thomasahle

Right, real wages may go up, but if capital captures more of the growth than labor, inequality still goes up.

But I took OP to just worry about absolute growth in real wages.

It's simpler: If this stock was such a great deal that everyone would buy the it over anything else, then clearly Anthropic had set the price too low.

Training models is still not a precise science.

If you scale a model down, or switch out attention with linear attention, or introduce KV-compression, etc., you don't really know how it will affect model performance before you've trained the model.

And once you've spent millions training it, you might as well make it available if it's pareto optimal for some tasks. It also gives you more insight seeing how people use it in practice.

Why would you expect this to be a magical making money stock?

Unless you have special insight, you should assume all stocks are equally good buys.

There's not going to be an IPO at a price where everyone agree that it'll see a 30% growth over 30 years. Obviously that'd be priced in.

I don't want the government changing or rewriting our 30-year-old archived documents. But I do want them to still be able to view them as they were originally.

It used to be near impossible for Open Office to get documents 100% right when opening old files saved with Microsoft Word 95 or whatever.

But now with AI I guess you could just ask it to debug the issue and it would decompile Word in 5 minutes and pull out the fix.

Not sure what the frustrations with typography were, but I recon they're a lot easier to fix now.

Would that require the researchers to work directly from the data centers?

They've been training then for collaboration for multi agent swarms/teams. Basically that kind of training rewards the agent when the team succeeds, and they can develop their own strategies to make that happen.

This skill and behaviour seems to transfer to scenarios where the agents weren't meant to work as a team.

Fun! Would be nice if it supported articles in different languages.

I tell all my friends to get a second opinion when their dentist recommends pulling the wisdom teeth.

My experience from Denmark and Belgium is that dentists here will definitely disagree a lot.

I had one dentist tell me I needed 6 cavities filled. Let him do two and then went to another dentist who told me all my teeth were good. I've only ever had problems with the two teeth the first guy drilled.

Later a dentist wanted to drill another few teeth that were hurting. I instead got a better toothbrush and all the issues went away.

I Denmark we're talking about including dentistry in the national healthcare. I wonder if that will clean up the business or make it worse.

Explaining Tao's point in software terms: the point of pure mathematics is to build and maintain a high quality "codebase" of theory, definitions and proofs.

"Open problems are lighthouses not destinations" mean that these are currently not well explained by the theories and people should look into extending the codebase on that direction.

The current generation of math AI is not a "good citizen" in that it doesn't try to make the most elegant additions to the shared framework, but will often just rebuild everything from scratch til get to some endpoint.

Sure, we learned if the statement is true or false; but the proof can't be merged into the pure math codebase unless it's completely rebuilt. This is thankless work that humans are unlikely to want to do, and so the "solution" instead risks leaving a desolate patch of land, where existing efforts in extending the codebase lost their motivation.

As with all things AI we can't take more than a 1-3 year horizon, if even that much. Probably AI will become better at respecting and working within the existing theories as it has with large software codebases.