Story heavily edited by AI about an AI company with an AI product that makes AI videos that is closing so they can spend money on some other AI product. All seasoned with some AI goop images. I hate the future.
> If this is the case, it's just a matter of time until costs can be reduced.
Is it, though? We cannot predict technological advancement, and the times of ̶M̶u̶r̶p̶h̶y̶'̶s̶ ̶L̶a̶w̶ Moore's Law* for computational power are long gone. There is simply no guarantee that the costs will go down enough.
I think there is plenty of room to make AI inference much more energy efficient. For example, there are companies testing creating custom silicon to run the model. Once that technology matures and we have some "good enough" models for normal use, inference cost for non-bleeding-edge models can come way down.
I don't expect bleeding-edge models to become any cheaper, but previous generation models can potentially be really cheap.
Disneyworld has lines longer than the park can manage for decades, do you expect it to just be a matter of time until park management finally figures out how to queue people efficiently enough, or do you think the solution will be once again raising costs for the customer.
> But doesn't $15M/day of inference cost imply "demand" from users? If this is the case, it's just a matter of time until costs can be reduced.
If you build a website that gives $100 for free to each one of your users, you’ll quickly have "demand" but that’s not "a matter of time until costs can be reduced".
Not sure what you're referring to. If you're talking about inference cost for frontier models, that's going up because researchers keep pushing those frontiers, often without considering cost. And while they're subsidized (to gain market share), users have no reason NOT to use the crazy expensive frontier models.
Once the market consolidates, and users get used to the idea of using models that are "good enough" because frontier models are too expensive, there's no reason AI cannot be profitable.
> Not sure what you're referring to. If you're talking about inference cost for frontier models, that's going up because researchers keep pushing those frontiers, often without considering cost. And while they're subsidized (to gain market share), users have no reason NOT to use the crazy expensive frontier models.
The price of GPUs and the price of RAM to put in the servers.
There's not much profit in inference, it's heavily commoditized. There is an illusion of potential profitability because the closed-weight models are currently a step ahead of the open-weight models. However, if you ignore the closed-weight models, then the open-weight models are also getting better every year. In the limit, the open-weight models will end up just as good as the closed-weight models.
AI is an inverse gold rush, the people who are getting rich off it are the people using it. The shovel-sellers are screwed.
Yes but it was capped by their restriction on sign ups/registrations. That could've easily been in the hundreds of millions if the app had public signups.
Idk if Instagram would exist if they were spending hundreds of millions a day.
Why would demand imply costs will be reduced? If you're making an economies of scale argument, there's plenty scale right now, and costs don't seem to be trending down.
I would assume that the economic reasoning, if looked at it without dollar bills covering their eyes, would apply to AI in general the way we are using it.
How many seconds of video did they generate per day for those $15,000,000, i.e. what would it actually cost me to generate, say, a three minute music video for my garage band? This should probably take into account how many attempts I would likely need to arrive at something I am satisfied with.
Assuming I know what I want and am somewhat competent at describing it, I would guess ten times the final length should be plenty. If you are exploring different options, you can of course produce an unlimited amount of videos. But that is not really what I was referring to, I was more thinking of how many attempts it takes the model to produce what you want given a good prompt - I have never used it and have no idea if it nails it essentially every time or whether I should expect to run the same prompt ten times in order to get one good result.
I would be curious to know if there is actually as much business economic demand for AI video compared to images (logos, product graphics, etc.) or text (blogs, content everywhere, etc.)
My impression is that video is too complex to easily fit into an AI pipeline. Either you need something highly specific, like your own product’s UI. Or you need something personable and consistent, like someone talking into his camera.
As a movie consumer I am not interested in AI movies. You don't get to just keep the existing market and switch to AI. You are creating a new market of AI video consumers and hoping it's big enough.
As a product photographer/videographer - No its not good enough to understand products so each scene its different, you can't storyboard or collaborate with it,. For high end products (where the money is), colour shape, scale matter and its just not consistent enough for professionals. For cheap tiktop slop products is fine because what arrives it never what you ordered anyway.
The files are a pig to try and edit as well, making them beyond the generation and prompt costs expensive. At that point you might as well go and just film the ad.
I'm sorry to be this guy but this is an incredibly poor quality article. False structure (thesis/evidence), links to poor quality sources, and a non-examination of the core thesis, which is that it's burning too much money.
$15m/day inference? How was that calculated? Forbes? Did they get it right? Is that a reasonable estimate? Still valid? How was revenue calculated?
IMO most of the votes had to come from some vote ring (35 pts in 35 minutes for a crap article, no way.)
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