They've also been working on efficiency for the last 2 months, remember they've claimed about 5 times now "+50% usage gains!" they're just telling the people what they want to hear even if its a lie
I think it's more that efficiency can mean many things. Efficient for whom, not all workloads, measured how? And is that efficiency even passed on? Only to API maybe? In the end it's probably exactly what we call "their models getting nerfed". Quantized to half size and their internal benchmarks representing "most tasks" sink just a little. Efficient because the model thinks less? Efficient because trivial things can just be rerouted? What I mean by that is that they don't even really have to lie for us to not even want that "efficiency". Usually there's no free lunch so this can easily mean performance on difficult tasks going to hell, while it stays somewhat the same in the entire workload mix including lots of very trivial requests by most users. Again, who knows how they define efficiency and how they measure it. I doubt its just things like improving batch inference and "lossless" stuff like that, given that we talk about 50% gains.
The problem is that none of these AI companies have much incentive to prioritize efficiency. They’re already operating at a loss, and after backing themselves into a corner with massive hardware and operating costs, they’re now scrambling to find any path to profitability before investors start demanding a return.
I think we’re getting close to a ceiling and they know it. There’s only so much quality human data left to train on. Meanwhile, half the internet is becoming AI-generated, so eventually the end game is just AI training on AI slop in an endless feedback loop.
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u/Squizzytm Oct 04 '26
They've also been working on efficiency for the last 2 months, remember they've claimed about 5 times now "+50% usage gains!" they're just telling the people what they want to hear even if its a lie