elcritch
Another bigger impact might come by making regular researchers, lab workers, etc all more efficient by making data analysis more efficient or creating better tools. Possibly with less errors. That'd of course require big pharma to adopt them. We'll see on that point.
However the majority of a protein’s structure is functionally inactive. Only a relatively small region tends to be active and affect rate kinetics of various chemical reactions. Alternatively similar interactions affect the mechanical properties of other proteins.
We also know many of the pathways for most key biological processes. Of course we’re no where near simulating an entire human, however we could feasibly filter drug candidate drugs for strong affects on key biological pathways.
That’s not too different in a broad since than using surrogates like mice to test for toxicity across highly conserved pathways in metabolism, reproduction, liver function, etc. It’s a much more tractable problem as well.
We have a lot of experimental data for the effect of many proteins on important chemical pathways. Presumably drug companies have more information on how many drug candidates react with or interfere with key pathways.
My conjecture would be that we wouldn’t need fine grained interactions but that even coarse grained filtering could be valuable. As in sure drug XYZ is great for binding to serotonin but it also has high affinity to key stomach proteins. That could avoid expensive studies just to find out a promising anti-depression drug causes severe gastrointestinal issues in a lot of patients.
Even better if we could use genome variations to protein structure variations which could help differentiate between subsets of patients with or without severe side effects.
Prior to AlphaFold that was the state of predicting protein structures. That model seemingly figured out the "language" used by biology for making protein structures from DNA. It's not unreasonable to believe there's a good chance something similar exists for protein interactions.
Even if it was coarse grained but could eliminate drugs with high likelihood of unfavorable reactions it'd still be valuable. Still not sure LLMs would be ideal for that work.
Yeah so that's a lot of hidden magic and implicit logic. ¯\_(ツ)_/¯
I bought that as Zig's M.O., but then came the automatic "async/IO" colorless functions which is a huge chunk of implicit and hidden logic.
That's more implicit logic than what's in say JavaScript async AFAICT. Arbitrary int sizes with implicit casting rules, etc. More recently was the attempts of memory safety via an some sort of (optional) implicit GC mode. Personally I don't get it.
One of my goals with Merenda is to make a NextStep/Cocoa like GUI system but with a richer primitives - things like resizing corners dynamically with the shadows or adjusting gloss positions on skeuomorphic buttons.
Things web pages can do nowadays but in a NextStep like framework with rich MVC. Also with message passing but in Nim a more strongly typed language than ObjC.
An alternative evolution of Cocoa instead of SwiftUI.
The GUI library aspect is progressing well, but getting a desktop environment going is challenging even with AI. Still I want to capture things like the document workspaces and such. Lately Ive been tempted with making a postscript like display server based on GPU accelerated vector primitives.
1: https://www.fire.org/news/blogs/free-speech-dispatch/uk-teen...
Only mentioning it because LLMs are already taking over software development and everything online. Let's keep them out of our meet space peculiarities (1) while we can. ;)
Unless, or perhaps until, we figure out how to use LLMs effectively at the organization level we're still going to be limited to the "slime mold" level of organizational intelligence.
Will that happen? Most likely, but will it take a generation or ten? I suspect it'll be far harder for society to adapt at that level. We've had Mythical Man Month for decades yet software managers continue repeating the same mistakes.
Because of that the idea of OAI and Anthropic just moving to in-house product development makes sense.
Now entertainment and social media sort of fill that gap (not really) but enough that there's less people out. Less people out mean it's less entertaining, so less people go out etc.
There's plenty of devices operating in giant supercomputer networks accessible for cheap to consumers! Unfortunately the consumers in question are spam farms, bot networks, ddos rings, and other mostly criminal types.
It would be cool if similar networks could be used for good instead. Earning credits providing compute that could redeem for spurts of fast parallel compute.
Maybe that'll change in the future, but while AI can make awesome images without a good "director" they're pretty cookie-cutter and bland.
Makes sense to sow discord among your rivals. The US certainly does the same.
1: https://www.axios.com/2026/08/28/china-ai-data-center-backla... 2: https://www.telegraph.co.uk/news/2026/08/13/china-funding-pr...