This is fine if you are comfortable showing that SOME chip can be run in space at SOME cost, but it skips all the details that drive whether a data center in space is will work logistically and economically. There are many of these details, including but not limited to:
- Is it possible to use convective cooling in space? If so, how does one design it so that the convective fluid doesn't just dissipate? If not, can chips be designed that successfully evacuate heat radiatively or do the chips burn themselves up?
- What is the balance between collecting energy on the sun side of the system and protecting equipment from cold on the shaded side? Does the equipment have to survive large temperature cycles? If it can't does the data center have to use thrust to rotate itself to keep the hot side hot and the cool side cool? What would that mean for design cost, for refueling?
- How does all of this equipment (solar panels, batteries, GPUs, communication equipment, thrusters, control systems) deal with the high levels of ionizing radiation in space?
- The design and duty cycle of all these pieces of equipment implies various levels of refueling, repair, etc. Assuming all that can actually be done successfully at rocket's length, is the total cost within what the market will pay?
These details whether a data center can be made operational at all and if so whether their economics make them worth doing.

What Google is doing strikes me as a pretty sensible step - put a few chips in space and see what happens.

What Elon Musk does is claim that something will be solved next year and do some pump-and-dump crap around a company he claims will be doing that thing next year.
Where is "Full Self Driving", as in you can actually let the car drive and not monitor it? Where are the fleets of "Optimus" robots? Where is Tesla's super-awesome battery tech? And the Cybertruck that can float?
Oh right, they'll all be here at {year} + 1.

Sadly, it often is faster to do that than work within the old bureaucracy and its entrenched people, rules, and regulations.

This is pretty much a textbook case of a stopped clock being right twice a day.

Notwithstanding Hegseth's absymal record prior to and within the Department of Defense/War/Being Tough, the war in Ukraine has showed how different war can be run than the way our military is currently organized.

Even if if you don't buy the army having "Transformers" type robots punching each other out (and I don't), it's clear that there is going to be a lot of "AI"-ish and highly automated stuff fighting wars. There needs to be a unit devoted to understanding FPV drones on land, sea, and air, plus all of the infrastructure for using them, defending against them, the communications to run them, training people to manage these kinds of weapons and units.

...Modern ejector seats cause spinal injuries from which full recovery is "unusual". Smashing the spines of pilots might not be the most effective way to boost morale.

$22,000 per hour. The F35 will be nearer $42,000 per hour. These are under-estimates, as they do not include price hikes, crashes that close businesses, crashing into businesses, crashing into heavily-populated sports centres, etc.

Here's the question. What is the difference in work between review and understanding the code versus writing the code in the first place?

I think the results vary a lot by scenario. In some cases, the business problem, the requirements, and the implementation are clearly separable, and exceptions can be clearly defined and partitioned a prior. These are amenable to automatic code generation and "higher level of abstraction".
In other cases, the business problem and requirements can only be untangled by asserting some sort processing logic, and then working from there. The more granular the logic that has to be specified, the closer you are to saving time by just writing the code yourself.

I suspect that the estimates of massive labor savings put almost all work into the first category. In reality, I believe quite a lot of work lands in the second category, and much of the work in the first has already been automated.

Case in point, TFA mentioned writing a calendar app for her family. There are a bajillion calendar apps for families, and nearly every piece of enterprise software now ships with calendaring and project management features of some sort.
So this was a hobby project, or her family has some extremely unique un-apped needs. Who knows, as an Amazon exec she may need to schedule their personal helicopter, private jet, and security detail and that probably is more complex than everyone else's soccer practices, doctor's appointments, and school forms. But I have a hard time seeing how there's a huge space of unmet demand for calendar apps. The biggest problem with calendar apps is all the stuff that is going on outside of them that people do not want to put in their calendar. AI does not change that.

There is a whole culture around the tech bros that enables them to believe that what is good for them is ipso facto good for society. This allows them to think of themselves not as the ruthless capitalist barons they are, but as actual helpers and patrons of society.

This is also the basic manifesto of venture capital. Profits are inherently good and "consolidating fragmented markets" (i.e. monopolism) is also inherently good because they bring "innovation" to "everyone".

It's an exaggerated kind of neoliberalism from people who spend very little time outside of a rather narrow world.

The notion that AI can solve political problems is pure balderdash. Political problems get solved when people organize and interact with each other in ways that create stability and trust and enable societal or policy changes that address the problem. You can run AI a million ways to "solve" climate change, but until something gets people to negotiate and adopt different ways of operating, all AI has done is burn compute (ironically exacerbating climate change) while generating another set of outputs that nobody pays attention to.

But these are not people who like the slow boring of political work; they'd rather fantasize about some computer doing that.

Whether we like it or not, whether we use Windows or not, Microsoft is going to insist on adding the tax whether the PC comes shipped with Windows or not. This is illegal bundling, Microsoft has essentially ignored prior rulings, and it's pretty much a certainty the courts will be fine with this.

