disgruntledphd2

Nah, they were planning for 30bn two weeks ago, so this is a bad look if nothing else.

You should probably read the S1, they showed a mostly nominal loss due to accounting rules.

They have a lot of cash, but very few assets and a voracious hunger for renting compute.

Don't get me wrong, they make a fantastic model, but the business success is yet to be seen.

Even if you're a shareholder, the long term gains of an investment are much higher than the short term ones.

The trouble is that executives are bonused on much shorter time scales, and they run the company.

Yeah, but this is bottlenecked by both your ability to observe and understand the system, which is precisely the thing that's being reduced by careless use of AI tools.

As software intermediates more and more of the physical world, we're gonna end up at least as regulated as finance, and potentially as much as aviation.

The only real question is how much damage will be caused before that happens.

And just to pile on, payments is an incredibly competitive space, and operational excellence is non negotiable for someone like Stripe, as they are a higher priced provider.

I think that it was very strange how this became such an issue. Like, as long as it doesn't hurt anyone, who cares about another's gender expression?

But it's such a tiny proportion of the population, and matters much less than making things better for the populace (which is the whole point of governance).

I'm not a Marxist, but the whole culture war stuff is very very convenient for those who don't want to work on reducing inequality (by taxing the upper earners more). And given how much of the Democratic vote is dependent on these people, I guess it makes political sense in the short term.

Dunno while I'm replying to a random anon, but anyways.

People can not (mostly) change their sex (AFAB etc).

However, there are a vast proportion of potential gender expressions, and it should be entirely uncontroversial that people can choose from them.

And now, given that I have disagreed with both partisan (US) positions on this, I'll withdraw.

We still don't know that, as Anthropic don't split our costs by training vs inference.

Maybe someday.

Sort of. It really does depend on the company and how they're set up. Like, brands using agencies or more non DR advertising only update on marketing mix models which are much slower.

Depends on the big spenders, tbh. Internet native brands are more likely to do this, but larger more staid companies will take a lot longer.

> I think what's happening is Chines models genuinely try to benchmax on it and hence they suffer when a new version comes out.

I read somewhere on Substack that a lot of this was driven by the inclusion of more knowledge work benchmarks, where the US labs are stronger, versus coding which is where most Chinese models appear to put their energies for now.

> They are like almost like malicious compliance geniuses lol.

They're like a monkey's paw or evil genie I think.

You'll get exactly what you ask for, but lots of weirdness you didn't expect.

To be fair to the model makers, deciding what to do with an under-specified prompt is hard, particularly in an RL environment where you can't just ask the user.

For stuff I want to run for a while, I'm defaulting to telling it to ask me the questions upfront to reduce the incidence of these kinds of problems.

The secret here is that building the semantic layer is a ton of work, predominantly done by data people.

I'd say more likely the FAANG type companies, suspect big 4 are much smaller.

But the FAANG customers are the opposite of sticky, given that they all have their own models.