It would be great, but it could never happen. All the marketing of AI is around speculation of what it could do.
Investors know what a train is, what it does and how much it costs. They don’t know any of those things when it comes to AI, so they’re willing to spend a lot, because they were promised a lot.
But what about this promise makes it so uniquely seductive?
There are a million guys with ideas for cars that will go 750km on a thimble-full of Fresca, robot butlers that can’t turn evil because they don’t have red LEDs in the eye positions, and 200:1 data compression as long as you never have to decompress it. They must all be looking at Altman and company and asking where their bubbles.
I sadly suspect the charm is “we can sack some huge percentage of workers if it delivers”
But what about this promise makes it so uniquely seductive?
Part of it is, as you pointed out, just the elimination of costly labor. That’s a capitalist’s wet dream. But the main thing that makes it attractive as a slick, highly marketable investment vehicle is that AI models are inherently black boxes.
There are ways you can examine the ways they work (for example, researchers found that the parts of an LLM that “understand” one topic, like money, can also simultaneously “understand” other different, yet related things, like value, credit, etc), but we can’t truly comprehend everything about them. It would be like looking at a math problem billions of equations large and assuming we could hold the whole equation perfectly in our brain and do the mental math to solve it. We can’t.
That means that instead of seeing “here’s our robot that is currently capable of this, but these are the components that could be upgraded/replaced, X is an issue it faces because of Y” and so on, instead you get “It’s not good at this yet, but it will be if you just throw a few billion dollars more compute at it, we promise this time.”
Problems are abstracted away to “something that will fix itself later,” or something that “just happens, but we’ll find a way to fix it”, and not any kind of mechanical constraint a VC fund manager might be able to understand.
And because the rest of the market is really slow and barely above inflation so not really worth much to invest in while AI is going like it’s the good ol’ days. That’s how the money boys see it anyway.
Do you know what good mass transit could do though? Imagine cities without parking lots and garages. Imagine having spaces that are much safer and more comfortable to walk. Imagine solving the housing crisis, since you can now build downtown complexes where those parking garages were.
Imagine getting most semis off the road and reducing road repairs by more than half.
Trains could do a lot, and it doesn’t take much imagination.
It would be great, but it could never happen. All the marketing of AI is around speculation of what it could do.
Investors know what a train is, what it does and how much it costs. They don’t know any of those things when it comes to AI, so they’re willing to spend a lot, because they were promised a lot.
But what about this promise makes it so uniquely seductive?
There are a million guys with ideas for cars that will go 750km on a thimble-full of Fresca, robot butlers that can’t turn evil because they don’t have red LEDs in the eye positions, and 200:1 data compression as long as you never have to decompress it. They must all be looking at Altman and company and asking where their bubbles.
I sadly suspect the charm is “we can sack some huge percentage of workers if it delivers”
if firing people is the ultimate good, maybe we can get the corpos behind UBI so nobody cares too much about getting fired?
Part of it is, as you pointed out, just the elimination of costly labor. That’s a capitalist’s wet dream. But the main thing that makes it attractive as a slick, highly marketable investment vehicle is that AI models are inherently black boxes.
There are ways you can examine the ways they work (for example, researchers found that the parts of an LLM that “understand” one topic, like money, can also simultaneously “understand” other different, yet related things, like value, credit, etc), but we can’t truly comprehend everything about them. It would be like looking at a math problem billions of equations large and assuming we could hold the whole equation perfectly in our brain and do the mental math to solve it. We can’t.
That means that instead of seeing “here’s our robot that is currently capable of this, but these are the components that could be upgraded/replaced, X is an issue it faces because of Y” and so on, instead you get “It’s not good at this yet, but it will be if you just throw a few billion dollars more compute at it, we promise this time.”
Problems are abstracted away to “something that will fix itself later,” or something that “just happens, but we’ll find a way to fix it”, and not any kind of mechanical constraint a VC fund manager might be able to understand.
It’s that LLM output looks like human writing, so it looks like they might be able to do anything a person can.
It’s that, and a really impressive working prototype.
And because the rest of the market is really slow and barely above inflation so not really worth much to invest in while AI is going like it’s the good ol’ days. That’s how the money boys see it anyway.
Do you know what good mass transit could do though? Imagine cities without parking lots and garages. Imagine having spaces that are much safer and more comfortable to walk. Imagine solving the housing crisis, since you can now build downtown complexes where those parking garages were.
Imagine getting most semis off the road and reducing road repairs by more than half.
Trains could do a lot, and it doesn’t take much imagination.
I mean there was a train bubble once, simpler times tho
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