"How did you get hired?"
"I was top at Fortnite. You?"
"Combat flight sims."

"Damn, two Boeings just crashed over an oil refinery on city limits and took out half the city."
"Did you remember to save your position beforehand?"
"Yeah."
"The reload and continue from there. No-one will notice."

I love computer games. I have XPlane 12 and many scenery packs. I rank well on Elite:Dangerous. From the sounds of it, the FAA would see me as over-qualified. In reality? There's no way in hell I'd be taking those kinds of risks with real lives. There's a huge difference between having good reflexes and a good eye, versus having the complex 4D spacetime relationship mental models needed for robust air traffic control.

You see, Linus has embraced transhumanism and is testing out the new kernel as a supplementary brain function, using MOSIX to offload all of the irritable comment generation at yet more nonsense on the mailing list to an Elizabot that he has written specifically to do this. This saves his actual brain for real work.

AI is not currently capable of performing any meaningful conceptual abstraction, it is only capable of very basic mechanical abstraction. Abstraction is itself multi-dimensional. Nor is there any indication that AI could ever perform multi-dimensional abstraction or multi-dimensional decomposition. Precious few humans are capable of it either, but some can.

No, AI would need humans because the best system is not a pure system but a hybrid system, and that means transhumanism with AI operating as an additional brain function rather than as a replacement for a brain.

Tool calling is, yes, but the current approach to it is (a) overt not transparent to the user, (b) not designed for the purpose I've outlined, and (c) not remotely good enough for the AI to be able to manage data through such tools.

Yes, you can connect an AI to a PostgreSQL database. Whoopee. Not even close to an AI detecting via classifiers that some of the data is relational in nature, transparently setting up its own PostgreSQL database in response, and using that proactively as an additional way to examine the data. You would need to explicitly set up the constructs, explicitly set up the databases, explicitly tell it when, where, and how to use the database, and if that particular usage conflicted with a way the AI actually worked, the AI would not be able to use it effectively. That's not remotely close to what I'm talking about.

What I'm talking about is much, much deeper than that. AIs lose focus when there is too much information currently in the system. Absolutely nothing stops an AI from using a document database as a virtual memory in which it can page in and out conceptual spaces so that the focus on any given step of a problem concerns just the problem. Other than such a concept not existing yet, which is kind of a limiting factor. But you need to know what connects to what. The AI could use an ontology reasoner for that, or a relational database, or an external graph. But you cannot provide those tools and you cannot configure them, for the simple reason that the AI is the only entity with any detailed map of how the underlying neural net is connecting those ideas up.

The AI itself has to select the tools, has to configure the tools, has to do all the work. Which means YOU cannot use any sort of API. YOU should have no involvement in the underlying mechanisms needed for the internal NN housekeeping.

And that's your other error. You're assuming this is about user data. No. It's about housekeeping operations within the NN itself to maximise functionality, it has nothing to do with the user side of things at all. If the user was capable of setting up dynamically structured databases that mutated with each step of a decomposed analysis, you'd have done everything the AI would be doing and you wouldn't need the AI to begin with.

You cannot do this work, you cannot even assist in this work, it has to be dynamic and it has to be utterly invisible to the operator.

We don't need bigger models, at this point. What we need is multi-dimensional decomposition, problem space transforms, and the ability for AI to use external tools (such as SQLite, memcached, etc) so that it can externalise static data that it needs to not corrupt accidentally but still keep in easy access.

If we had that, most of the things "bigger models" will do will actually end up being done better, quicker, with fewer compute resources.

To be useful in deep space, you're going to have to deal with very harsh radiation, far harder than the Vikings dealt with. You really want to have some large number of computers, where the number has to be odd and exceed 5. The reason it has to exceed 5 is that you need 5 in order to be able to use the Byzantine General's Problem to discern which computers are working correctly and which are radiation damaged. Since some will be damaged over time and you want 5 computers still operating around the time the power runs out, you have to start with more than that.

Massively radiation-hardened highly-robust low-power chips could be done. If you were clever enough, you could do this as a wafer-scale system where you marked parts as bad and networked around them. Free space optical communication across the wafer might be doable.

They might well still end up being low-density CMOS discrete logic.

Lead-lining the electronics isn't as much of a problem if you've launchers capable of handling heavy objects and don't mind burning through a lot of ion drive propellant for course corrections. This would improve the hardening beyond what can be done through the usual operations.

But, yeah, you'd need defect-free microelectronics. You really can't handle F00F bugs or defective instructions once you pass Earth orbit. And I can't think of anyone who knows how to do that.

Well, not just the solar system but the heliopause as well, and the near-Sol galactic winds. We can estimate the effects of space from just about any ancient near-Earth space junk, but I'm not sure how we'd go about calculating the impact of the galactic winds on something. The Voyager sensors won't be sending back nearly enough high-quality data to establish that.