These deals give Nvidia more exposure to that, in both directions. Certainly Nvidia shareholders should be cognizant of this. But nothing structurally problematic is occurring here.

I don't know if it makes sense to call that person an SWE, and some people currently employed as SWEs either won't be good at this or aren't interested in doing it. But the existing pool of SWEs is probably the largest concentration of people who'll end up doing this job, because it's the largest concentration of people who've thought a lot about, and developed taste with respect to, how software should work.

What's missing here? What else should they be doing? What are their competitors doing, in any space relevant to their markets, that's much different? None of these critiques ever seem to say.

If AI ends up being another 'normal' technology, Apple's advantages in distribution (~2B active devices, with a user base that installs updates pretty reliably), ability to give their AI tools access to your existing data and apps, and general facility with packaging tech so consumers actually understand what it's good for, put them in an extremely strong position to capture value from it.

If AI ends up being something other than a 'normal' technology, if we really are a few years from building the sand god, well, all bets are off, and it's a little silly to evaluate the strategic planning of an individual company against that backdrop.

The authors speculate that this pattern is a consequence of reasoning models actually solving these puzzles by way of pattern-matching to training data, which covers some puzzles at greater depth than others.

Great. That's one possible explanation. How might you support it?

- You could systematically examine the training data, to see if less representation of a puzzle type there reliably correlates with worse LLM performance.

- You could test how successfully LLMs can play novel games that have no representation in the training data, given instructions.

- Ultimately, using mechanistic interpretability techniques, you could look at what's actually going on inside a reasoning model.

This paper, however, doesn't attempt any of these. People are getting way out ahead of the evidence in accepting its speculation as fact.

So, it doesn't make sense to say that what LLMs do is Moravec-easy, and therefore can't be extrapolated to predict near-term progress on Moravec-hard problems like robotics. What LLMs do is, in fact, Moravec-hard. And we should expect that if we've got enough compute to make major progress on one Moravec-hard problem, there's a good chance we're closing in on having enough to make major progress on others.

Even more annoyingly, politicians wrote in an exception for themselves. In combination with the way campaign finance works in the US, this means that if you've ever give your number to any political campaign, it will be passed around forever and you'll have multiple politicians begging you for money for months leading up to every election. Each individual campaign/organization seems to respect 'STOP,' but once your number is on an e.g. 'Has ever donated to a Democratic candidate' list, there's seemingly no way to get it off for good. Thanks, Obama. (I gave him $50 in 2008.)

Now, initially this still looks like it's going to reduce demand for janitors by 99%. So it's still going to cause mass unemployment, right? Except, it's going to substantially reduce the cost of janitorial services, so more will be purchased. Not just janitorial services, of course. We'll deploy such robots to do many things at higher intensity than we do today, and as well as many things that we don't do at all right now because they're not cost effective. So in equilibrium (again, the transition may be messy), with 99% automation we end up with an economy 100x the size, and about the same number of humans employed.

I know this sounds crazy, but it's the historical norm. Today's industrialized economies already have hundreds of times the output of pre-industrial economies, and yet humans mostly remain employed. At no point did we find that we didn't want any more stuff, actually, and decide to start cashing out productivity increases as lower employment rather than more output.

It's really only AGI, by eclipsing human capabilities across all useful work, that breaks this dynamic and creates the prospect of permanent structural unemployment.

Nor does autonomy have to be limited to car-sized vehicles. It could be used for larger vehicles, perhaps intermediate between car and bus size — once you don't have to pay for a driver, operating a larger number of smaller vehicles is much more viable. These could be dynamically dispatched and routed, eliminating many drawbacks of existing bus service.

I think I'm going to enjoy how surreal widespread access to generative AI will make the world.

What would probably make more sense is to just add a single-button auto-land feature, that sends an emergency destress call and configures and invokes existing automatic navigation, approach, and landing features to find the nearest appropriate airport and land. Given how rarely this would be used, there wouldn't be a need for the system to navigate complex traffic patterns, as ATC could just clear other aircraft out of the way. Something like this has recently become available for general aviation aircraft, but I haven't heard about anyone working on it for airliners yet.

I've seen a lot of mockery of this request, but I suspect people aren't considering the wide variance in employee quality that can exist within a mismanaged organization. What Musk was asking for here wouldn't be a good way to evaluate skilled, conscientious developers, but it would be a pretty effective way to rapidly identify people who are basically incompetent or just aren't really doing anything.

Also, per later comments, this feature only kicks in when you're actually viewing SDR content, so there's no downside (even contrast) in everyday use.