Below I link to a small dataset with 130 figures of over $1 trillion (e.g. Apple’s Market capitalization: $1 trillion, or the value of Global Residential Real Estate: $163 trillion).
If something costs trillions, then it might score highly on the scale criteria of some prioritization frameworks. This because even if we have to invest billions to somehow save that money, the savings would be huge.
These numbers might also be helpful to get a sense of the world economy. For instance, World GDP is ~$100 trillion, and more than half of that is US, China, and EU with a GDP of ~$20 trillion each.
Also, people sometimes treat big numbers—billions and trillions—as if they're all the same (c.f. scale insensitivity or scope neglect). Even researchers sometimes confuse big numbers ("Advertising has become an over $500-trillion-dollar global industry" (should be billions) or “The benefits of automated and autonomous vehicles $1.3 quadrillion” (should be trillions)).
So how can you conceptualize $1 trillion? 1 trillion is 1,000 billion. 1 billion is 1,000 million. Houses often costs ~1 million. So 1 trillion ≈ 1 million houses—a whole city.
Some of the figures might be wrong. For instance, because I optimized for selecting very high figures (I had a Google Scholar alert for things like "USD * trillion"), they are more likely to be inflated than other numbers (c.f. optimizers curse).
Also note that not all of these figures are necessarily directly comparable (some figures are stock and some are flow, some of the large figures are notional).
I hope this is interesting or even useful to some people here.