nowayno583

Your reasoning is correct if you think about negotiating options, or going all in on a trade, but its not quite right for stocks. The borrowing rates for MSFT and NVDA - even for a retail investor - are less than 1% yearly. So if your view is right you could hold a short on them for years. The market cap for these companies has already incorporated a large capex investment for AI DCs. As long as you use a reasonable rebalancing strategy, and you are right that their current investment in AI will not pay off, you will make money.

Mind you, this is a view that exists - a few large hedge funds and sell side firms currently hold negative positions/views on these companies.

However, the fact of the matter is, fewer people are willing to take that bet than the opposite view. So it is reasonable to state that view with care.

You might be right at the end of the day, but it is very much not obvious that this bet has not (or will not) pay off.

That's a very out of the money view! If you are right you could make some very good money!

It is a very complex phenomenon, with no single driving force. The usual culprit is uncertainty, which itself can have a ton of root causes (say, tariffs changing every few weeks, or higher inflation due to government subsidies).

In more uncertain scenarios small companies can't take risks as well as big companies. The last 2 years have seen AI, which is a large risk these big companies invested in, pay off. But due to uncertainty smallish companies couldn't capitalize.

But that's only one possible explanation!

That was a very fun recap, thanks for sharing. It's easy to forget how much better these things have gotten. And this was in just six months! Crazy!

Markets are supposed to be decision makers, and having pricing information be public helps firms and other allocators take better decisions. The question at hand is if the flow of information today is so fast that it deters smart decision makers because of other participants who can extract their decisions from them before they can act on them. Wether you believe markets should be more or less opaque should be decided by where you sit on this spectrum imo.

Oh! It's been a good while since I've worked in noise cancelling. I didn't know current tech was at the point where we could do direct reproduction of the outside noise, instead of just using mic arrays! That's very cool, it used to be considered totally sci fi to do it fast enough in a small headset.

Does anyone understand why they are taking the difference between transformers instead of the sum? It seems to me that in a noise reducing solution we would be more interested in the sum, as random noise would cancel out and signal would be constructive.

Of course, even if I'm right proper training would account to that by inverting signs where appropriate. Still, it seems weird to present it as the difference, especially seeing as they compare this directly to noise cancelling headphones, where we sum both microphones inputs.

Makes sense! My impression that phase matters from audio comes from when editing audio in a DAW or anything like that. We are very sensitive to sudden phase changes (which would be kind of like teleporting very fast from one point to another, from our heads point of view). Our ears kind of pick them up like sudden bursts of white noise (which also makes sense, given that they kind of look like an impulse when zoomed in a lot).

So when generating audio I think the next chunk needs to be continuous in phase to the last chunk, where in images a small discontinuity in phase would just result in a noisy patch in the image. That's why I think it should be somewhat like video models, where sudden, small phase changes from one frame to the next give that "AI graininess" that is so common in the current models

Intuitively, audio is way more sensitive to phase and persistence because of the time domain. So maybe audio models look more like video models instead of image models?

I'm not really sure how current video generating models work, but maybe we could get some insight into them by looking at how current audio models work?

I think we are looking at an auto regression of auto regressions of sorts, where each PSD + phase is used to output the next, right? Probably with different sized windows of persistence as "tokens". But I'm a way out of my depth here!

But Swift is just a messaging protocol, right? It doesn't handle trust at all - like you said, you need an awful lot of documents for a single transfer.

I think what could be gained with a zkp protocol would be timeliness. Not needing to confirm if the client has funds in the other institution manually or from trusting their in house APIs would be pretty nice.

The Brazilian central bank has a system that does essentially that, and wires here (even for very large sums) take seconds to fill, instead of the usual 2 days for US interbank wires.

Imagine you are Goldman Sachs and a client wants to make a 100mm USD wire transfer to one of their accounts at Citibank. How does citibank know that the account at GS has the money to cover this transfer?

Right now, the way this works is essentially through a lot of trust and some guarantees by the fed. This has some downsides: because you need a lot of confirmations, it makes transfers take longer. Also, small players can't really get in on this system, so some regional banks are at a disadvantage.

How do you make this safer and more robust? GS obviously can't send info on all of its clients accounts and balances to Citi. You could imagine a protocol where the client/GS sends Citi a zkp to prove that the client has the money (as long as all inputs are agreed upon).

Of course, you don't really need zkps. You could also have the fed keep a database on all money in all accounts (like they do in Brazil), so that the bank only has to ask the central bank to give you an ok. But that is a whole lot of power in the hands of a central authority, as well as a single point of failure, which is something banking systems should avoid imo