>Why does SpaceX warrant a change of existing trading rules?
They don't, while timing certainly benefits, and potentially was triggered by them and OpenAI and Anthropic IPOs, these rules are not specific to only apply to SpaceX.
FTSE Russell (Russell 1000/2000 etc.) Adopted "fast entry" for large IPOs. Eligible companies (investable market cap above Russell Top 500 cutoff) can join after 5 trading days (previously quarterly rebalances). Also eased float rules with carve-outs.
Nasdaq (Nasdaq-100): Effective May 1, 2026, top ~40 market-cap companies can enter after 15 trading days (previously 3+ months). Adjusted low-float handling.
S&P Dow Jones (S&P 500): Reducing seasoning from 12 months to 6 months for megacaps and waiving the 4-quarter GAAP profitability requirement for large issuers.
The answer remains, these rules do not specifically apply to only SpaceX, they apply to a range of companies that fit specific profiles. Timing happens to favor SpaceX, but will equally favor OpenAI, Anthropic and others within the same qualifiers.
The links above provide specifics as to the what's and the why.
And prior to that Elon did float the idea of IPOing on a non-NYC exchange, some Texas exchange. So a bit of a stick and some honey in the IPO fees and early access.
We all know they get paid by musk to load up on overvalued stocks so musk can get some cash from pension funds, the pay off a bit Russell’s for bending the rules. No one in their right mind would change rules to buy space x. What profit must have to compensate the valuation?
Stop downvoting the only person that talks reason. We have reached a point where Musk and its tech pals must be stopped with all means possible, because government oversight, democratic processes, and the judicial process clearly do not apply to them anymore.
Sadly that's what happens when people have a "high" technological culture with absolutely zero political nor ethical education. They see all the cool gadgets while being completely blind to the political and social side effects
This is a good point. It's like a strange form of selective magical thinking, or maybe it's really just a global psychosis.
Tech people without a background in humanities (not academic, even just because of personal interest) are the most prone to this in my experience.
Damn this comment really made the anti-unionists come out of the woodwork. I’ll admit, I’m a bit skeptical and think it’s not a given that the benefits you listed will come to be.
But I’ve been annoyed at the significant shift in tone that software company executives’ have used when communicating with employees lately. For one, we went from being admittedly pampered compared to most other industries to getting threats of mass layoffs unless we do more and demand less.
I wouldn’t mind the idea of using the possibility of unions to have executives back off, but if people are going to pop off at mere suggestion of unions I don’t think we’d get very far.
> it was born of some arrogance that they were speeding towards the inevitability of AGI
I think it was partly also PR. Google, OpenAI and Anthropic are fighting for mindshare and Dalle-E, Sora, Nano banana, etc generated a lot of media buzz for Google and OpenAI at various points in time.
He was on stage and had a mic. I don’t know that the students had a lot of options to make their voices heard in the situation. And since folks like Schmidt already have access to channels to spread their opinions and this was the students’ graduation I think they get a pass.
People who reach outlier-level success in a field tend to have strong opinions and an emotional connection to said field. It’s probably a non-trivial part of why they are so successful.
Maybe for folks who are deep into this, but it’s not exactly accessible. I tried reading up on it a couple of months ago, but parsing through what hardware I needed, the model and how to configure it (model size vs quantization), how I’d get access to the hardware (which for decent results in coding, new hardware runs $4k-$10k last I checked)—it had a non trivial barrier of entry. I was trying to do this over a long weekend and ran out of time. I’ll have to look into it again because having the local option would be great.
Edit: the replies to my comment are great examples of what I’m talking about when I say it’s hard to determine what hardware I’d need :).
For me the big hangup is the hardware. If I could find a simple guide to putting together a machine that I can run off an outlet in my home, I am sold. The problem is that I haven't found this yet (though I suppose I haven't looked very hard either).
$10K should be enough to pay for a 512GB RAM machine which in combination with partial SSD offload for the remaining memory requirements should be able to run SOTA models like DS4-Pro or Kimi 2.6 at workable speed. It depends whether MoE weights have enough locality over time that the SSD offload part is ultimately a minor factor.
(If you are willing to let the machine work mostly overnight/unattended, with only incidental and sporadic human intervention, you could even decrease that memory requirement a bit.)
As a typical example DeepSeek v4-pro has 59B active params at mostly FP4 size, so it needs to "find" around 30GB worth of params in RAM per inferred token. On a 512GB total RAM machine, most of those params will actually be cached in RAM (model size on disk is around 862GB), so assuming for the sake of argument that MoE expert selection is completely random and unpredictable, around 15GB in total have to be fetched from storage per token. If MoE selection is not completely random and there's enough locality, that figure actually improves quite a bit and inference becomes quite workable.
I've never seen reports of this kind of setup being able to deliver more than low single-digit tokens per second. That's certainly not usable interactively, and only of limited utility for "leave it to think overnight" tasks. Am I missing something?
Also, I don't know of a general solution to streaming models from disk. Is there an inference engine that has this built-in in a way that is generally applicable for any model? I know (I mean, I've seen people say it, I haven't tried it) you can use swap memory with CPU offloading in llama.cpp, and I can imagine that would probably work...but definitely slowly. I don't know if it automatically handles putting the most important routing layers on the GPU before offloading other stuff to system RAM/swap, though. I know system RAM would, over time, come to hold the hottest selection of layers most of the time as that's how swap works. Some people seem to be manually splitting up the layers and distributing them across GPU and system RAM.
Have you actually done this? On what hardware? With what inference engine?
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