It sounds hard but it shouldn't not make sense.

1. Solving cost of launching mass has been the entire premise of SpaceX since day one and they have the track record.

2. Ingress/egress aren't at all bottlenecks for inferencing. The bytes you get before you max out a context window are trivial, especially after compression. If you're thinking about latency, chat latencies are already quite high and there's going to be plenty of non-latency sensitive workloads in future (think coding agents left running for hours on their own inside sandboxes).

3. This could be an issue, but inferencing can be tolerant to errors as it's already non-deterministic and models can 'recover' from bad tokens if there aren't too many of them. If you do immersion cooling then the coolant will protect the chips from radiation as well.

4. There is probably plenty of scope to optimize space radiators. It was never a priority until now and is "just" an engineering problem.

5. What mass manufacture? Energy production for AI datacenters is currently bottlenecked on Siemens and others refusing to ramp up production of combined cycle gas turbines. They're converting old jet engines into power plants to work around this bottleneck. Ground solar is simply not being considered by anyone in the industry because even at AI spending levels they can't store enough power in batteries to ride out the night or low power cloudy days. That's not an issue in space where the huge amount of Chinese PV overproduction can be used 24/7.

> There is probably plenty of scope to optimize space radiators. It was never a priority until now and is "just" an engineering problem.

It's a physics problem, as others pointed out, but even if we take it as another "just an engineering problem", have a look at the Hyperloop. Which is similarly just a long vacuum tube, and inside is like an air hockey table, not that big a deal, right?...

> There is probably plenty of scope to optimize space radiators. It was never a priority until now and is "just" an engineering problem.

Well, it's a physics problem. The engineering solution is possibly not cost efficient. I'd put a lot of money that it isn't.

I have no expertise is this area, so I'm not getting into whether or not this idea makes sense.

That being said, this statement strikes me as missing the point:

> Solving cost of launching mass has been the entire premise of SpaceX since day one and they have the track record.

As I understand it, SpaceX has a good track record of putting things into space more cost effectively than other organisations that put things into space.

That is not the benchmark here.

It doesn't matter if Musk can run thousands of data centres in space more cost effectively than (for example) NASA could. It matters whether he can do it more cost effectively than running them on earth.