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The base training data isn't the same for all the models. We know that there's likely enormous overlap, because of low hanging fruit like the Common Crawl. Yet the different companies fight tooth and nail to either semi-legally scrape more data, or strike deals with entities like Reddit or Bloomberg for access to theirs.
And even then, they have different post-training and fine tuning. There might be overlap between different companies because they at least partially outsource to data-annotators in places like Nigeria, India or Vietnam. Even so, anyone who has used all of these models can tell you that them giving the same answer is unusual, for most non-trivial questions where there isn't a canonical solution.
Note that the prompt asked about how to implement tariffs easily. Not effectively. This was the maximally easy, non-rigorous solution.
You're underselling the size of Common Crawl. At that pretraining scale, the emergent properties of the model are near identical. Shady data is useful for turning models into experts at narrow tasks. But if the task is generic and isn't gated by access to shady data, then the models will give identical answers.
Broadly speaking: Model_output = function_of(prompt, post trained personality, conditioning information)
I am assuming that the prompts were identical and the post-training personalities don't factor into this exercise. That leaves conditioning information.
Fields like programming (Github) and News (Twitter) have private sources that Openai or Grok can leverage to get an edge over their competitors. Other fields like Physics, gaming & image creation are amenable to simulation and therefore improvement through RL. Lastly, private data collection can help fill in gaps for applied fields where there is a rift between what is written and what is understood. (medicine, law, etc). Macro-economics is none of these 3. There is no simulation, no private corpus, no information that an expert can feed into an LLM that improves its intuition on how markets work. In such cases, the LLM will default to a logical process that emerges from the median knowledge of its public corpus.
This means that models will give identical answers.
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So most likely there is some canonical solution. Problem is I am not at all familiar with tariff studies, or whatever it's called, and very few people are. So a lot of people's first instinct especially since it's the Trump administration is they came up with it randomly or AI or some kind of stupid way.
I'm no expert, but my understanding of the consensus opinion of most economists is that tariffs are net negative, and negative sum for both partners in trade. There are other relevant concerns, such as geopolitical leverage, but I've yet to be swayed from my belief that these tariffs are fundamentally stupid and not imposed in a reasonable fashion.
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