In the last 10 articles in this series, I have covered a bunch of topics at the intersection of AI (or more broadly digital technology) and economics. We’ve seen how a ‘job’ is but part of a wider economic equation, a key aspect of which is production (or productivity), which I’ve explored in some detail. I’ve also chronicled the impact of both software and hardware, and other related forces, in this wider context. Let’s now move on to peering into the future, where I’ll explore how, given this foundation, things might pan out.
In this article, I want to introduce and elaborate on the concept of abundance. Of course, we all know what that word generally means, but within the framework of AI and economics, there’s a lot more to be said on the idea of abundance than meets the eye.
The social science of ‘economics’ was born out of the need to study and improve upon (scarce) resource allocation. The birth of economics as a serious discipline is traced to Scottish philosopher Adam Smith and his landmark opus “An Inquiry into the Nature and Causes of the Wealth of Nations” (1776), with the definition further delimited later by another British philosopher and economist, John Stuart Mill.
A key aspect here is that word ‘scarce’ - because if a resource were available aplenty, would you bother? Have you heard of anyone discussing the apportioning of air for breathing?
Economics primarily deals with scarce resource allocation
This need to overcome the problems imposed by scarcity has colored the growth of the field and much of the social and political ramifications that derive from economic observations and theory. And this is where AI comes in and shakes things up big time!
As we’ve covered in previous articles AI (including robotics) can significantly alter, what I’ve called, the Economic Equation: Input + Processing = Output. In summary, by improving the processing component significantly, and with digitization and dematerialization, decreasing the amount of input needed. Let’s see how this may play out in different areas of the economy.
In relation to an economy, I will make an emphatic distinction between essential and non-essential resources, with of course the main focus here being on the essential ones:
Basic needs (food, shelter, healthcare)
Education and information
Basic transportation and communication
Essential services (financial, legal, administrative…)
We can at least for now park the non-essential ones, such as luxury goods and experiences - that yacht, that Ming vase, that Madonna original by Raphael!
Another potentially game-changing factor is that we are at the cusp of an AI-accelerated energy revolution. Despite the jokes about it always being 30 years away, we are starting to make rapid strides in research in areas such as Fusion (mimicking how our Sun creates seemingly infinite energy), energy-storage solutions and related technologies. It’s not hard to imagine how concrete developments in such technologies are going to significantly improve the Economic Equation. I’ll come back to this later.
So let’s start with a whistle-stop tour at how all this could affect the different areas of economic essentials, some of which we’ve already touched up on in previous articles, bringing the production costs to near-zero. (‘AI’ here may include robotics, and in some cases, more broadly, traditional Machine Learning (ML)).
Food and Agriculture: AI/robotics-driven farming coupled with AI/ML-driven optimization, along with other promising advances such as lab-grown proteins and vertical & indoor farming.
Healthcare and Medicine: AI diagnostics and treatment coupled with LLM-empowered consulting and counseling, personalized medicine based on ML simulations of an individual's physiological state, drug discovery enhanced again by ML simulation-based testing.
Education and Knowledge: AI tutors fully personalized to each student’s specific needs, universal access via digital mediums, and cognitive enhancements afforded by working with AI integrated to the workflow or even, via brain-machine interfaces (BMI) directly with individuals.
Other essential services such as those mentioned, being primarily within the cognitive domain (knowledge work) are already undergoing this LLM-driven transformation and can be expected to mature in the next few years.
Finally, I’ll return to transport and housing (property) in future articles as they require some more context, but for now I’d say even if it may be less obvious, similar strides towards abundance are possible in these two areas as well.
At this point, I’ll add the caveat that while all of this might appear rather rosy and easy, there remain significant challenges that need to be overcome before we get there.
This is indeed going to be a radical transformation similar to or bigger than the first Industrial Revolution that started in Britain more than 2 centuries ago. Similar to how life after the Industrial Revolution gathered steam (pun intended!) would be unrecognizable to the medieval king and peasant alike, there are limits to how much we can project to this future from our current understanding and knowledge.
But that doesn’t mean we shouldn’t try, and in that spirit I will continue to explore other factors in question, and importantly the transition: how we get from here to there. Stay tuned.
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