The dead economy theory

India has the problem with farming that the US is starting to have with AI. Farming in India is still far too labor intensive by world standards. 43% of workers still work in agriculture. [1] For the US, that number is under 2%. China is at 22% as of 2023, and dropping steadily.

This inefficient agricultural system is not by accident. It is supported by heavy subsidies. Attempts to cut the subsidies resulted in riots.[2] Trouble is ongoing. Comments from someone who knows more about this than I do would help here.

The US and most of the EU went through that transition over several generations, and farming is still heavily subsidized in both areas. The transition happened faster in China, and a hukou system was put into place to prevent people from migrating from farms to cities faster than the cities could absorb them.

Looking at how countries coped with a fast transition from labor intensive agriculture to an urban society gives hints on how an AI transition may look. All the Asian countries that went from poor to rich in a generation did this, with different approaches. How that took place may provide more useful info than philosophy.

[1] https://economictimes.indiatimes.com/news/economy/indicators...

[2] https://en.wikipedia.org/wiki/2024%E2%80%942025_Indian_farme...

I know this isn't exactly related, so maybe a low value comment, but it itches in my mind. Years ago I talked with a recruiter at Facebook and they bragged about how many floors of developers they had working on Messenger in just one location (Seattle).

What on earth do you do with that many devs on a project like Messenger? I mean, really?

I feel like in a way, AI just adds to that weird situation of overcapacity. Maybe we were already oversupplied with talent. In which case why the heck were we still hiring more, more, more developers? Before the AI craze, Musk chopped an awful lot of headcount at Twitter, right, and proved it was overkill, has that panned out?

I just struggle to imagine how the economics of SWE really work in reality, outside of the niche that I am in. I have never worked for a pure software company on products that ship directly to outside customers, I've always been an internal developer. Maybe that is why I have such a big blindspot.

I won't be surprised if the net result of this wave of LLMs is ... not much. A change in tooling, but otherwise not revolutionary. On paper it should be revolutionary, but the more I use it (for both coding and non-coding tasks) the more I think it isn't anywhere near magic enough for that. It does have its moments though.

I think when these companies IPO later this year, we’re going to see the reality of the PNL numbers and whether they are sustainable etc as all the financials will become public.

Rumor mill suggests that Anthropic might be profitable (but at what magnitude), OpenAI is not profitable, Google is mostly vertically integrated and has a low cost structure as they are have pre-existing data center buildouts, their own silicon and experience that suggests they will be able to operate at a very low cost, but they still have to justify their spend.

I think having to report numbers publically on a quarterly basis will bring the whole thing into reality.

This article describes a vicious cycle:

Layoff of workers -> Workers stop spending -> businesses suffer

This is not a foregone conclusion. Laid off workers could find other jobs, with higher incomes, due to productivity increases from AI.

This narrative falls into the trap of zero sum thinking, taken at the limit, you can advocate for jobs programs and helicopter money where people get paid to do nothing to keep the economy humming.

>The US horse population grew from nine million in 1840 to twenty-one million by 1900, seemingly immune to technological change. Within sixty years of the internal combustion engine, the population collapsed by eighty-eight percent.

I think this is a very interesting and chilling point, especially if you draw the parallel literally. For quite some time, I was pondering the question:"Who is buying though?". I.e if you automate workers out of labor, who are we selling these AI services to?

I guess if global population drops by 80-90℅ you suddenly get a "sustainable" economy, as everything is repriced the economy of scale needs a much smaller scale.

(Not speculating this is a plan, just a thought that occurred to me when reading about horses example)

Why is this time different?

Won't super power AI tools allow companies to do more with the same number of people? Don't you think a smarter way to run a business is to capture more of the market if you have the resources to do so?

If company A decides they just want the same slice of the market they have now and can fire half of their employees and pocket $$$, can't company B hire the same workers and compete harder with these new extra productive workers they hired? Won't the company B tend to capture more of the market and thus survive longer?

In nature we say there are no unfilled niches, meaning that if there were space for something to come compete for resources, it would quickly be 'solved' by the motivating factors involved. Not a precise thing, but a good heuristic.

US knowledge-worker compensation is around $10T / year. Anthropic and OpenAI have raised (not spent yet, just raised) $317B. That's ~3% of knowledge worker spending in one year alone. What business wouldn't pay 3, 5 or 10% more a year to make their worker productivity increase by larger factors?

I have led AI integration in a university faculty. From this experience I can conclude that good work is only produced when humans are in the loop. It's not a technical barrier, but a categorical one. "Good" work is defined by humans and our judgment is irrational but rooted in our evolutionary survival needs. In other words, AI don't have human motivation by definition. Without human in the loop, the top most motivation is never fully aligned with us, today, as humans. This removes the premise at the basis of this post.

> The people writing the checks are not in the habit of lighting trillions of dollars on fire for a better autocomplete and an endless proliferation of longer and longer memos that nobody reads.

Aren’t they though? What about that whole crypto thing.