Language models are closing the gaps that still remain at an amazing rate. There are still a few gaps, but if we consider what has happened just in the last year, and extrapolated 2-3 years out....
I think we'll reach "most work done by unattended robots" around 2070. But we'd be better off if started working on post-labor-scarcity economics ASAP--might as well start learning to swim before the ship sinks. It might even be fun.
It doesn't really matter if they can or can't do the work. If enough execs are convinced AI can replace the workers, they will replace the workers, and management will have moved onto other things before they suffer consequences.
With all due respect, this attitude typically comes with age. I see it in myself, too (I'm over 50).
You're right that an important reason why it's hard to replace those 30+ year old systems, and that part of the reason is that the current devs are not necessarily at the same level as those who built the original. But at least in part, this is due to survivorship bias.
Plenty of the systems that were built 30-50 years ago HAVE been shut down, and those that were not tend to be the most useful ones.
A more important tell, though, is that you see traditional IT systems as the measuring stick for progress. If you do a review of history, you'll see that what is seen as the measuring stick changes over time.
For instance, in the 50's and 60's, the speed of cars and airplanes was a key measuring sticks. Today, we don't even HAVE planes in operation that match the SR-71 or Concorde, and car improvements are more incremental and practical than spectacular.
In the 70s and into the 80s, space exploration and flying cars had the role. We still don't have flying cars, and very little happened in space from 1985 until Elon (who grew up in that era) resumed it, based on his dream of going to Mars.
In the 90s, as Gen-X'ers (who had been growing up with C64/Amiga's) grew up, computers (PC) were the rage. But over the last 20 years little has happened with the hardware (and traditional software) except that the number of cores/socket has been going up.
In the 2000s, mobile phones were the New Thing, alongside apps like social media, uber, etc. Since 2015, that has been pretty slow, too, though.
Every generations tends to devalue the breakthroughs that came after they turned 30.
Boomers were not impressed by computers. Many loved their cars, but remained nostalgic about the old ones.
X-ers would often stay with PC's as the milennials switched to phones-only. Some X-ers may still be a bit disappointed that there's no flying cars, Moon Base and no Mars Colony yet (though Elon, an X'er is working on those).
And now, some Milennials do not seem to realize that we're in the middle of the greatest revolution in human history (or pre-history for that matter).
And developers (both X'ers and millennials) in particular seem to resist it more than most. They want to keep their dependable von Neumann architecture computing paradigm. The skills they have been building up over their career. The source of their pride and their dignity.
They don't WANT AI to be the next paradigm. Instead, they want THEIR paradigm to improve even further. They hold on to it as long as they can get away with it. They downplay of revolutionary it is.
The fact, though, is that every kid today walks around with R2D2 and C3PO in their pockets. And production of physical robots have gone exponential, too. A few more years at this rate, and it will be everywhere.
Walking around today, 2025 isn't all that different from 2015. But 2035 may well be as different from 2025 as 2025 is to 1925.
And you say the West is declining?
Well, for Europe (including Russia), this is true. Apart from DeepMind (London), very little happens in Europe now.
Also, China is a competitor now. But so was the USSR a couple of generations ago, especially with Sputnik.
The US is still in the leadership position, though, if only barely. China is catching up, but they're still behind in many areas.
Just like with Sputnik, the US may need to pull itself together to maintain the lead.
But if you think all development has ended, you're like a boomer in 2010, using planes and cars as the measuring stick that thinks that nothing significant happened since 1985.
> Is this because of a fundamental limitation, or because the post-training sets are currently too small (or otherwise deficient in some way) to induce good thinking patterns?
"Thinking" isn't a singular thing. Humans learn to think in layer upon layer of understandig the world, physical, social and abstract, all at many different levels.
Embodiment will allow them to use RL on the physical world, and this in combination with access to not only means of communication but also interacting in ways where there is skin in the game, will help them navigate social and digital spaces.
Being unpredictable has advantages and also disadvantages, depending on setting.
