The start(-up?)[sic] generates up to $2 billion annually from ChatGPT and an additional $ 1 billion from LLM access fees, translating to an approximate total revenue of between $3.5 billion and $4.5 billion annually.
I hope their reporting is better then their math…
Probably used ChatGPT….
I see Scott Steiner has a hold of their calculator…
Maybe they also added 500M for stuff like Dall-E?
Good point - it guess it could have easily fallen out while being edited, too
OpenAI is no longer the cutting edge of AI these days, IMO. It’ll be fine if they close down. They blazed the trail, set the AI revolution in motion, but now lots of other companies have picked it up and are doing better at it than them.
There is no AI Revolution. There never was. Generative AI was sold as an automation solution to companies looking to decrease labor costs, but’s it’s not actually good at doing that. Moreover, there’s not enough good, accurate training material to make generative AI that much smarter or more useful than it already is.
Generative AI is a dead end, and big companies are just now starting to realize that, especially after the Goldman-Sachs report on AI. Sam Altman is just a snake oil saleman, another failing-upwards executive who told a bunch of other executives what they wanted to hear. It’s just now becoming clear that the emperor has no clothes.
Generative AI is not smart to begin with. LLM are basically just compressed versions of the internet that predict statistically what a sentence needs to be to look “right”. There’s a big difference between appearing right and being right. Without a critical approach to information, independent reasoning, individual sensing, these AI’s are incapable of any meaningful intelligence.
In my experience, the emperor and most people around them still has not figured this out yet.
this was my fav take on it https://archive.ph/lkpuA
Generative AI is just classification engines run in reverse. Classification engines are useful but they’ve been around and making incremental improvements for at least a decade. Also, just like self-driving cars they’ve been writing checks they can’t honor. For instance, legal coding and radiology were supposed to be automated by classification engines a long time ago.
It’s sort of like how you can create a pretty good text message on your phone using voice to text but no courtroom is allowing AI transcription.
There’s still too much risk that it will capitalize the wrong word or replace a word that’s close to what was said or do something else wholly unconceived of to trust it with our legal process.
If they could guarantee a 100% accurate transcription of spoken word to text it would put the entire field of Court stenographers out of business and generate tens of millions of dollars worth of digital contracts for the company who can figure it out.
Not going to do it because even today a phone can’t tell the difference between the word holy and the word holy. (Wholly)
If they closed down, and the people still aligned with safety had to take up the mantle, that would be fine.
If they got desperate for money and started looking for people they could sell their soul to (more than they have already) in exchange for keeping the doors open, that could potentially be pretty fuckin bad.
Well, my point is that it’s already largely irrelevant what they do. Many of their talented engineers have moved on to other companies, some new startups and some already-established ones. The interesting new models and products are not being produced by OpenAI so much any more.
I wouldn’t be surprised if “safety alignment” is one of the reasons, too. There are a lot of folks in tech who really just want to build neat things and it feels oppressive to be in a company that’s likely to lock away the things they build if they turn out to be too neat.
Many of their talented engineers have moved on to other companies, some new startups and some already-established ones.
When did this happen? I know some of the leadership departed but I hadn’t heard of it from the rank and file.
I’m not saying necessarily that you’re wrong; definitely it seems like something has changed between the days of GPT-3 and GPT-4 up until the present day. I just hadn’t heard of it.
There are a lot of folks in tech who really just want to build neat things and it feels oppressive to be in a company that’s likely to lock away the things they build if they turn out to be too neat.
I’m not sure this is true for AI. Some of the people who are most worried about AI safety are the AI engineers. I have some impression that OpenAI’s safety focus was why so many people liked working for them, back when they were doing groundbreaking work.
AI engineers are not a unitary group with opinions all aligned. Some of them really like money too. Or just want to build something that changes the world.
I don’t know of a specific “when” where a bunch of engineers left OpenAI all at once. I’ve just seen a lot of articles over the past year with some variation of “<company> is a startup founded by former OpenAI engineers.” There might have been a surge when Altman was briefly ousted, but that was brief enough that I wouldn’t expect a visible spike on the graph.
I do expect them to receive more funding, but I also expect that to be tied to pricing increases. And I feel like that could break their neck.
In my team, we’re doing lots of GenAI use-cases and far too often, it’s a matter of slapping a chatbot interface onto a normal SQL database query, just so we can tell our customers and their bosses that we did something with GenAI, because that’s what they’re receiving funding for. Apart from these user interfaces, we’re hardly solving problems with GenAI.
If the operation costs go up and management starts asking what the pricing for a non-GenAI solution would be like, I expect the answer to be rather devastating for most use-cases.
Like, there’s maybe still a decent niche in that developing a chatbot interface is likely cheaper than a traditional interface, so maybe new projects might start out with a chatbot interface and later get a regular GUI to reduce operation costs. And of course, there is the niche of actual language processing, for which LLMs are genuinely a good tool. But yeah, going to be interesting how many real-world use-cases remain once the hype dies down.
It’s also worth noting that smaller model work fine for these types of use cases, so it might just make sense to run a local model at that point.
womp womp
I will be in a perfect position to snatch a discount H100 in 12 months
Oh no!
Anyway…
Bubble. Meet pop.
Now’s the time to start saving for a discount GPU in approximately 12 months.
They don’t use GPUs, they use more specialized devices like the H100.
Everyone that doesn’t have access to those is using gpus though.
We are talking specifically about OpenAI, though.
People who previously were at the high end of GPU can now afford used H100s -> they sell their GPUs -> we can maybe afford them
the hermit crab gambit, everyone line up in order of size!
Yep and if OpenAI goes under the whole market will likely crash, people will dump their GPUs they’ve been using to create models and then boom, you’ve got a bunch of GPUs available.
That would depend entirely on why OpenAI might go under. The linked article is very sparse on details, but it says:
These expenses alone stack miles ahead of its rivals’ expenditure predictions for 2024.
Which suggests this is likely an OpenAI problem and not an AI in general problem. If OpenAI goes under the rest of the market may actually surge as they devour OpenAI’s abandoned market share.
Can I use a H100 to run hell divers 2?
Last time a batch of these popped up it was saying they’d be bankrupt in 2024 so I guess they’ve made it to 2025 now. I wonder if we’ll see similar articles again next year.
Totally not a bubble though.
Yeah. It’s a legitimate business, where the funders at the top of the pyramid are paid by those that join at the bottom!
For anyone doing a serious project, it’s much more cost effective to rent a node and run your own models on it. You can spin them up and down as needed, cache often-used queries, etc.
For sure, and in a lot of use cases you don’t even need a really big model. There are a few niche scenarios where you require a large context that’s not practical to run on your own infrastructure, but in most cases I agree.
PLEASE!!!
Ai stands for artificial income.
I hope so! I am so sick and tired of AI this and AI that at work.
