B56b

yay?

The problem is that $1500/engineer/month would be a pretty modest amount of demand for labs. OpenAI/Anthropic are basing their $1T valuations on the explosive uncapped growth of unlimited agentic token spending. On so many levels of the industry this growth is now priced in. You don't think so?

What, why? This is the web, so it has to be a solution that can handle old browser versions. Take away the old version and it's as small as it can be.

This forum is oddly hesitant to accept good news, a weird feature of online communities.

So just ignoring the link entirely, cool cool cool

Ya, that sounds right to me. Coastal city housing is very supply constrained, part of why it's so expensive, but it is hugely in demand and provides tons of value to many by letting them live near high paying companies. Unless by "overinflated" you mean a constrained supply/demand curve?

What's wrong with choosing to care about coding manually then?

The problem with this hype cycle has always been that the hyperscalers are pouring unbelievable amounts of capital into a technology that hasn't proven it can generate the revenues needed to justify that.

Nvidia might have an ok P/E right now, but the question is if the industry can sustain buying over $50B of GPUs every quarter(or that it even needs to).

The task isn't "make something that you could plausibly call a pencil". It's "understand every step of how a modern pencil is produced".

That's the critical difference. You could always find some person who understood a particular piece of a complex puzzle. It's a very new, worrying thing to have pieces that no one understands.

Social media being bad for mental health in childhood is one of the most robust theories I've ever seen for these kind of society-wide problems. You can peruse the After Babel Substack for the evidence if you're not convinced, but Jonathan Haidt has consistently done incredible work here.

This hits the nail on the head. There's a marked difference between a JSON parser and a real world feature in a product. Real world features are complex because they have opaque dependencies, or ones that are unknown altogether. Creating a good solution requires building a mental model of the actual complex system you're working with, which an LLM can't do. A JSON parser is effectively a book problem with no dependencies.