Over $2B ARR now.

Why do you doubt $3k/yr? Corporate usage skews a lot higher, when it's evaluated against hiring, not as a nice to have addon.

If $10k/yr means you get work done with one less hire that's an easy decision.

Claude has to use more tokens to read the grep output.

I mean they doubled revenue from $1B/yr to $2B in a month.

At some point it can be valued as a high growth business, the code that backs it is almost irrelevant if the business is strong.

trusting a startup to accurately report its revenue in this market is about the dumbest thing you can do

Their supply of being able to serve the models is hardcore rationed by their ability to scale up datacenters and GPUs.

They really don't need to subsidize unprofitable customers at this point when there is a line out the door to pay thousands of dollars a month per user, that are revolting because they aren't actually being able to get reliable uptime.

They have all the growth they need for now, they really don't need the cheap users.

This is superintelligence. The mixed signals are tested to increase their revenues. Superintelligent AIs wouldn't be honest.

In my wilderness first responder class they emphasized taking a cocktail of ibuprofen and acetaminophen - both are effective pain relievers, each with different dangerous side effects.

The benefits stack, the side effects don't.

So if you are going to be loading up on higher doses of pain relief, take half acetaminophen and half ibuprofen.

Wouldn't it have to have a negative effect on the security to be securities fraud? Causing an investor loss is a key point of securities fraud.

"We made a ton more money with ads and the stock went up" lacks that key element of fraud?

Investors who bought an artificially inflated stock would be harmed.

How would the stock be harmed by them selling better performing or more relevant ads?

I don’t know that there were any promises anyway. But if there were, then an investor could have plausibly believed that that was a better long-term business model.

It’s early days for these LLM hosts, maybe investors could be worried about taking the really annoying business notes before users are properly addicted.

But America is personified by the Simpsons!

I guess Saddam Hussein's propaganda team thought so in 1991 too.

I'm not sure what the state of the art is today, but 15 years ago I worked on a cross-lingual search engine - a challenge with Chinese was that ngram-like models for detecting common language errors (such as typos) were simply ineffective due to this.

We found a lot of gain by having ranking features based on Pinyin to detect typos/misspellings due to homophones (and similar sounding words). I was investigating stroke decomposition to try to be able to detect near homographs, but wasn't able to find any good libraries at the time.

I could imagine the homophone issue is especially relevant for spoken input to LLMs. LLMs are good enough that they're usually right, so it's probably less of an issue, but in English I can have crazy typos and everything just works, I am curious how well that would work for Chinese, since I suspect it's a harder problem by far due to the lack of subword tokens?

Simulation is largely what traditional engineers do - I mean how many classes have you taken on finite element methods, discretizing PDEs, etc.? It's not web dev.

Fair. I think this is about the extent of my training, which was as an Applied Mathematics and Econ undergrad about 15 years ago: Partial differential equations : an introduction / Walter A. Strauss > https://libcat.canterbury.ac.nz/Record/1093497/TOC

Maybe my idea of NASA was too encompassing. I figured that, apart from the engineering work, general sim would require optimizations and productionalization similar to how we have AI Engineers focused on the practical implementation of ML systems apart from the core model R&D.

I got a bit hooked on Econ for awhile which held my attention through an MS, which is when I learned about computers and then applied that into DS and development.

Most of my simulation experience is in stochastic systems and modern digital twins where agents sometimes have asymmetric information. I can see how I'm of no practical use to NASA now, but it still stings. What a bummer existing and not doing anything cool with life. A warning to youth!

I think you are underestimating your ability to contribute and also putting NASA on too much of a pedestal.

I'd argue your background is extremely valuable, but not easily traversible to NASA at the moment.

If you are deeply interested in the space, working with the newer startups in geospatial/hyperspectral imaging (be it climate or defense usecases) or CV space.

In a lot of cases, NASA is basically just acting as a coordinator between multiple vendors who are doing "the cool stuff" with less bureaucratic minutiae and stress from what's going on in DC.

Lots of interesting players in the ClimateTech and DefenseTech space who would like your background, and indirectly or directly they all work with NASA anyhow.

Thanks. I did find a space jobs site last week, and some jobs looked like they aligned closely. That's probably why I was surprised the nasa reqs weren't as broad.

I wasn't really looking for a change; I have 1 and 3 year olds and am fully remote, and the flexibility with sicknesses is really a benefit. I think it was mostly a shock to my system that I may never do anything "cool" with my life.

One way of viewing this is that to a moderate degree, NASA has largely been outsourced to SpaceX.

Were you in an Econ program that required tons of Matlab, SAS, R?

Not in undergrad (a single upper division class), but yes in grad school. I did a lot of applied mathematics in undergrad and only took the min required upper division probability/stats class. I didn't find it interesting at the time. But when I got to Econ grad school there was a massive focus on econometrics, and I learned it from first principals.

For languages: SAS in undergrad econ/Matlab for math classes, STATA primarily in grad school, and I pivoted to R and then python when I hit industry.