasters like this, the corporate reflex is predictable:
throw more money at the data.
Update the sensors, increase the compute, polish the predictive mirror. That is an optimization trap which treats a structural decision failure as a technology problem.
When organizations try to handle these gaps, they default to standard corporate governance: risk committees, ethics charters, vendor audit checklists, in other words expensive administrative workflows designed to ensure “compliance” on paper. But there’s a distinction heavy industry keeps missing:
Governance gives a system guardrails. A decision architecture gives it a spine.
Guardrails only activate after a number crosses a preset line – the model provides a score of 10 for example which activates a specific rule – they’re reactive, built to catch problems after the fact. A spine determines behavior before the crisis hits. It establishes who holds ultimate authority, what specific contextual signals they’re empowered to act on, and what organizational protection exists when a human commander decides to shut down an asset against a model’s recommendation; including a model built in-house.
The “Decide Who Decides” Test
The fix isn’t a more complex model. It’s a step that has to happen before the system ever goes live. Run every core operational workflow through a simple, binary test:
Is this a Measurement Problem? (The Machine Owns It) – If a question has a single, definitive, verifiable answer given good data, the machine should settle it. Calling an offside in football or measuring real-time voltage frequency on a wire are math problems. Autonomy belongs here.
Is this a Judgment Problem? (The Human Commands It) – If a question involves chaotic variables, shifting physical conditions, and asymmetric risk, where two reasonable experts looking at the same data could honestly disagree, it’s a judgment call.
An internal model’s risk score against a regional National Weather Service PDS warning, in a canyon known to funnel Santa Ana winds unpredictably, was a judgment call.
Nothing in the public record shows SCE had a structured doctrine for resolving that conflict. That means the system defaulted to treating a model output as sufficient on its own, rather than as one input a human was empowered to override.
That is backwards.
The Moment of Design
Control room operators, incident commanders, and risk underwriters are being handed sophisticated predictive models and left to manage the handoff under running clocks.
The centre of control doesn’t live at the moment of crisis. It lives at the moment of design.
As regulation, see Article 14 of the EU AI Act as one example, and tort litigation start closing the door on corporate deniability, executives won’t be able to point to an automated threshold as the reason a decision got made.
The organizations that get this right won’t be the ones with the most expensive models. They’ll be the ones with the discipline to decide, in advance, what happens when a chief meteorologist and their algorithm disagree .. before a jury has to ask why nobody did.
Has your organisation sorted which decisions are measurement and which are judgment — or built oversight on top of a map nobody drew? If you’re not sure, that’s usually the answer.
I write Decision Layer Weekly for people working on exactly this gap: Subscribe here. And if it’s live for you right now, reach me directly: mattsheehan@spatialnext.io
Matt Sheehan
Matt is an AI strategist with 25 years at the intersection of geospatial intelligence and decision-making. He maps the architecture connecting three layers most organisations haven’t yet seen together: the sensing layer the geospatial industry has built, the causal reasoning layer now arriving, and the human decision layer nobody is designing. The third layer is where most deployments fail. And where Matt has his primary focus.