Most recent news about AI seems to involve staggering amounts of money. OpenAI and Nvidia sign a $100b data center contract. Meta offers researchers $100m salaries. VCs invested almost $200b in AI …
This is the real future of neural networks. Trained on supercomputers - runs on a Game Boy. Even in comically large models, the majority of weights are negligible, and local video generation will eventually be taken for granted.
Probably after the crash. Let’s not pretend that’s far off. The big players in this industry have frankly silly expectations. Ballooning these projects to the largest sizes money can buy has been illustrative, but DeepSeek already proved LLMs can be dirt cheap. Video’s more demanding… but what you get out of ten billion weights nowadays is drastically different from a six months ago. A year to date ago, video models barely existed. A year to date from now, the push toward training on less and running on less will presumably be a lot more pressing.
The bubble popping will be a good thing. Henry Ford didn’t come around until after the electrification bubble popped, after all. Bezos didn’t come around until the dotcom bubble burst.
It’s after all bubbles burst - when the genuinely useful things are most salient and apparent, that the true innovations happen.
The bubble continuing ensures the current paradigm soldiers on, meaning hideously expensive projects shove local models into people’s hands for free, because everyone else is doing that.
And once it bursts, there’s gonna be an insulating layer of dipshits repeating “guess it was nothing!” over the next decade of incremental wizardry. For now, tolerating the techbro cult’s grand promises of obvious bullshit means the unwashed masses are interpersonally receptive to cool things happening.
Already the big boys are pivoted toward efficiency instead of raw speed at all costs. The closer they get toward a toaster matching current tech with a model trained for five bucks, the better. I’d love for VCs to burn money on experimentation instead of scale.
I’m very interested in this approach because I’m heavily constrained by money. So I am gonna be looking (in non appliance contexts) to develop workflows where genAI can be useful when limited to small models running on constrained hardware. I suspect some creativity can yield useful tools with these limits, but I am just starting out.
This is the real future of neural networks. Trained on supercomputers - runs on a Game Boy. Even in comically large models, the majority of weights are negligible, and local video generation will eventually be taken for granted.
Probably after the crash. Let’s not pretend that’s far off. The big players in this industry have frankly silly expectations. Ballooning these projects to the largest sizes money can buy has been illustrative, but DeepSeek already proved LLMs can be dirt cheap. Video’s more demanding… but what you get out of ten billion weights nowadays is drastically different from a six months ago. A year to date ago, video models barely existed. A year to date from now, the push toward training on less and running on less will presumably be a lot more pressing.
The bubble popping will be a good thing. Henry Ford didn’t come around until after the electrification bubble popped, after all. Bezos didn’t come around until the dotcom bubble burst.
It’s after all bubbles burst - when the genuinely useful things are most salient and apparent, that the true innovations happen.
The bubble continuing ensures the current paradigm soldiers on, meaning hideously expensive projects shove local models into people’s hands for free, because everyone else is doing that.
And once it bursts, there’s gonna be an insulating layer of dipshits repeating “guess it was nothing!” over the next decade of incremental wizardry. For now, tolerating the techbro cult’s grand promises of obvious bullshit means the unwashed masses are interpersonally receptive to cool things happening.
Already the big boys are pivoted toward efficiency instead of raw speed at all costs. The closer they get toward a toaster matching current tech with a model trained for five bucks, the better. I’d love for VCs to burn money on experimentation instead of scale.
I’m very interested in this approach because I’m heavily constrained by money. So I am gonna be looking (in non appliance contexts) to develop workflows where genAI can be useful when limited to small models running on constrained hardware. I suspect some creativity can yield useful tools with these limits, but I am just starting out.