101–110 of 151 posts
Author here. I made these plots because I had been searching for them for months and never found quite what I wanted: how the cheapest way to reach a fixed capability level has moved over time. Artificial Analysis publishes enough data to reconstruct it. If someone knows of a source that already tracks this, with historical prices, please share.
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Your computer may not feel faster than a 20 year old PC, but CPUs have gotten way, way faster. Compare the AMD Ryzen 7 9800X3D to the AMD A12-9800, you go from 4 cores to 8, and you double the power consumption. But it's not twice as fast. It's 10 times faster. Depending on the exact metric, it could be as little as twice as fast or as much as 1000 times faster, depending on the exact operations. On top of that memor…
I kind of hoped my point would come across more clearly. Obviously my current computer is faster than one from 20 years ago. But, it doesn't really react more quickly. It just does more: higher resolution video, advanced graphics, etc. The moment we add capability we rush to use it all up. This is not _always_ bad when it comes to computing, but I wouldn't mind if people in general had more restraint.
AI datacenters, absolutely. People? Nah we probably use 10 percent for 90 percent of the time.
But when I need to compile MAME, oh boy is it nice having a fast CPU.
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And why won’t the frontier models continue to become better? The open models are getting better but so are the frontier models. The frontier models might remain in a constant race to remain ahead
They are running out of novel, clean training data and compute. There is probably a limit to how much improvement can be squeezed out of LLMs. Recent improvements have been more about orchestration and "reasoning" loops (i.e. iteratively feeding context back through the model).
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I kind of hoped my point would come across more clearly. Obviously my current computer is faster than one from 20 years ago. But, it doesn't really react more quickly. It just does more: higher resolution video, advanced graphics, etc. The moment we add capability we rush to use it all up. This is not _always_ bad when it comes to computing, but I wouldn't mind if people in general had more restraint.
I suppose that's true superficially, but how many of us utilize 100 percent of our CPU and GPU power. AI datacenters, absolutely. People? Nah we probably use 10 percent for 90 percent of the time. But when I need to compile MAME, oh boy is it nice having a fast CPU.
If we're talking about video game graphics it's a bit of a mixed bag.
If we're talking about local LLM models then you need all the power you can get.
Etc, etc.
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Xiaomi robots do this in double digit seconds. Still slow compared to humans, but Chinese robots will be as successful as Chinese EVs, phones and solar panels.
Which is to say, they'll be banned in the US.
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> ultimately there are no starving fat people It has been credibly hypothesized that fat people are indeed malnourished. What would you do if all your food sources were appallingly lacking in essential nutrients and the sorts of things your body craved in order to get fed? You would begin to eat more, and more and more, seeking to satisfy those cravings for nutrition. But if nutritional cravings aren't satisfied, you…
>What would you do if all your food sources were appallingly lacking in essential nutrients and the sorts of things your body craved in order to get fed? When I hit 300 lbs I might suspect there was a flaw in my reasoning.
I think what a lot of people miss about jevon's paradox is the elasticity of demand of the underlying resource textiles had jevons paradox, and many more textile workers were employed even when textile machines were being created, until we saturated the demand for cheap clothing in the world and then textile workers were kaput (same for farming, and horses) software is currently undergoing jevons paradox, but it's ve…
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Xiaomi robots do this in double digit seconds. Still slow compared to humans, but Chinese robots will be as successful as Chinese EVs, phones and solar panels.
Which is to say, they'll be banned in the US.
It’s way too early to tell the true cost of intelligence. Inference is still heavily subsidized, and training is apparently being funded by a mountain of free money
How do we know inference is heavily subsidized? Are all these third party Chinese model providers subsidizing the true cost?
Balance sheets
> Are all these third party Chinese model providers subsidizing the true cost?
It could be argued the subidies are even heavier than American model providers as the price war among Chinese providers is so fierce. https://www.scmp.com/tech/big-tech/article/3358868/after-tri...