Very much agreed, and keep in mind how much we might Jevons Paradox ourselves if we just try to count heads. I use LLMs a lot for basic background research at work, and the way I describe it to coworkers is "Imagine you have an unpaid intern, at 9am on their first day, who has no experience in your field, but is very widely read, and can give you very fast top-of-mind thoughts." This kind of research probably, on its own, improves my productivity 10-20% by freeing up time that I can spend on other things. I'm in a field where human time is expensive and reducing human time probably enables more clients to afford buying our product by more than enough to offset labor savings from rising productivity.
Research Status: Written & researched quickly. I think the key point is fairly simple and obvious. I relied on Claude to help with rewriting.
There's been a growing interest in predicting when various products or jobs will be "fully automated." Will we soon have popular movies, books, or even CEOs that are entirely AI-generated?
Some very quickly-found links:
- https://manifold.markets/ChaseStevens/will-an-aigenerated-paper-be-accept
- https://manifold.markets/GabeGarboden/will-aigenerated-art-win-a-major-tr
- https://manifold.markets/probajoelistic/-by-2026-will-any-fully-aigenerated
- https://www.forbes.com/sites/sherzododilov/2024/01/11/can-ai-become-your-next-ceo/
It's an intriguing question, but I believe its definition is slippier than some realize. Here's why.
First, the idea of "full automation" on complex tasks (movies, books, CEO duties) is somewhat of a false dichotomy. In practice, automation exists on a spectrum, with diminishing returns at the extreme. Consider self-driving cars. Even the most advanced autonomous vehicles still have remote human monitors who can intervene if necessary. This human involvement might be significant at first (one intervention for every few miles, in the cases of Tesla and Cruise) but will likely diminish over time as the technology improves. However, even if there's just 1 person left overseeing all vehicles, we technically wouldn't reach "full automation."
This leads to what I call the Hilda[1] Scenario. If we ask, "When will movies be 100% automated?" we're effectively asking, "When will Hilda, the very last human involved in the moviemaking process, be let go?" Perhaps Hilda has a unique talent for crafting prompts that yield remarkable AI-generated special effects, or a keen eye for making subtle but impactful script adjustments. If the cost of retaining Hilda is minimal, her involvement could persist even in an otherwise automated workflow. As long as Hilda remain cost-effective to have in the loop somewhere, it's not fully automated.
The question of a "fully automated CEO" also highlights the limitations of this framing. Even if the vast majority of a CEO's responsibilities could be automated, there might still be significant value in having a human in the role. It's one question to ask, "When are ~99.99% of current CEO duties able to be automated?" It's an entirely different question to ask, "When will companies officially deem it best to not technically have a human at the helm?" In this case, it might well be the case that a human will be needed in the role for legal reasons, even if they functionally have few duties.
Separately, there's the complexity that sometimes automation makes humans more valuable over time.
Consider fields like visual effects. Despite the rapid advancement of automation and innovation in VFX, the industry hasn't seen a drastic reduction in human workforce. Instead, the rising quality standards and the increasing demand for VFX shots have led to a growth in VFX artist employment.
This phenomenon mirrors the Jevons paradox in economics: as efficiency increases, consumption of a resource may rise rather than fall. In the context of automation, as certain tasks become more efficient, the demand for those tasks may grow, ultimately leading to an increase in human labor rather than a decrease.
Rather than fixating on the notion of "full automation," I think we should focus more on other, more precise benchmarks. Coming up with these is difficult, but here are suggestions.
For different industries (like visual effects, software engineering, management, etc):
More work thinking along these lines seems useful.
Hilda is an example person here. My guess is that the absolute last person in this chain won't be named Hilda, but it's a possibility. The name Hilda was one of the first to come from a random name generator.