I’m not sure. Almost everyone thinks that they’ll be the ones to sell just before it all goes south. They can’t all be right of course…

But this all needs a much more inclusive and deep national conversation as the increasing risks become more obvious. Clearly this is unlikely to happen right now for… reasons. But it would still be a good idea!

Brokers always gain from higher prices (so their motivation is clear), but buyers should really benefit from better information (or indeed any information about risks), and so a responsible industry (ha!) would be perhaps a bit more nuanced. 🤷

In the absence of any consensus or federal action to update hazards mapping, this kind of retail providing of info is the only way to bring this info into the system, but this effectively means that it is current homeowners who will (partially) bear the cost (and they aren’t happy to do so).

…since risks are increasing there is an overall loss of value (relative to the mean) and it’s a real ethical and social dilemma who should be on the hook for that: The current home owner? The future one? Insurance companies? The state? The feds? The fossil fuel companies?

There are real concerns about how accurate the estimates of risk are, but that is inevitable since you are projecting out to the future in a non-stationary system. Having said that, the First Street guidance only claims to be indicative. Hiding these indicators does a disservice to the buyers. But…

Pentium chip would like to have a word…

Goes for substacks as well...

What approach are you taking to the code? OpenACC, or translation to a DSL or Julia or Jax or something else?

There are already impressive efforts in AI/ML for climate change (oops, I said the words!) and I would be shocked (maybe not surprised) if this doesn't get included in this effort in some way.

www.realclimate.org/index.php/ar...

The use of AI to describe everything from LLMs to regression on big data sets is a bit problematic. It's used by the folks creating AI slop to claim credit for ML successes based on totally different architectures & we are now at the point where normal people hear AI & think chatbot (argggh!).

Nonetheless, if the budget is there, this mission could be useful because some of the biggest roadblocks to big ML efforts is really bringing the training data onto one accessible system. Note that this will really be ML, and not based on GenAI efforts such as ChatGPT etc.

There are some downsides. The DOE is a very expensive place to do 'normal' (not national security related) science. Overhead rates that pay for the security at national labs are ~300 to 400% which means that doing something within their firewall is roughly 3 times as expensive as doing it outside.