Readit Newsbfirsh commented on OpenAI's H1 2025: $4.3B in income, $13.5B in loss techinasia.com/news/opena... · Posted by u/breadsniffer
I think the most interesting numbers in this piece (ignoring the stock compensation part) are:
$4.3 billion in revenue - presumably from ChatGPT customers and API fees
$6.7 billion spent on R&D
$2 billion on sales and marketing - anyone got any idea what this is? I don't remember seeing many ads for ChatGPT but clearly I've not been paying attention in the right places.
Open question for me: where does the cost of running the servers used for inference go? Is that part of R&D, or does the R&D number only cover servers used to train new models (and presumably their engineering staff costs)?
Free usage usually goes in sales and marketing. It's effectively a cost of acquiring a customer. This also means it is considered an operating expense rather than a cost of goods sold and doesn't impact your gross margin.
Compute in R&D will be only training and development. Compute for inference will go under COGS. COGS is not reported here but can probably be, um, inferred by filling in the gaps on the income statement.
(Source: I run an inference company.)
I have no affiliation with either company but from using both a bunch as a customer: Replicate has a competitor at https://fal.ai/models and FAL's generation speed is consistently faster across every model I've tried. They have some sub-100 ms image gen models, too.
Replicate has a much bigger model selection. But for every model that's on both, FAL is pretty much "Replicate but faster". I believe pricing is pretty similar.
Founder of Replicate here. We should be on par or faster for all the top models. e.g. we have the fastest FLUX[dev]: https://artificialanalysis.ai/text-to-image/model-family/flu...
If something's not as fast let me know and we can fix it. ben@replicate.com
Replicate (YC W20) | San Francisco, CA + Remote | https://replicate.com/
Replicate makes it easy to run AI in the cloud. You can run a big library of open source models with a few lines of code, or deploy your own models at scale.
We're an experienced team from Spotify, Docker, GitHub, Heroku, NVIDIA, and various other places. We're backed by a16z, Sequoia, NVIDIA, Andrej Karpathy, Dylan Field, Guillermo Rauch.
We're hiring:
- An infrastructure engineer
- A machine learning engineer who's an expert at image models
- An engineer who likes talking to people to look after our customers
... and more: https://replicate.com/about
bfirsh commented on FLUX is fast and it's open source replicate.com/blog/flux-i... · Posted by u/smusamashah
> We added a new synchronous HTTP API that makes all image models much faster on Replicate.
ooh why is synchronous fast? i click thru to https://replicate.com/changelog/2024-10-09-synchronous-api
> Our client libraries and API are now much faster at running models, particularly if a file is being returned.
... thanks?
just sharing my frustration as a developer. try to explain things a little better if you'd like it to stick/for us to become your advocates.
You're right -- this wasn't clear. Added another paragraph to explain what you had to do before.
Is this duct tape over historical architectural decisions that assumed trust? Could we design something with less complexity if we designed it from scratch? Are there any operating systems that are designed this way?