The Coping Mechanisms of the Precariat: Prelude To The Great Resignation

This post is a continuation of my series of posts on economic precarity and the precariat.  In the last post in this series, I introduced the concept of a social nonmovement.  To quickly review, a social nonmovement is the spontaneous, unplanned emergence of a set of social practices among a large number of people, among whom these practices begin to encroach upon and ultimately disrupt an existing status quo.  The concept of the social nonmovement is introduced and explored in Asef Bayat's book Life As Politics.  What is especially relevant to the precariat is the emergence of social nonmovements among the poor and powerless in response to the pressure inflicted on these people by the rich and powerful masters of an existing status quo.  These social nonmovements encroach upon and weaken the power of the masters of the existing status quo, yet they frequently operate outside the notice of these masters even as they weaken the power of these masters.  However, sometimes a social nonmovement catches the eye of a large number of the privileged members of a society - especially when the social nonmovement appears suddenly, spreads quickly, and achieves a massive amount of disruption in a short amount of time.

Such a social nonmovement is the Great Resignation - a time in which massive numbers of people decided that their jobs were such a royal pain that they refused to take anymore, and quit.  Most scholars and journalists consider the Great Resignation to be one of the outcomes of the COVID-19 pandemic which shut down much of the American economy in 2020 due to the failure of then-President Donald Trump and his Republican Party to effectively prepare for the pandemic.  These scholars and journalists consider 2021 and 2022 to be the peak years of the Great Resignation, and some of these even say that the Great Resignation is now largely over.  However, there are minority voices such as journalists at the Harvard Business Review who say that the Great Resignation is actually a long-term trend which began at the beginning of the last decade and is still continuing.

Most people who have been alive for any length of time realize that throughout history, worker attitudes have fluctuated between job satisfaction or dissatisfaction in cycles that are reminiscent of the alternation of yin and yang in ancient Chinese philosophy.  In today's post I hypothesize that the 1960's in the United States were a time of increasing job satisfaction for an expanding number of people.  However, in making such a hypothesis, I am confronted by the difficulties which social scientists have had in defining what exactly is job satisfaction, let alone in figuring out how to measure it.  (See, for instance, "What is Job Satisfaction?", Edwin A. Locke, Organizational Behavior and Human Performance, 1969.)  Nevertheless, a 1982 report from the U.S. Bureau of Labor Statistics supports my hypothesis, noting that in 1973, 87 percent of workers were either very satisfied or moderately satisfied with their jobs.

Yet that picture has obviously changed over the years.  In 2017, an organization called the Conference Board provided a chart outlining the historical measurement of U.S. worker job satisfaction from 1987 to 2016.  According to that chart, worker satisfaction was at or below 50 percent during five of the eight years of the presidency of Republican George W. Bush.  According to the 2022 "Job Satisfaction Chartbook" from the same source, job satisfaction "is the highest it has been in a decade" at 60 percent.  Yet according to the Achievers Workforce Institute, two-thirds of employees are thinking about leaving their jobs in 2024.  This was also true in 2022, according to the Institute. This is yet more evidence that the Great Resignation is an ongoing trend.  (Maybe the people who answered the Conference Board surveys in 2022 weren't fully sharing their feelings...)

Now declining job satisfaction can be tolerated by workers for a time, yet as it intensifies, it leads to a point in which people decide that the pain of staying in an existing intolerable situation exceeds any potential suffering involved in making a change to that situation.  And workers have from time to time reacted explosively to their workplaces as illustrated by songs like "Oney" (written by Gary Chesnut and sung by Johnny Cash) and "Take This Job And..." (written by David Allan Coe and sung by Johnny Paycheck), as well as idioms such as "going postal."  (By the way, I do not condone or encourage workplace violence!)  But stories about successful quitting have been made to seem like the sort of rare events that are beyond the reach of most working stiffs.  Yet the undeniable fact is that during the last years of the last decade and the first years of this decade, a huge number of people found themselves pushed into quitting.  It is natural to ask what factors pushed so many into quitting at around the same time.

