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Musings Report 2023-21 5-20-23 Will AI Deliver the Worst of Both Worlds?
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Will AI Deliver the Worst of Both Worlds?
Setting aside sensationalist dystopian fears of AI taking over our Spaceship ("I'm sorry, Dave. I'm afraid I can't do that," the famous line from the film 2001: A Space Odyssey), let's focus on two more realistic possibilities suggested to me by my friend A.T. recently:
1. Per my blog post What If AI Is Only a Cost and Not a Profit Bonanza?, AI is already a commodity and therefore there is no way to establish enduring profits as competitors have the same tools, much less grow high-profit trillion-dollar monopolies such as Google, Apple or Microsoft based on commoditized AI.
2. AI eliminates jobs which are not replaced by a massive wave of new jobs, a process known as technological job displacement (the elimination of jobs when human workers are replaced by technology).
As A.T. explained, the economist John Maynard Keynes discussed the potential of technological job displacement to disrupt the economy and society, as the gains (higher corporate profits and productivity) would be outweighed by the loss of broad-based, stable employment.
I am skeptical of the claims that tens of millions of jobs will be lost due to LLM AI (large language model) or machine-learning AI. I discussed the limits of AI and technology in my book Will You Be Richer or Poorer?: Profit, Power and A.I. in a Traumatized World.
It seems more likely that these AI tools will boost the productivity of skilled human workers rather than entirely replace skilled human workers.
But for the sake of debate, let's assume the projections of 30 million jobs will be lost will prove accurate.
Optimists point to the remarkable success of technology in creating new jobs as fast as old ones are lost. Thus factory jobs were replaced by new service-sector jobs. This chart illustrates one specific technological advance--spreadsheets such as Microsoft Excel (Multiplan on the original Mac, in a bit of tech nostalgia)--which many feared would wipe out bookkeeping jobs.
These tools did drastically reduce bookkeeping employment but they created many more jobs in auditing, accounting and financial analysis.
This history suggests to many that AI will create millions of new jobs within the AI industry.
The evidence is actually not quite so clear that this new job creation is predictable. The number of jobs in high tech has been remarkably stable for many years. The advance of new technologies hasn't doubled or tripled the number of jobs in high tech.
If Elon Musk's recent drastic reduction in the number of Twitter employees is any indication, it seems many of the jobs within Big Tech--especially the non-engineering sectors-- are superfluous, even without AI.
The Disappearing White-Collar Job: A once-in-a-generation convergence of technology and pressure to operate more efficiently has corporations saying many lost jobs may never return. (WSJ.com)
In other words, we may find that AI delivers the worst of both worlds: it slashes profits as everyone loads up on the higher costs of AI but without any enduring competitive advantage that would support higher prices and profits, and it displaces wide swaths of human labor that are not replaced with new sectors generating tens of millions of new jobs.
The whole point of AI, after all, it that it learns on its own and fixes its own coding. In other words, the whole point is the removal of human labor from the process of improvement and debugging.
Yes, there will always be human oversight and input-- for example,
"We Are Grunt Workers": The Lowly Humans Helping Run ChatGPT Make Just $15 Per Hour--but there is no guarantee the scale of such employment will magically equal the jobs lost.
There is a feedback loop to job losses that aren't replaced, something Keynes recognized: when people lose their earned income and depend on unemployment or possibly Universal Basic Income (UBI), their income is typically lower and they're no longer able to spend and consume as much as when they had a job. The entire economy shrinks.
In the idealized world of UBI proponents, corporations will always be immensely profitable and so we can pay for UBI by taxing corporations and the wealthy who receive the dividends, stock options and capital gains generated by immensely profitable corporations.
So what happens if AI destroys both profits (due to it being "free" and a freely distributed commodity) and jobs, especially the kind of higher paying jobs that have been impervious to the technological displacements that have ravaged working-class employment in factories and low-skill work?
Ironically, it's the high-skill manual labor jobs that are least likely to be replaced by AI. Even so-called low-skill labor isn't as easy to replace as many assume. As many have noted, "ChatGPT can't make a hotel bed."
While videos of robots that can leap and dance abound, can the robot clean a hotel room for a total lifecycle cost that's less than a human maid? Recall that robots are costly, need to be maintained and recharged, etc. By the time a robot is developed that can do all the tasks of cleaning a messy hotel room, lift the heavy bed, place new plastic trash bags in the bins, etc., will it really be cheaper than human labor? It's an open question because the tasks require a great many different levels of strength and dexterity.
The core assumptions at work in the optimistic view are: AI will deliver untold profits via the reduction of human labor and millions of new jobs will be created by AI. Both assumptions are essentially based on the magic of history repeating itself rather than on the fundamentals of AI, the source of profits and the creation of jobs, which first and foremost must generate a profit for the employer.
These questions should elicit discussions about fundamental changes we could make in how labor and capital (i.e. technology) are treated. For example, what if the 15.3% tax on labor for Social Security and Medicare (the combined employer and employee taxes) and the income tax on earned income from labor (up to 37%) for all but super-high earners were eliminated and those taxes were instead loaded onto capital and technology?
In other words, what if capital carried a 50% tax rate and labor was untaxed below $200,000 per individual? How would that change the costs and benefits of labor and capital?
We as a society may be forced to entertain these tradeoffs in ways few seem to anticipate.
Highlights of the Blog
2023: Echoes of 1973 5/19/23
How Many Dominoes Must Topple Before One Falls on Us 5/17/23
What If AI Is Only a Cost and Not a Profit Bonanza? 5/15/23
Best Thing That Happened To Me This Week
Lasagna!
From Left Field
NOTE TO NEW READERS: This list is not comprised of articles I agree with or that I judge to be correct or of the highest quality. It is representative of the content I find interesting as reflections of the current zeitgeist. The list is intended to be perused with an open, critical, occasionally amused mind.
Many links are behind paywalls. Most paywalled sites allow a few free articles per month if you register. It's the New Normal.
The Poverty Paradox: why is there still so much economic hardship in the US?
Thailand elections: young people want radical change in a country they say is stagnating.
Why would we employ people?’ Experts on five ways AI will change work.
Henry Grabar's 'Paved Paradise' explains why parking is both a local nuisance and a global blight.
Varna Man and the Wealthiest Grave of the 5th Millennium BC
COVID and the Heart: It Spares No One.
The Disappearing White-Collar Job: A once-in-a-generation convergence of technology and pressure to operate more efficiently has corporations saying many lost jobs may never return
The Return of Paganism: The spiritual crisis afflicting contemporary America has ancient and enduring roots—and so does the cure (via Rick A.)
Bioregionalism: A Model for a Self-Sufficient and Democratic Economy.
Diabetes Rates by Country
Deal of the Month: $22M Office Teardown Makes Way for Multifamily-- often cheaper to tear down office buildings and build new housing than rebuild office buildings into residential...a simple fact of the differences in infrastructure and construction....
Let’s be clear about what Geoffrey Hinton is saying about deep learning
The map illustrates the most prevalent names given to baby girls in the United States between the years 1950 and 2018. (twitter)
"The purpose of art is to lay bare the questions that have been hidden by the answers." James Baldwin
Thanks for reading--
charles |
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