The book "The Great Leveler: Violence and the History of Inequality from the Stone Age to the Twenty-First Century" by Walter Scheidel makes a similar argument:
> Are mass violence and catastrophes the only forces that can seriously decrease economic inequality? To judge by thousands of years of history, the answer is yes. Tracing the global history of inequality from the Stone Age to today, Walter Scheidel shows that inequality never dies peacefully. Inequality declines when carnage and disaster strike and increases when peace and stability return. The Great Leveler is the first book to chart the crucial role of violent shocks in reducing inequality over the full sweep of human history around the world.
> Thriving companies like Arc and Linear build an entire aesthetic ecosystem that invites users and advocates to be part of their version of the world.
Afaik Arc still has no revenue and no clear path to a business model, so I’m not sure I’d call it a “thriving company.” I like and use their browser but I fully expect it to die once the money runs out, because people won’t pay for a better looking browser.
It's a beautiful language that's a joy to write. It's safe and ergonomic, and has an extremely powerful type system. I'd say those are good reasons to use Swift.
I had to look this up. I'm doing my master's in computer science at ETH Zurich, where it is ranked #5 globally according to THE [1], and I pay 804 CHF (874 USD) per semester, i.e. 1608 CHF (1,748 USD) per year. I just had to check MIT's tuition.
> The median annual price paid by an undergraduate who received an MIT Scholarship was $12,715 in the 2022–2023 academic year. [2]
You're basically right, MIT (ranked #3) is almost eight times more expensive than ETH (ranked #5). To be clear though, ETH Zurich is in Switzerland, which is not in the EU.
> My limited understanding is that Nerfs are compute-heavy because each cloud point is essentially a small neural network
There's no point cloud in NeRFs. A NeRF scene is a continuous representation in a neural network, i.e. the scene is represented by neural network weights, but (unlike with 3D Gaussian Splatting) there's no explicit representation of any points. Nobody can tell you what any of the network weights represent, and there's no part of it that explicitly tells you "we have a point at location (x, y, z)". That's why 3D Gaussian Splatting is much easier to work with and create editing tools for.
[1] https://marigoldmonodepth.github.io/