Argument against 20% GDP growth from AI within 10 years [Linkpost]

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Mohammed Bavarian, a research scientist at OpenAI, tweeted this thread arguing that he could see “the overall US GDP growth rising from recent avg 2-3% to 20+% in 10 years.” Feel free to check out those arguments, though they’ll probably be familiar to you: GPT-3, GitHub Copilot, and image synthesis will drive unprecedented improvements.

Cameron Fen, an economics PhD student at the University of Michigan, responded with this thread disagreeing with Bavarian’s argument. I wanted to share some of the arguments that I found novel and interesting.

Argument #1: The impacts of previous transformative technologies

There have been 3 industrial revolutions in history, mechanization, electricity and mass production, and IT and the internet. China went through all 3 at the same time and was barely able to go above 7% growth annually.

The newest industrialization will be big, but will it be bigger than moving 95% of the population working in agriculture to working factories, not to mention all 3 combined?

In particular, the growth of the first industrial revolution accelerated growth from 1.5% to 3% a year in the UK (Source). Growth from electrification was 1.5% a year on average (source) and growth during the information industrialization was 3.5% a year (source).

Given these growth rates, it seems unlikely that a single industrial revolution can move the needle to such an extent that US GDP growth accelerates from 2% to 7%.

Argument #2: The size of the tech industry

Perhaps you can argue that the industrial revolution on the horizon is going to be 3x bigger than any industrial revolution in the past. Let’s see what that would imply:

7% growth implies 5% growth over our current trend rate of about 2%. This comes out to be about 1.15 trillion GDP additional growth this year and increasing as the base gets larger.

According to this article, Facebook contributes 100 billion to US economic activity. While much of this is uncounted because GDP methodology is imperfect, I’ll go with that number.

To get to the 1.15 trillion additional growth you need 11.5 Facebook created every year. This comes out to a market cap of 5.5 trillion dollars created every year in tech to get something like a 5% growth. The market cap of the entire tech sector is 13.5 trillion dollars.

Call adjacent markets another 6.5 trillion dollars. Thus a 20 trillion dollar market cap sector needs to add 6 trillion (5.5 + .02*20) dollars a year. Has the tech sector ever grown so fast that it is creating the equivalent of 12.5 Facebooks from nothing every year? No.

Let’s think about this another way, assume the US is growing at 2% a year. The tech sector is 10% of the US economy. If the US were to grow 7% a year on all tech sector growth, the tech sector would have to grow at 52% a year.

Even if you assume adjacent sectors (20% share) growing at 12% a year, you need 32% growth from the tech sector to just get 7% total GDP growth. This seems infeasible to me.

Argument #3: Estimating the market impact of LLMs, image synthesis, and AlphaFold

Just to get a sense of how massive innovation has to be to move the needle, I’m going to discuss three central innovations. Let me know if I missed something—more will be invented—but I plan on showing that these game changing techs will not drastically improve growth.

The triumvirate of GDP impactful techs are 1) GPT-3 and other LLMs, 2) Self-driving cars and other robotic control, 3) and Alpha Fold. I don’t include text-to-image models like DALLE and other diffusion models because idk any commercial applications that move the needle.

Market impact of natural language generation: 0.5% of GDP per year because there are free alternatives. (My response: Wouldn’t the free alternatives boost production for users?)

LLM: GPT-3 is good at a lot of NLP tasks, but versus specialists with fewer parameters it often performed worse. For example, BERT outperformed GPT-3 for word embeddings. Something like PaLM maybe outperforms BERT, but the improvement is incremental.

Is there a space for people who are afraid to go to Hugging Face to download BERT and press run on their computer? Yes. If you want Open AI to hold your hand for a fee, there is a place for that, but outside of NLG GPT-3 isn’t really a category killer

NLG: Maybe other applications add additional impact from GPT-3, using the same logic maybe another 5% growth a year from other adjacent sectors (10% of the economy). You have robot therapists, robot financial planners, and robot CRMs. Again, not everyone will choose these options as some will prefer the human touch, you also have the same cost disease problem, and people losing their jobs.

Most interesting argument: Code generation only raises tech sector growth by 3.5% /​ year because of the Baumol effect.

Codex: First I will start out with Codex, which to me seems like the category killer app of the category killer that is NLG. Assume that coders spend 23 of their time coding and 13 of their time doing other stuff. This is optimistic.

Let’s say Codex makes all coding instantaneous. Coders would only accomplish 3x more work. Why? This is an example of Baumol’s Cost disease. Certain things don’t get more productive and as the productive things get more productive, the unproductive things take up more of your time. Coders would now spend 100% of their time on not coding, but that takes 33% of their time before coding.

Let’s say the tech sector is 33% coders, again Baumol’s cost disease hurts us as that would imply that 66% more work is being done in the tech sector due to Codex. This is widely optimistic.

The total number of developers inside and outside tech in the US is 4.1 million people. This includes anyone who codes, not just engineers. Tech employs 12 million people.

If you assume it takes 15 years for Codex to diffuse (the lightbulb was invented in 1879 and it took until 1925 for half the homes in the US to have electricity (source), that comes out to about 3.5% growth a year for the tech sector, from Codex, and by fiat assumption 3.5% growth for coders in the 10% of the economy outside tech that has software engineers.

At the same time, as Codex is improving efficiency, coders are losing their jobs. So there is a net increase in GDP there is also a drag on growth. I don’t know how much this is but someone losing their 100K-200K job will spend a lot less on the economy. I ignore this.

Self-driving cars isn’t a huge industry

Self-Driving Cars: The problem with self-driving cars is that the industry is too small. Automobiles is a 100-billion-dollar industry (source). Since the US economy is 23 trillion dollars in size even if you have a 5x growth in the industry that’s still only a one-time growth of 1.7%. Businesses like Covariant and Boston Dynamics are niche businesses in a niche industry and do not move the needle.

Pharmaceuticals aren’t a huge industry either

Alpha Fold: The story for Alpha Fold is the same as for self-driving cars. The pharmaceutical industry is 625 billion and is 2.7% of US GDP. (source) Even increasing the pharmaceutical industry by 5x would lead to an additional 11% one-time bump in GDP. Smoothed over fifteen years comes out to be less than a 1% increase in growth.

Conclusion: These hypothetical boosts only add up to 2% additional GDP growth from AI

Adding all these things together, 3.5% increase in tech sector (10% of economy) = .35%, 5% increase in adjacent sector = .5%, 3.5% increase in engineers outside of software (10%) = .35%, Self-driving cars 1.7%/​ 15 years = .1%, pharma growth is .66%.

Putting all this together you have 2% additional GDP growth from deep tech and related advances, 2% from current growth which leads to 4% GDP growth in this widely optimistic scenario. You still need 3% growth from another tech that I haven’t named or hasn’t been invented yet, but I hope my exercise has illustrated how much inertia must be overcome GDP-wise to get even 3% more GDP growth.

This will be even harder by 2030 when the economy will have increased from 23 trillion to around 30 trillion in size. The amount needed to grow 7% at 30 trillion would be more than 25% higher than what is needed at 23 trillion.