A few years ago in college we discussed AI and the instructor showed a video regarding AI or rather AGI. The point is super intelligent AI is a far off problem it's AGI just like an average Joe which will be a problem for workers. You can have a million instances of Joe AGI working 24/7/365 doing call center work, programming, reservations, any job not requiring physical contact.
This sentence makes me concerned for the future.
Most small towns that used to rely on agriculture (in my area of the US at least) are hollow. Poverty, drug abuse, and all the problems that accompany.
So if that's the future with AI. . . . Uh oh.
If we are seeing problems in former farm country now, a century later, it suggests people are far less flexible than economists wish they were.
(Fwiw I was just reading a blurb at the feed store about autonomizing both tractors and their attached machinery)
Blithely approaching knowledge and creative work and trades and manual labor in the same way is probably going to have all kinds of unintended consequences that include employment but go well beyond it.
Especially if we start with the premise that while the model is valuable and deeply dependent on training data, we shouldn't make any attempt to structure the value derived so it finds its way back to the training data.
Have you found your real purpose? Is something stopping you from pursuing it? Yes? Well, AI researchers are trying to fix that. If they succeed, you won't have to spend the rest of your career doing a robot's job.
My recency bias aside, its uncanny how much his concepts infuse this Economist article.
Almost every paragraph echoes a concept from Wardley’s writings; diffusion versus evolution, inertia, co-evolution of practices and capabilities, capital flows, initial innovation versus refinement of an idea, and with hindsight, eventual ubiquity.
I greatly enjoyed the serendipity of this article appearing alongside my holiday reading.
One point missing from the article is the increased speed of diffusion via communication, and the relatively evolved states of compute, and other required underlying infrastructure for AI.
One could map the user needs of farms and farmers and todays knowledge enterprises, alongside the underlying infrastructure required to deploy tractors and AI, and draw some conclusions.
Which version of the book are you enjoying? I believe the content is largely the same, but the formatting / treatment of images can vary.
One massive difference between tractors and AI - not addressed in TFA - is that unlike tractors, using AI doesn't impose a massive upfront cost on the adopters. Many of the major software platforms are rolling out AI without even being asked to. People using Photoshop, MS Word, etc, suddenly find that AI-based functionality is appearing in their next product release. This is radically faster than the decades it took for tractors to achieve dominance.
For example these days we live in a borrow/lease economy. Back then the companies wanted to sell you a tractor, but today no one wants to sell you a LLM, they want to sell you a service they maintain. Also interest rates and risk was pretty high in those Years making borrowing more difficult.
https://chat.openai.com/share/2aba9eaf-8868-41bf-a152-c28f9e...
From a literary perspective, that’s horseshit.
AI and LLM on the other hand are operating equipment and fully variable costs. Also software, and immaterial.
The only real similarities between a tractor and a LLM is that both at some point were new inventions, and that both makes some tasks easier to do at scale. That's all, as seen from an economic perspective. In all other respects they are almost incommensurate.
Why is it that people think that the mere notion of "AI" means that you can just argue nonsense and forget all previous knowledge? I don't get it.
I think economist.com needs to take an Economy 101 lesson.
