anon373839

What depreciation? I bought a DGX Spark a month ago for Qwen Flash Next (excellent model, by the way - using it basically exclusively at this point). The hardware has gone up in value 55% since I bought it. I just wish I had bought two of them.

> I always pegged Apple for ANT..

Those two are very different cults. Cultures, I mean.

AA has dramatically altered their methodology twice in just a few weeks. I really don’t see the utility in their composite scoring anymore - it’s basically the US News college rankings of LLMs.

For the time being, LiveBench seems to provide a better calibrated signal.

Agree. They had just fiddled with the formula a few weeks prior to this, too. There seems to be an effort to get a certain result from the model rankings.

Another huge potential loophole in all this is *derivative data*, e.g. LLM-generated summaries of your prompts and context files. Does Anthropic parse its agreements to include or exclude such derived data from terms like "your inputs or outputs"?

Another huge potential loophole in all this is derivative data, e.g. LLM-generated summaries of your prompts and context files. Does Anthropic parse its agreements to include or exclude such derived data from terms like "your inputs or outputs"?

I don’t know if you’ve actually followed this field over the past several years, but a headline fact is that open weights models started far behind the frontier and continue to get closer and closer rather than the opposite trend of the frontier pulling away.

I think you have to put up some facts to justify the assumption that uber-super-ultra-premium intelligence will continue to be significantly more economically valuable than boring old super-ultra-premium intelligence. Most things in reality have a point of saturation. The AI industry doesn't like to think about that; they prefer to make claims based on promises of infinite exponential growth.

> Distilling just works too well.

I don't think the evidence supports this claim. All the important capability gains now come from RL rollouts in real environments. You can't fake that by generating text from a teacher model. Anthropic makes a lot of noise about how China is distilling their models, but all Chinese labs can access is text outputs. They can't even get the logits. Now, compare all of the recent US open-weights models that are far behind the Chinese frontier. Why haven't they simply "distilled" all of those powerful open weight models that are available to them?

I think the idea of emergent properties in LLMs is less and less relevant. Yes, the first few times they scaled pretraining and such models could do few-shot tasks was quite interesting. But the models we use now are really the product of extensive RL hillclimbing on a wide array of curated tasks. What’s interesting now is that the tasks they’ve been fitted to (long running tool call chains interleaved with reasoning tokens) do very useful things.

The comments below are correct re judicial review, Marbury v. Madison. But another way to think of this is that the Constitution is also a law, a statute even, with higher ranking than the laws passed by Congress. So in this instance, the courts are interpreting and applying the higher-ranking statute.

Also: it’s a bit crazy to overturn Wickard simply because so much subsequent development rests on it, but it was wrongly decided. The commerce power shouldn’t ever have been read as broadly as it was.