Picture courtesy of DALL-E
The Lump of Labor fallacy says that there is only a finite amount of work to do and that any attempt to automate work will put people out of work. The reality is more nuanced. Yes, the invention of motorized vehicles did put the horse and buggy business nearly out of business, but it also gave rise to an increase in steel production, oil production, gas stations, motels (invented from Motor Hotel), Walmart and a good part of the 20th Century economy.
If society discovers a process that makes an existing activity faster, easier and cheaper, it frees up money to buy new products and ends up being a net boon to employment. The new employment goes to new people and the craftsmen making horse drawn carts may not have the skills to be employed in the automobile industry.
No one in 1800 could have predicted that a million Americans would be employed by railroads by 1900. No one in 1875 (before invention of telephone) could have predicted that a hundred years later millions would be employed in Call Centers.
Another thing happens to technologies that reduce costs: Jevons paradox.
In 1865, the English economist William Stanley Jevons observed that technological improvements that increased the efficiency of coal use led to the increased consumption of coal in a wide range of industries. He argued that, contrary to common intuition, technological progress could not be relied upon to reduce fuel consumption.
In 1865, the date Jevons introduced the paradox, Britain was the world leading coal producer. Steam engines, which ran on coal at that time, he argued would increase in use as the efficiencies of the steam engine reduced the cost of operation of individual ships. The lower the cost to operate, the greater the demand for steam ships. Demand for coal increased as efficiencies improved.
The problem this time around is that AI efficiencies are reducing jobs among the most educated people in the population. Horse-drawn coach craftsmen didn't have an entrée to newspapers to lament their jobs going away. Tech bloggers are filling the airwaves about the evils of AI. They have a voice and are using it. No longer is it the poor, semi-skilled laborers who are feeling the brunt of layoffs due to technological innovation.
The most inefficient industries in the economy are filled with college educated people: Education, healthcare, banking, insurance and science. These are people well-equipped to fight back. That's why all the "go slow" rhetoric in the press. These people are used to being quoted in the press and are taking advantage of their elevated position.
Automation started automating legs, then arms, then fingers and now brains. If all jobs can be automated through AI, then what is left for people, the Luddites ask? Just ask the lawyer who submitted arguments to the court with fake case law. People will still need to be in charge.
The six inventors of transformers (the T in ChatGPT stands for Transformers) published a paper in September 2022, kicking off the Large Language Models (LLMs) gold rush. The six researchers were at Google, including an employee who joined in 2000. They have all left to form their own companies.
Google just announced RT-2, their second generation robot.
Google says that RT-2 can allow a robot to recognize and throw away trash without having been specifically trained to do so. It uses its understanding of what trash is and how it is usually disposed to guide its actions. RT-2 even sees discarded food packaging or banana peels as trash, despite the potential ambiguity.
If history has a lesson for us it's that when RT-3 or 4 gets close to developing a useful product, the scientists who developed RT-2 will leave to form companies of their own.
Arizona State University began an experiment in 2021. They began handing out chatbots to students. The chatbots acted like tutors: They wouldn't give out answers, they would ask questions designed to stimulate the student to make the connection. Senior lecturers got training from:
Dreamscape Immersive, a virtual reality company co-founded by an individual responsible for blockbuster movies such as WarGames and Men in Black, partnered with the university to create Dreamscape Learn.
The model has flaws. It isn't the solution to the education mess, but it is an interesting experiment. Grades went up considerably.
The acknowledged inefficient industries are going to be experimented on, but they will take time. The initial successful companies will do mundane things, but with AI, not people -- more of the RT-2 kind of solution.
Be alert, there will be opportunities there, but the huge gains will come from solutions that aren't obvious. They will probably come from refugees from Apple, Microsoft, Google, Meta, etc. but they will be new companies. No matter how hard they try, employees with an idea are going to set out on their own.
Sorry for being a day late. Spent most of Tuesday at doctors.
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