Let's say Alice buys 100 shares of Microsoft stock for $100. Then Microsoft implements a new management style that makes them much more effective, doubling the stock price. For emphasis on the new price, Bob then buys 1 share of Microsoft stock for $2. Alice's shares are now worth $200, but the extra $100 doesn't seem to have come from someone's transactions. This $100 would conventionally be considered capital owned by Alice, but the actual substance of this capital is purely based on the new management style of Microsoft, rather than Microsoft's assets.

That may apply to bonds (am not familiar with that), but I don't think double entry accounting is used to decide the value of stocks?

The distributivity property is closely related to multiplication being repeated addition. If you break one of the numbers apart into a sum of 1s and then distribute over the sum, you get repeated addition.

When economists talk about “capital assets”, they mean things like roads, buildings and machines. When I read through a company’s annual reports, lots of their assets are instead things like stocks and bonds, short-term debt, and other “financial” assets - i.e. claims on other people’s stuff. In theory, for every financial asset, there’s a financial liability somewhere. For every bond asset, there’s some payer for whom that bond is a liability. Across the economy, they all add up to zero. What’s left is the economists’ notion of capital, the nonfinancial assets: the roads, buildings, machines and so forth.

Can't stocks be worth a lot due to the profitable positive interaction between different things the company owns and rents, rather than due to their individual value? I'd think companies like Microsoft are to a substantial degree valuable because they've hired employees who've learned to collaborate to manage the technology sold by Microsoft.

"Probabilities" are a mathematical construct that can be used to represent multiple things, but in Bayesianism the first option is the most common.

Which world gets to be real seems arbitrary.

It's the one observations come from.

Most possible worlds are lifeless, so we’d have to be really lucky to be alive.

Typically probabilistic models only represent a fragment of the world, and therefore might e.g. implicitly assume that all worlds are lived-in. The real world has life so it's ok to assume we're not in a lifeless world.

We have no information about the process that determines which world gets to be real, so how can we decide what the probability mass function p should be? 

Often you require need some additional properties, e.g. ergodicity or exchangeability, which might be justified by separation-of-scale and symmetry and stuff.

P represents your uncertainty over worlds, so there's no "right" P (except the one that assigns 100% to the real world, in a sense). You just gotta do your best.

My impression is that health problems reduce height but height also causes health problems (even in the normal range of height, e.g. higher cancer risk). I'd be surprised if height was causally healthy.

Putting it on bread and crackers seems like it dilutes it. Is it still good on its own?

By "gaygp victim", do you mean that you are gay and AGP? Or...?

That's not really possible, though as a superficial approximation you could just keep the weights secret and refuse to run it beyond a certain scale. If you were to do so, though, it would just make the AI less useful and therefore the people who don't do that would win on the marketplace.

I'm not sure I understand your question. By AI companies "making copying hard enough", I assume you mean making AIs not leak secrets from their prompt/training (or other conditioning). It seems true to me that this will raise the relevance of AI in society. Whether this increase is hard-alignment-problem-complete seems to depend on other background assumptions not discussed here.