Market capitalization takes into account the future value of the business, while equity (an accounting concept) only looks at present day cash & liabilities.

When a company gets liquidated, the future growth/value of the business is, pretty obviously, zero, so the "market cap" is irrelevant. What matters is what is left over once all debts are liquidated.

SpaceX's equity at the time of their IPO was $34 Billion (it's in the S1!). You can also see in their S1 that their cash decreases by about $8B a year, so unless things change they should be underwater in about 4 years.

1. SpaceX issued long-term bonds whose coupon (ie, "interest rate") was 6.5%.

2. Those bonds are now trading for less than their face value. That means that if you buy one of those bonds on the secondary market, you will get a return (yield) of 7.387% (if the price of a bond goes down, but the coupon stays the same, the yield goes up).

3. This doesn't affect SpaceX directly, but it tells you that if SpaceX were to issue new bonds today, they would have to offer 7.4% coupon on them, not 6.5%. Note that even though that was caused by a 10% drop in the bond value, it's a 13% increase in cost of borrowing!

SpaceX is a cash-flow negative company that depends on debt and selling equity in order to pay the bills. They will have to issue bonds again, and those bonds will be more expensive.

Note that the shortest maturity bonds don't have to be repaid for 5 years, so the impact on cash is not going to manifest for a while...at least 5 years time (assuming they did another bond offering tomorrow). In that sense it's a nothingburger.

The immediate impact it could have is if spacex depends on issuing new, shorter-term debt (lines of credit, etc) whose price could be impacted by the market's perception of their riskiness.

I experience this mostly when asking for music. Before gemini, mistakes were common but deterministic. It was easy to understand where the query had gone wrong and so how to fix it. Example:

"Hey google, play Blackstar"

(Plays the album blackstar by David Bowie, not what I wanted)

"Hey google, play "Blackstar by Radiohead"

(Plays the right thing).

Now:

"Hey Google, play Blackstar by Radiohead" can result in playing... something vaguely semantically related with no way to course correct. In this exact instance (happened yesterday!) it played an album by the hip hop due Black Star.

I will admit that there are some superpowers hidden in Gemini that were not present in the previous AI assistant. I recently discovered that Gemini can manipulate the navigation app, and a prompt like "Mute alerts" works, which is kind of cool. However like OP said, it's incredibly verbose, which is super annoying.

The only problem that "data centers in space" solves is the problem of trying to scale a rocket company where the potential demand for rocket launches is simply not that big.

This will depend on how much inference happens for consumer (desktop, local) vs enterprise ("cloud"), vs consumer mobile (probably also cloud).

I would assume that the proportion of "consumer, local" is small relative to enterprise and mobile.

These people's writing is usually incoherent and they are very proud of it. If you've ever read a bad new-age self-help book you've probably encountered writing like this.

Good writers understand that writing is about communication. The initial act of writing (ie, word puke) is worthless. What matters most is a piece of writing's ability to communicate clearly.

This writing is usually pleasant, concise, and clear.

I don't follow. The comparison is 30% of energy use for transportation vs 4% for AI, and soon 30% for transportation vs 10% for AI.

And most of that new capacity will be natural gas. That increase would basically whipe out the reduction in CO2 emissions the USA has had since 2018.

Gravitational deflection (General relativity) received pretty important confirmation in 1919, only 8 years after Einstein first proposed it.

Time dilation (Special realativity) was experimentally confirmed in 1932.

The current "futurist" vision is one of humanoid robots taking over many/most jobs done by humans today, but - as someone that routinely hires human welders & assemblers - the dexterity required for most ad-hoc tasks seems many many decades (if not more?) away from what I see robots do--yes, even the fancy chinese jumping ones.

This has led me to think one of two things:

1. The robotics revolution will not come. It's predicated on the idea that advances in robotics will follow a curve of the same shape as advances in compute/ai, which will not happen. OR...

2. There has been some paradigm-shift or some breakthrough that has put robotics improvement on a new curve.

To an outsider, what I see in robots is not categorically different than like, the sony AIBO dog in 1999. It's significantly better of course, but is it really that different? (Whereas what we can do in compute-land today is categorically diffrent because of the transformer model breakthrough).

So:

1. Have there been any breakthroughs that would lead us to believe that a robot will be able to like, look under a table to adjust a screw?

2. What are the scaling laws & practical limits to present-day robotic dexterity? Is it materials? Energy density? What?

3. What is the real rate of improvement along these key dimensions? Are robots improving linearly? Geometrically? Exponentially?

4.Or should I keep discounting robotics until we get our first robots that are made of meat? That I'd believe would result in exponential change!

And I admit that I made that assertion from my gut without actually knowing if it's true or not.

We have a way of determining if Anthropic is, or has the capability of being profitable, and what the levers to that may be. AI may be world-changing, but the accounting principles behind AI labs are no different than those behind a Pizza Hut.

Even if the cost of "inference + serving" is lower than the cost of selling a token, the relevant question is what is the depreciation schedule of the cost of training. ie, if I spend $1 on training, how long do I have before I have to spend $1 again?

Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable. So the question is:

What can be done to make training depreciate more slowly? Perhaps users can be persuaded to stick around using non-fronteir models for longer, although then there's a shift in the competitive landscape.

If users cannot be persuaded (forced?) to use legacy models, then the entire business model is thrown into question, because there's no reason why training frontier models would ever get cheaper: even if it gets cheaper on the margin, surely that will result in more compute used to generate an even "better" model, resulting in more spend in the aggregate.

This doesn't mean that the AI industry is "doomed". A couple things could happen, and this is where the fronteir labs should be focusing their attention:

1. They could find a way to climb up the value chain and capture more of the consumer surplus.

2. There could be a paradigm shift in compute architecture/compute cost.

3. We could reach a limit of marginal utility, shifting consumption to legacy models, thereby lengthening the depreciation/utility of training.

Edit: My assertion of "Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable." is made with no real information, just a gut feeling, and should not be taken seriously.

With telsa it was robotaxis, and when that failed to materialize, humanoid robots (fucking LOL).

SpaceX is an even more insane example. They are eyeing an IPO at a 1.5 trillion valuation. And yet the market for satellite launches is simply not that big. (What would you do with a satellite, if I gifted you one for free?). Estimates have SpaceX doing about $3B in annual earnings, which would give them a 500x earnings multiple at a 1.5T valuation (Apple: 35).

And so SpaceX/Elon had to invent the absolutely idiotic idea of "data centers in space" to sell some future vision of tens of thousands of launches per year.

He keeps upping the ante (and the ridiculousness of the vision), and so far investors keep funding it.

Me? I've realized that this madness is entirely "opt-in" and I choose to simply...not opt-in.

(At the time of writing this comment there's a sibling claiming that the comment cannot possibly understand this POV because they are not "an American POC.")