This is comparable to datacenters in space. We have no idea whether:
a) it is possible to construct such a scanner
b) the results of a scan would be able to diagnose anything
c) the false-positive rate would be low enough to make this useful
But it is probably very good as a source of speculation to hype the valuation of the company, because iff the above issues are solved, then this could be very valuable.
If any company can put profitable data centers in space, it will be SpaceX. But I doubt that any company can. The difficulties of the physics and engineering of cooling seem like they will always outweigh the advantages of keeping your data center on Earth.
I am annoyed by the insistence that the value of this company comes from something that no one has been able to show is possible yet without multiplying it by the obvious risk factor. And they seem to have got other companies like Alphabet[1] and Anthropic to publicize the idea, to give it more credibility.
I do not want my pension to automatically buy shares at $1T, but it looks like it will have no choice.
Talking in terms of "carbon" is misleading. Methane is much more potent than CO2. I don't know why you think methane is broken down at the same rate as it is added.
- Cattle release methane
- Forests are burnt to make room for crops/grazing
- Fertilizer for crops for cattle produces nitrous oxide
I do not claim this adds up to 60%, but to suggest it is zero is incorrect.
re 2: special relativity is not general relativity - large elements will not provide testable predictions for a theory of everything that combines general relativity and quantum mechanics.
re: "GR environments (such as geostationary satellites)" - a geostationary orbit (or any orbit) is not an environment to test the interaction of GR and QM - it is a place to test GR on its own, as geostationary satellites have done. In order to test a theory of everything, the gravity needs to be strong enough to not be negligible in comparison to quantum effects, i.e. black holes, neutron stars etc. your example (1) is therefore a much better answer than (2)
> For instance, when you're calling an airline, it can automatically find your flight details from your email and display it during your phone call.
Is this really the best example usecase they can think of? How often does an individual call an airline? I'm sure in aggregate they get a lot of calls, but I don't think I've ever had to.
It just seems really weird that this is the top example of on-device AI. The other examples mentioned, like "finding the right photos to share with a friend", seem more relatable.