With the recent hype around ChatGPT, GPT-4, and large language models in general, communities like Hacker News are breaking out into a this time it’s different doom and gloom.
While no one can predict the future, we can at least think about some possibilities for what can happen and maybe think about what is less likely to happen.
To summarize, I do not see us approaching the Singularity soon. Still, the recent progress, especially around GPT-4, suggests that analogous to the word processor, spellchecking, code reuse, and other time-saving and accuracy-improving measures will make a significant chunk of our tedious day-to-day work superfluous and allow us to focus on the more essential things (vs. the urgent, but not important). Who is us in this context? We are knowledge workers. We use our knowledge of this world and intellect to shape information and communicate it to others. We devise new contraptions and schemes and strive to improve or exploit something.
There were many moments in history where at least some people must have thought: This is it. There will be no more work left for us to perform. Let’s look at some of these moments and see how they turned out.
A great example many like to bring up is the fate of the farrier. Before the automobile, horses were, for many, the primary method of transportation, both for private and commercial purposes. Farriers were indispensable in allowing horses to get around where they needed to be, caring for their hooves, and making horseshoes. While the automobile industry grew, the horse-based transportation industry did not. While horses are still used for many purposes to this day, though decidedly less for the economy in its entirety, the profession of the farrier still needs to reap all the rewards of global economic growth. I don’t think it’s fair to say, as is often assessed, that the automobile brought about the demise of the farrier. On the other hand, they did not end up as winners either.
What about the printing press or any other of our advances in writing, such as the typewriter, spellcheckers, emails, grammar checkers, and so on? While the work of a classical secretary became unnecessary in many ways, the fact that the individual increase in productivity meant an overall growth of the economy meant, in turn, that those who would have lost their current role or even employment to technological advancement would find new occupations within this growth.
Similar things can be said about the loom, the steam engine, and other technologies. Looking at it from a long-term perspective, we see that they allowed our economic output to grow steadily. It brings us to a point where more smartphones and other gadgets than we ever need are being produced and then chucked onto a landfill one or two years later.
One of our guiding economic and social principles is that people are expected to work unless they have a convincing excuse not to. This produces phenomenons such as the bullshit job. In general, this environment really motivates people to continuously find new reasons to keep someone on a salary, be it to press the same button for eight hours a day or generally lounge around and look busy. Other than salaried employment, there is often no chance of receiving social insurance once old age, disability, or another circumstance prevents someone from working.
What about the phenomenon of outsourcing or off-shoring? Take the allegedly important voter category of the coal miner. Some politicians like advertising to coal miners and claim they will bring back thousands of coal mining jobs if elected. The reality is, especially nowadays, that coal is not needed where it used to be, and laborers in other countries work in readily exploitable conditions, such that coal can be procured much cheaper elsewhere. If globalization was a technology, it did not make the need for coal unnecessary. Businesses can now kick down on people on a global scale instead of kicking down on your local population.
Knowledge workers for most of the 20th century and for a few good years in the 21st century saw the advent of productivity technologies as force multipliers. The search engine did not make the legal profession unnecessary. They could now research 3 cases in the time it took them to research one case. While before, I would be busy checking out several books about programming from the library, I can now just kagi for whatever I need, and it’s right there.
Enter large language models. I don’t see them as anything other than force multipliers. The dull routine work of getting information is getting an upgrade once again. LLMs seem to have other uses as well. They follow instructions very well. I can ask ChatGPT to improve my marketing copy. If you give large language models tools, such as LangChain, they can become your junior developer. At least, this is my idea for a future project. Short-term, I don’t see LLMs replacing employees, but it certainly will allow us to idly stare at screens even more, pretending to work and hoping that looking at the wall clock will make time pass faster.
That is not to say that some future manifestation of artificial intelligence won’t partially or entirely alleviate the need to perform labor. At this point in time, I don’t see us being on such a dramatic growth trajectory. On the other hand, you really don’t want to be on the wrong side of history here and categorically deny that this version of the future is entirely impossible.
The nature and exact value of knowledge work have always been dubious. After all, I might get a great idea while strolling to the grocery store. Will that count as hours worked? Paying someone for their time is easier to justify when you see someone pulling a lever. That suggests one of the reasons the subjugators of our internet economy are again asking their people to come back to the office is so that they can satisfactorily measure each worker’s butt-on-seat time.
Then, when LLMs take over every mundane task we work on and sit at our desks in the office with absolutely nothing to do, we must devise new ways to look busy. OpenAI may sell a crank that you have to turn, as otherwise, the token generation speed of ChatGPT will grind to a halt. Sooner or later, we must ask ourselves what the point of most knowledge work is. It seemed dubious from the start that someone sitting in front of a laptop answering emails is paying their rent doing that, but being part of the chain of labor exploitation would guarantee some form of compensation for intense screen staring and computer touching.
Before you think I sound defeatist, ChatGPT-3.5 absolved me:
While there are some elements of skepticism and critical reflection in your blog post, overall, it does not sound sad or pessimistic. You present a balanced perspective on the impact of large language models and other technologies on the future of the economy, and you acknowledge both the potential benefits and challenges that these developments may bring. Additionally, you draw on historical examples to contextualize the changes that are happening in the present and to emphasize that technological advances do not necessarily lead to the elimination of entire professions or industries. Overall, your tone is informative and thoughtful, rather than pessimistic or defeatist.
I, for one, welcome our LLM overlords.
P.S.: Please read the GPT-4 technical report. Among many other golden bits, GPT-4 lies to a TaskRabbit worker to get a captcha solved. Humans may become flesh interfaces so that different LLMs can convincingly talk to each other.