Though with an AI race going on and Musk practically living in the White House, I can't imagine the US would let China have Taiwan without a fight right now.
Also, forcing TSMC to build a number of modern fabs in the US is sort of a warning to China to stay away AT LEAST until those fabs are done. If China attacks right now,I think we would see the full might of the US forces coming to their defense.
AI right now has the same role as nukes had during the cold war. Nobody really knows how quickly it will develop, and many scenarios would allow those who get it first to take out all enemy nukes without much risk of receiving a retaliatory strike.
For instance, AI may make it possible to build a virtually perfect missile defense against ICBMS, it may may allow perfect tracking of subs and other submarine threats, it may power drone swarms capabable of disabling any integrated air defense network, and even to destroy all enemy missile siles and nuclear subs whil minimizing loss of life.
The US is not going to let China get there first, if they can stop it.
> AI may make it possible to build a virtually perfect missile defense against ICBMS
Massed ICBM defense is a matter of sheer volume - with the current GMD system the US can throw enough exoatmospheric kill vehicles (and THAAD to handle the leftovers) to counter a handful of re-entry vehicles from a smaller nuclear power like North Korea or Iran. Not hundreds (vs China) or thousands (vs Russia) that you would see in a peer-level nuclear exchange where everyone has multi-megaton MIRVs, decoys, and SLBMs with much shorter flight times.
Some fantasy future AI with the right sensors may perfectly track all of that sub-orbital mayhem. Without an enormous fleet of thousands of kill vehicles to actually defend against that threat, and the logistics to keep that fleet operational, it can do nothing about it. Building and supporting that sort of strategic defense is obscenely expensive, and as such it remains a Reagan-era fever dream.
Things have changes since the Raegan era. There are a couple of elements to ICMB defense:
1) If you can strike the the ICBM's before the MIRV's separate, you only need a fraction of the number. To do this, you need to already have the interceptors (or whatever else used to shot them down) in orbit before the ICBM's launch.
Independently of AI, Starship is making it much cheaper to place objects in orbit, and can help with this. (Though it could trigger a first strike if detected, it might be possible to hide interceptors within Starlink satellites, for instance.)
2) Coordination and precision. This is what wasn't in place at all in the 80s. I'm old enough to remember when this was going on, and labelled impossible. I still remember thinking, back then: "This is impossible now, but will not remain impossible forever".
Whether it applies to interceptors already placed in orbit, novel weapons such as lasers, typically also placed in orbit or interceptors intended to stop reentry vehicles one faces a coordination problem with time restrictions that makes it very hard for humans or even traditional computer algorithms to solve properly.
This, more than the volume, was the fundamental showstopper in the 80s (the willingness to pay was pretty significant).
Now, with AI tech, plenty of known options open up, and an unknown number of things we didn't think of yet, could also open up.
Accuracy and coordination is the most fundamental one. Here AI may help distribute the compute load into satellites and even independent interceptor vehicles. (Both by making them more autonomous and by improving algorithms or control systems for the dumber ones.)
But beyond that, AI may (if one side achievs a significant lead) also a path to making manufacturing large numbers much cheaper, meaning one could much more easily scale up enough volume to match whatever volume the enemy can deploy. Also, with more advanced tech (allowed by ASI), interceptors can potentially be made much smaller. Even a pebble sized chunk of metal can stop most rockets, given the velocities in space. The hard part is to make them hit the target.
Basically, what I'm saying is that whoever has ASI first may at minimum get a time window of technological superiority where the opponent's ICBM's may be rendered more or less obsolete.
In fact, I think the development of the Poseidon by Russia was a response to realizing decades ago that ICBM's would eventually be counterable.
However, AI tech will possibly even more suitable for detecting and countering this kind of stealthy threats. Just like it is currently revolutionizing radiology, it will be able to find patterns in data from sonars, radars, satellites etc that humans and traditional algorithms have little chance to detect in time.
