Everything in AI seems to be focused on LLMs lately. But in my opinion, powerful problem-specific models like this are even more interesting. The SOTA AI models used in weather forecasting are already outperforming the classic NWP models while being orders of magnitude more efficient (inference). Most are based on multi scale (hierarchical) Graph Neural Networks, an architecture which is not often talked about. The original Graphcast paper is worth a read if you think this is interesting: https://arxiv.org/abs/2212.12794

Maybe was this that was the last drop for Sundar.

Demis: "I have a new amazing breakthrough"

Sundar: "Great! We really need a answer to Sol and Fable"

Demis: "They are completely owned in typhoon forecasting"

This is really cool, please more of this from the AI folks! That’s way more impactful and interesting than another coding agent

I just discovered typhoon/cyclone predictions and they're insane. I get mine via https://zoom.earth (whose iPhone app is terrific).

Here's a selection from Typhoon Dolphin, currently sitting off the east coast of China.

    Dolphin continues its slow, trochoidal Z motion, generally heading westward deeper into the East China Sea. Over the past 12 hours, the system completed another cyclonic loop and has decelerated, exhibiting continued meandering prior to establishing a sustained westward track.

    The erratic motion witnessed over the past two days is attributable to a weak steering environment produced by a break in the subtropical ridge 2 over Korea, combined with the dynamics where the inner core is cocooned within a much larger parent circulation.

    While the general steering pattern is weak, a mesoscale deep-layer ridge is seen building over southern Japan.

https://zoom.earth/storms/dolphin-2026/

Here's Chan-hom, which threatens to make my birthday a windy day here in northern Japan.

    Intensity guidance is in good agreement overall. However, the JTWC forecast is placed lower than all the guidance save for Google DeepMind over the next 36 hours, before joining the consensus envelope (which peaks at 95 km/h (50 knots) at 60 hours) through the remainder of the forecast.

https://zoom.earth/storms/chan-hom-2026/

From the tagline in the article: "WeatherNext enables accurate cyclone forecasts that can give an extra day of warning. Now we are open sourcing the model."

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Next step: steering them. (As in Permutation City's "Operation Butterfly".)

> We can now generate a single 15-day forecast in less than a minute on a TPU, empowering forecasters to quickly evaluate the probability distribution of potentially devastating tail-risks.

Crazy

They should try to forecast earthquakes, that would really be a breakthrough If anything better than random comes out

Accurate weather forecasting has been one of the major achievements of the 20th and 21st century. Computing power is a central piece of this story, but it's also important to remember that the government infrastructure in place to collect ground-truth current weather data is utterly critical to these model's successes. From launching weather balloons to running global weather-monitoring satellites, the scientists and systems at NOAA/NWS (and in this case, the UK counterparts) provide critical expertise and data.

I say this because it seems that earlier announcements where industrial deep neural nets "outperformed NOAA" likely encouraged the slash-and-burn Trump administration in its gutting of critical activities and centers of expertise at NOAA. The impression that industry can predict weather better than the government agencies totally misses that the industrial models utterly rely on government data for inputs. In fact, almost all weather reports you see---weather.com, TV, etc.---are just lightly repackaged products that NOAA provides for free on weather.gov (which you can access for free without ads).

It seems especially useful for cargo ships, with better predictions they could save some fuel and be safer.

I keep relearning this every few months