Most parts of the world look at McMansions and feel unsettled, particularly when they are marketed as "aspirational": sell your soul in order to get the biggest place you can get a mortgage for on your salary, so you can never leave your job.
The other benefit with having multiple projectors/lasers at different angles are that you can use different wavelengths: one that causes polymerisation, one that inhibits it.
> Suddenly, no one wants to invest in a company that requires significant capital
But this is the point: the reason Uber is successful isn't particularly because of their technical excellence or innovation, it's because they've used massive amounts of capital to very knowingly buy the market.
This is an example of one of the nefarious sides of capitalism -- a flaw -- not a celebration of how great it is at solving problems (which is genuinely is in a lot of circumstances).
If Uber didn't have as much capital, smaller distributed co-ops may well have prospered.
I am no cheerleader for capitalism, but for any business whose core offering is a marketplace (as with Uber/Lyft, who connect buyers of rides with sellers of rides), then building out that marketplace requires solving the chicken-and-egg problem, and typically that is done through advertising, marketing, pricing, customer loyalty, etc.
Much of that requires capital, and the asset you end up with after investing that capital is a large user/subscriber base, which can lead to other competitive advantages. It is true that having a large user/subscriber base does not imply technical excellence or innovation, but it is a business asset nonetheless.
But if that asset can be easily siphoned off, then there is less of an incentive to build it out to begin with.
Fundamentally, if a human can drive a car with nothing but two relatively poor eyes with a pretty small field of vision set in a single location inside the vehicle, an AI can be trained to drive using the same inputs - any more sensors are a bonus.
The other thing people forget is the bar isn't that high: self driving vehicles don't have to be perfect, they just have to be better than humans are. All of the "whatabout" edge cases people proffer as examples of areas an AI would have trouble with, people have trouble with too. The difference is that once an AI learns to solve that edge case, it doesn't have to relearn going forward.
https://www.goodreads.com/book/show/70420.The_Undercover_Eco...