A thousand tomorrows, and the one that breaks the grid
Rob Cirincione runs the weather a thousand times before deciding what it means. Not once. A thousand. His company, Sunairio, does not hand a trader a single forecast and a shrug. It hands them an ensemble - a swarm of plausible tomorrows - and then points at the rare, ugly ones hiding in the tail. Because in modern power markets, the average day is not the problem. The problem is the day the sun sets, solar drops off a cliff, and prices rocket toward a thousand dollars a megawatt-hour while everyone else is still looking at yesterday's chart.
That is the bet at the center of his work: the grid does not break on average. It breaks on the extremes. And the extremes are exactly what the old tools blur away.
Sunairio is a weather intelligence platform built for the people who keep the lights on and price the risk of keeping them on - energy traders, utilities, grid planners, renewable developers. Cirincione co-founded it after concluding that the off-the-shelf forecasts the entire industry leaned on were not granular enough for a grid increasingly run on wind, sun, and split-second decisions.
The premise is almost stubbornly old-fashioned for an AI company: be honest about what you don't know. A single forecast pretends to certainty it does not have. An ensemble of a thousand admits the truth - that the future is a distribution, not a point - and then does something useful with that admission. It tells you not just what is likely, but how badly things could go, and how often. For a trader sizing a position or a planner stress-testing a grid, the shape of the tail is the entire conversation.
The power market landscape has shifted from a predictable environment to a highly volatile arena.
The one percent that runs the show
Here is the number Cirincione keeps coming back to: the extreme one percent of hours can account for roughly a third of a power market's entire annual real-time value. One percent of the calendar. Thirty percent of the money - and most of the danger.
That single statistic reframes the whole game. A forecast that is accurate "on average" is worse than useless if it smooths over the handful of hours that actually matter. So Sunairio inverts the priority. Instead of optimizing for the typical day, it sharpens its aim on the rare one - the cold snap, the windless afternoon, the sunset ramp - where fortunes and grid stability are decided.
To track it, the team built the Grid Stress Index, a single readable number that folds together load, wind generation, solar generation, and thermal outages. When solar dropped at sunset and ERCOT spiked toward $1,000/MWh, the broader market did not see it coming. The Grid Stress Index did.
Why one forecast isn't enough
REAL-TIME MARKET VALUE, BY SHARE OF HOURS
Illustrative, based on figures cited by Rob Cirincione in interviews. The point: rare hours carry outsized value, so Sunairio simulates many futures rather than betting on one.
From the trading floor to first principles
Cirincione did not arrive at weather risk as an outsider. He lived inside it. He earned a BSE in engineering from Princeton, then went to MIT for a master's where he studied the effects of uncertainty on technology strategy - which, in hindsight, reads less like a thesis topic and more like a mission statement.
Then came the floor. More than twelve years developing ways to manage weather-based risk in the energy sector, with senior trading roles at Constellation Energy and Boston Energy Trading & Marketing, where he rose to Managing Director of Energy Trading. He spent those years watching weather move markets in real time, and watching good forecasts fail at precisely the moments they were needed most.
So he left to fix the input rather than keep gambling on the output. Sunairio was selected for the Techstars Alabama EnergyTech accelerator in 2021, and the company has been sharpening its edge ever since.
Traditional, off-the-shelf weather forecasts often lack the granularity required for the power sector.
Everyone trained on the same blurry archive
There has been a gold rush of AI weather models lately, each more impressive-sounding than the last. Cirincione noticed something the hype skipped over: almost all of them were trained on the same public, relatively low-resolution historical archive. Train enough models on the same blurry source and you get a chorus of models that confidently miss the same extreme events.
His answer was to stop borrowing the input. Sunairio built its own high-resolution historical weather archive, nicknamed the SHED - the Sunairio High-resolution Earth Dataset. The company started with industry-benchmark historical data, used machine learning to sharpen its resolution, then trained its forecasting on that sharper picture while baking in the way the climate itself is shifting underneath the grid.
In October 2025, that work became a product: Sunairio ONE, a next-generation grid forecast model the company describes as delivering high fidelity across every time scale - from the next hour to the next season. It is the difference between a weather report and a risk map.
1% of hours
A sliver of the year decides most of the outcome. Sunairio's entire approach is built to see that sliver early - which is why it runs a thousand scenarios instead of issuing one tidy guess.
Watch net demand, not demand
Cirincione's advice to modern traders is deceptively simple and quietly radical: stop watching demand. Watch net demand - what's left after wind and solar have done their unpredictable thing. On a grid where renewables swing wildly with the weather, the gap between the two is where the risk now lives.
It is a worldview shaped by two forces colliding at once: AI-driven load growth pushing demand up, and accelerating renewable adoption making supply jumpier. Both make weather more consequential, not less. In early 2025 the company rolled out solar-oriented software for site-specific weather and variability scenarios, and Cirincione has carried the message onto industry stages and podcasts, including Norton Rose Fulbright's project finance series in February 2026, arguing for a rethink of how the industry forecasts in an uncertain climate.
The throughline, from MIT thesis to trading desk to startup, is uncertainty - not as something to eliminate, but as something to measure, price, and respect. He went to school to study it. Now he sells the cure for pretending it isn't there.
The extreme 1% of hours can account for a third of a market's annual real-time value.
Traders must now monitor net demand, not demand alone.
The power market landscape has shifted from a predictable environment to a highly volatile arena.
Traditional, off-the-shelf weather forecasts often lack the granularity required for the power sector.
The flagship model trains on a proprietary archive nicknamed the SHED - Sunairio High-resolution Earth Dataset.
Sunairio runs roughly a thousand simulated scenarios instead of issuing a single forecast.
The name fuses sun and air - fitting for a company built on the elements that move the grid.
He went to MIT to study uncertainty itself, then built an entire business out of taming it.
A team fluent in four languages: energy, markets, climate, AI
Cirincione is careful to point out that Sunairio is not a one-man show. The company describes its team as experts dedicated to understanding energy, markets, climate, and artificial intelligence - four fields that rarely sit at the same table, let alone build the same product. That blend is the whole point. Weather risk in power markets lives in the seams between disciplines, and the seams are exactly where most tools fall apart.
The bench reflects it. Eric Hewitt, the chief technology officer, brings more than fifteen years in analytics, data engineering, and data science, having previously led software and data teams at the weather analytics company Understory. Raiden Hasegawa, director of data science, is a former senior data scientist at Google with a PhD in statistics from the University of Pennsylvania. Patrick Hawbecker, a senior data scientist, is an atmospheric scientist with a PhD from North Carolina State and a focus on renewable energy meteorology and machine learning. It is a roster assembled to argue with the weather from several directions at once.
On camera
Rob Cirincione sat down for a citybiz interview about Sunairio, the energy market, and the entrepreneurial journey behind the company. Watch the conversation on YouTube →