A lot (all?) VCs charge some form of fees (typically capped at 20% of the entire fund, split in various percentages through 4 years investing, 4 divesting period). These fees often are only paid out only based on the actively deployed capital, and are not the only incentive: the main incentive is shares in gains (carry).
The reason they're based on actively deployed capital isn't that the LPs (people who give VCs money to invest) want them to deploy the money in a stupid way, but they definitely don't want VCs to get the fees if the money wasn't invested. Therefore, VCs:
1. Want to raise as much money as possible
2. Want to deploy as much money as possible
Ideally, as quickly as possible.
There's nothing fraudulent about the idea of calculating VCs fees in various scenarios.
There's however the extremely dodgy part of the portfolio companies paying their investor (VC) fees for anything. This is an obvious conflict of interests, and should never happen, but I personally know of multiple VC funds here in Europe (will skip the names to not get sued, lol) who base their entire operational model on funding shitty companies that have 0 chance of success, charging them for the office space and often "shared services" they provide. Unsure if this is a regulatory overlooking, or something that's deliberately legal, but IMHO shouldn't be. Probably they talked their LPs into agreeing to this on paper.
There have been numerous cases of sanctioning and wealth confisactions (Afrghanistan, Venezuela, Iraq, Iran, Libya), and "_now_ carries confiscation risk" is just factually incorrect. It has always carried such risk, this risk has materialized numerous times, and most importantly, no diversification is happening - literally nothing changed: https://data.imf.org/en/news/4225global%20fx%20reserves%20de...
By the way, good luck with trusting China, Russia, or other places to store wealth more than Europe. It's total ignorance to believe there's somewhere safer to store your money than the West.
"Pozsar’s argument: the moment Western nations froze Russian foreign exchange reserves, the assumed risk-free nature of these dollar holdings changed fundamentally. What had been viewed as having negligible credit risk suddenly carried confiscation risk."
This has nothing to do with dollar. Almost all of the confiscated currency was in Europe, and it has to do with invading your bank's ally. No country in the world ever assumed that's risk free, it wasn't being priced back then and isn't now, because nobody except from Russia is stupid enough to do that.
If you're adding some computational/problem breakdown/heuristic steps on top/instead of mathematical concepts, then you're doing the opposite of what the author proposes.
Scientific conensus in math is Occam's Razor, or the principle of parsimony. In algebra, topology, logic and many other domains, this means that rather than having many computational steps (or a "simple mental model") to arrive to an answer, you introduce a concept that captures a class of problems and use that. Very beneficial for dealing with purely mathematical problems, absolute distaster for quick problem solving IMO.
"Think in math, write in code" is the possibly worst programming paradigm for most tasks. Math notations, conventions and concepts usually operate under the principles of minimum description lenght. Good programming actively fights that in favor of extensibility, readability, and generally caters to human nature, not maximum density of notation.
If you want to put this to test, try formulating a React component with autocomplete as a "math problem". Good luck.
(I studied maths, if anyone is questioning where my beliefs come from, that's because I actually used to think in maths while programming for a long time.)
Agreed, but that's not in the C dimension of a first-layer embedding of a single token though, it's across the whole model and that's what I said in the comment above.
It's... really not what I meant. This requirement does not have to be relaxed, it doesn't exist at all.
Semantic similarity in embedding space is a convenient accident, not a design constraint. The model's real "understanding" emerges from the full forward pass, not the embedding geometry.
Language models don't "pack concepts" into the C dimension of one layer (I guess that's where the 12k number came from), neither do they have to be orthogonal to be viewed as distinct or separate. LLMs generally aren't trained to make distinct concepts far apart in the vector space either. The whole point of dense representations, is that there's no clear separation between which concept lives where. People train sparse autoencoders to work out which neurons fire based on the topics involved. Neuronpedia demonstrates it very nicely: https://www.neuronpedia.org/.
The reason they're based on actively deployed capital isn't that the LPs (people who give VCs money to invest) want them to deploy the money in a stupid way, but they definitely don't want VCs to get the fees if the money wasn't invested. Therefore, VCs:
1. Want to raise as much money as possible 2. Want to deploy as much money as possible
Ideally, as quickly as possible.
There's nothing fraudulent about the idea of calculating VCs fees in various scenarios.
There's however the extremely dodgy part of the portfolio companies paying their investor (VC) fees for anything. This is an obvious conflict of interests, and should never happen, but I personally know of multiple VC funds here in Europe (will skip the names to not get sued, lol) who base their entire operational model on funding shitty companies that have 0 chance of success, charging them for the office space and often "shared services" they provide. Unsure if this is a regulatory overlooking, or something that's deliberately legal, but IMHO shouldn't be. Probably they talked their LPs into agreeing to this on paper.