If a market researcher wants to know how people will respond to a new product, or a political pollster wants to know how people will vote in an election, the models will give plausible answers. That’s what they do, but the answers will only be plausible, no more. To the extent that the situation were are asking about is novel, exactly when we might gain from accurate predictions, they will be useless.

There’s more in the talk I haven’t covered, it really is an excellent introduction to both the limitations of LLMs and some deeper issues about why psychological science is so hard. Recommended.

Dokyun Lee's talk on why LLMs fail at predicting human behavior with a great write-up from tomstafford.substack.com

To that end, here’s an even more interesting question around distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here?

In fact, this paradox is the solution. I believe that open weight models are good for innovation (and, per the above, I think that labs on the frontier will be fine), but it’s a problem to be dependent on China. The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation, for U.S. companies at a minimum. Stopping distillation — which is literally just querying the API — is nearly impossible; the U.S. should go the other way and lean into a new copyright policy that both indemnifies the labs and also guarantees that what they learned fuels further innovation for everyone else.

Stratechery on Chinese AI models. Good points all around on R&D costs versus COGs, distillation and open source in general.

NYC has been using aerial photography for over 100 years to catalog and measure the city. Thanks to some superb data preservation, you can explore some amazing aerial photography data going back to 1924.

BPD covers NYC's building footprints dataset updated weekly and covering 1M+ structures. Garages make up about a fifth of NYC buildings.

The cost of DNA sequencing has fallen faster than Moore's Law. Since 2001, the National Human Genome Research Institute (NHGRI) has tracked costs at its funded sequencing centers — from $95 million per genome in 2001 to around $500 today.

Genome sequencing costs fell 190,000-fold since 2001. Data here stops at 2023 and I can't seem to find reliable sources since then.