I can't understand why xAI charges 50% more per month for Grok over competitors when it doesn't even gracefully downgrade to a cheaper model when paid subscribers hit the limit.

I was dubious about SpaceX (orbital data centers need to solve for extreme radiation and error-correction during training), but then I remembered that xAI is actively working on virtualizing white collar workers ("Macrohard").

In my opinion, this is the only TAM that justifies $1T in data center investment, because the consumer market for ChatGPT-style AI is saturated. There's a lot of enterprise TAM available for AI, but I think what these companies training frontier models are really after is selling a product that allows companies to eliminate the cost of white collar salaries.

Have uploaded the audio files for anyone who's interested. They've since patched the ability to ask for output in zalgotext, but it was pretty strange while it lasted.

Comments URL: https://news.ycombinator.com/item?id=48283590

Points: 2

# Comments: 1

Also $1T in data center investment makes sense when you realize that companies are racing to create virtual white collar workers. Google spends $9B a year on software engineers.

I've tried to convey this to OpenAI through various available channels (dev forums, app feedback, etc.).

Grok solves this by having an optional push-to-talk mode, but this is not hands-free and thus more cumbersome than just having a user-configurable variable like seconds_delay_before_sending_voice_input.

(1) LLMs are basically Stack Overflow on steroids. No need to go look up examples or read the documentation in most cases, spit out a mostly working starting point.

(3) Learning. Ramping up on an unfamiliar project by asking Antigravity questions is really useful.

I do think it makes devs faster, in that it takes less time to do these two things. But you're running into the 80% of the job that does not involve writing code, especially at a larger company.

In theory, this should allow a company to do more with fewer devs, but in reality it just means that these two activities become easier, and the 80% is still the bottleneck.

Then they changed the architecture so voice mode bypasses custom instructions entirely, which was really unfortunate. I had to unsubscribe, because walking and talking was the killer feature and now it's like you're speaking to a Gen Z influencer or something.

However, at the quantum level, identity is not bound to space or time. When you split a photon into an entangled pair, those "two" photons are still identical. It's a bit like slicing a flatworm into two parts, which then yields (we think) two separate new flatworms... but they're actually still the same flatworm.

Experiments like this are surprising precisely because they break our assumption that identity is bound to a discrete object, which is located at a single space, at a single time.

This metaphor of the pachinko machine (or Plinko game) is exactly how I explain LLMs/ML to laypersons. The process of training is the act of discovering through trial and error the right settings for each peg on the board, in order to consistently get the ball to land in the right spot-ish.

To be fair, most of software engineering is this.

Related: I've found that the internet becomes significantly better when I use a Chrome extension to hide all comment sections. Comments are by far the most significant source of toxicity.

Abstracting chunks of compound conditionals into easy-to-read variables is one of my favorite techniques. Underrated.

> isValid = val > someConstant

> isAllowed = condition2 || condition3

> isSecure = condition4 && !condition5

> if isValid && isAllowed && isSecure { //...