> I am not fan of Musk, but one has to have some crazy blinders on not to give him credit here.
Tesla does have ~700k other employees.
455 karma · joined November 20, 2015
Tesla does have ~700k other employees.
Long-term investors would lose about 40% of their investment to subsidize the inflated valuation of X (bought by xAI), the inflated valuation of xAI (bought by SpaceX), and SpaceX (whose valuation is based on the assumption that demand for inference is effectively unlimited, and that everyone will prefer hardware in space over Earth).
The valuation of both companies was inflated prior to acquisition, and Tesla shareholders will be asked to dilute their investment by absorbing that cumulative inflation.
Not even to mention that Musk would have exclusive control over the entire conglomerate, with not even the pretense of a Board to check his power.
When even Steven Mark Ryan is like "let's pump the breaks a little bit," you know it's bad.
They also consistently lose in court once these charges are subjected to scrutiny. The point seems to be to merely manufacture bad headlines to smear the targets.
Prior to this administration, the feds had a 93% conviction rate. With so many frivolous cases falling apart in court, I'd be curious to know where that percentage is now.
The other factor that threatens the $6T required to justify... open source models crushing the pricing power of these companies. If I can use some open source agent management framework + self-hosted Qwen to basically replace my entire Product org for free, then why would I pay Microsoft/Google/Anthropic/OpenAI?
Warehouse and factory labor is the next largest market for AI/robotics, at about $7T/year globally.
The bubble question is really about the target year. Will these companies start on a trajectory where AI begins eating these massive markets before investors lose faith?
To me it looks like one of these is true:
(1) this is indeed a bubble, and AI will turn into a useful tool integrated into everything but fall short of expectations. We'll see some significant multiple compression in the share prices of these core AI companies, but they'll remain good investments in the long term.
(2) this is a real transformation, the investment was worth it, and these companies will earn massive revenue while causing Depression-era levels of unemployment (24% if half of knowledge workers and factory/warehouse workers can get jobs).
Basically, the revenue required to justify the infra spend requires that they start eating existing industries, because that's certainly not going to come from ChatGPT/Claude subscriptions.
https://chatjimmy.ai/ blew my mind at how fast etched model weights can be.
For on-device LLMs, there's a point of diminishing returns, meaning you don't need to have the latest frontier model for most operations.
I think in 2008 I walked by it and said "oh, that's a funny art installation." Then the phone rang, and I said "wait, what? is that a working pay phone?"
I picked it up and someone asked me if I was satisfied with my home insurance.
You could spend the entire week high as a kite, biking around and just looking at everything without talking to anyone, though the vast majority of the community are people you probably would enjoy interacting with. I find most burners thoughtful, compassionate, playful, caring, etc.
What I suspect would happen is you'd start out this way ^, but then you'd gradually discover that the community is great and interactions with people are pretty fun actually. Part of the experience is the opportunity to get outside of your normal baseline personality if you want. You can be anyone for a week. Most people don't even want to talk about their lives off the playa.
Culture changes on the weekend, when you get a lot of "partiers" just for the man and temple burn. There are a bit more irresponsible and inconsiderate people then, but not enough to ruin the overall community vibe.
They can take it for a "reasonable amount of time" (inconvenience you for a few hours and make you miss your connecting flight) while they copy an encrypted image. They then must return it to you.
This administration attacks any body or authority that has the power to hold them accountable. See: attacks on the judiciary, attacks on the media, attacks on academia, etc.
https://pubmed.ncbi.nlm.nih.gov/25271984/
In 88% (36 out of 41) of patients with high-risk HPV, taking reishi + turkey tail mushrooms daily for 2 months cleared the virus, versus 5% clearance (1 out of 20) in the control group. I suspect that 88% clearance rate would be higher if they continued for longer than 2 months.
I think you're saying that you can eventually train models to arrive at that destination by training on existing jokes, effectively encoding these leaps as probabilities.
In that case, the model isn't actually making an intuitive/comedic leap; they're just following new probability chains in attempting to approximate examples they've seen in training.
I'm suggesting that something architecturally different is necessary to create a model which can make intuitive/comedic leaps.
Try to get a frontier model to write a clever, funny joke which hasn't been seen before. Or, try to get it to make an intuitive leap that leads to a novel discovery.
You can use them to guide your own efforts along these lines, as a sounding board. But with current architecture I just don't think either is possible for an LLM to do on its own.
LLMs are inherently probabilistic, and there's currently no mechanism for producing an orthogonal directional change in the path traced through a latent space which is also contextually relevant (landing on a punch line).
In other words, LLMs are fundamentally incapable of making intuitive/orthogonal leaps in context.
It might be possible to add this capability with a new architectural component like transformers, but specifically for making "left turns"/intuitive leaps.
However, if you're serious about it, then why not hand-write your LLM-generated code? You'll get much better retention and comprehension than typing.
Think of it like this: if you can offload a task to an AI model that requires reasoning or representing a nonlinear system, you are offloading intelligence.
You're using available information (copyrighted works, or the output of another model) to train a model to encode the information in a new form. Why is the former not theft, but the latter is theft?