I will not definitively answer that question today.  However, I will suggest what I consider to be the likely factors.  Treat my suggestions as hypotheses, if you will.

• First, there is the erosion of the power of organized labor, an erosion which actually began with Republican President Richard Nixon's wage and price controls in the early 1970's.  This erosion kicked into high gear under the Republican presidency of Ronald Reagan and has not slowed down since.  The power of unions to protect their workers from low wages and excessive work demands was thus eroded.
• There is also the removal of the guarantee of lifetime employment for good and loyal employees of large corporations.  This was pioneered by such CEO's as Jack Welch of General Electric and was a direct contributor to the economic precarity suffered by a majority of working Americans today.
• There were the stresses imposed by globalism as wage and labor arbitrage.  This globalism was championed by right-wing, conservative executives of major corporations - the same sort of executives who are in many cases supporting the MAGA hostility to open borders championed by Donald Trump, as they see that sometimes smart people from poor countries can turn the tables on economic systems that are rigged against them.
• Consider also the removal or weakening of workplace protections against employer abuse.  Many employers (as well as business customers), thus unhindered from having to be humane toward their employees, turned some of those employees into metaphorical toilet paper, doormats, and punching bags onto whom these bosses could project their unresolved and unjustified hostility.
• Lastly (at least for today's post), there is the rise of the toxic workplace - a workplace in which bosses either perpetrate or enable bullying and mobbing behavior by popular workplace staff against those who are deemed to be scapegoats.  

Note that the last two factors are the direct result of the creation of a massive power imbalance between employers and employees over the last four decades.  The employees, reduced to a state of naked dependency on capricious bosses and a capricious labor market, were thus exposed to the prospect of either starving or having to meet unreasonable and destructive demands from these employers.  This made the management ladder a very attractive place for abusive, psychopathic, sociopathic, and otherwise personality-disordered people to take root.  Now here's an interesting perspective on the reason why leaders and managers allow abusive workplaces to continue: their continuance satisfies the ongoing psychological cravings of such managers.  A parallel to the abusive workplace is the abusive church.  As "Captain Cassidy" pointed out in a recent post on her blog Roll to Disbelieve, the whole point of creating an abusive power structure is so that the masters of such a structure (and those who are their special pets) can enjoy the psychological thrill of owning such a power structure.  And what is the best way to experience that thrill?  Why, to abuse the people at the bottom levels of such a structure, of course!  Consider Captain Cassidy's third and fourth points from the post I have cited:

• "Nothing is ever off-limits for those who hold power. More to the point, following the group’s rules is for the powerless. The powerful not only do not follow those rules, they flaunt their disobedience."

• "The powerful delight in the most potent expressions of power: forcing people to do things they don’t want to do; rubbing their own disobedience in the noses of the powerless. If power is not flexed, the powerful might as well not have it at all."

Captain Cassidy's perspective echoes what Chauncey Hare and Judith Wyatt wrote in Chapter 4 of their 1997 book Work Abuse: How To Recognize and Survive It.  But just as abusive churches (and abusive white American evangelicalism) have begun to suffer a loss of social power as their abuse has been exposed, abusive workplaces throughout the English-speaking world have begun to suffer an erosion of economic power.  Consider that workplace mistreatment cost U.S businesses between $691.7 billion and $1.7 trillion in 2021, according to a 2021 article in the Journal of Organizational Behavior.  A 2023 Forbes article puts the cost of toxic workplaces to U.S. businesses at $1.8 trillion annually.  According to a 2019 SHRM report, the cost of employee turnover in 2019 due to job dissatisfaction alone was $223 billion.  No matter what number is used, we're not talking chump change here.  What's more, toxic workplace culture has been a key characteristic of companies that either recently underwent scandals or were driven out of business, companies such as Volkswagen, Theranos (and its jailbird ex-CEO), and WeWork, to name a few.

The pinnacle of ecstasy for abusive employers seemed to come in the early months of 2020, in which powerful employers were able to bully their staff (many of whom were stuck in low-wage "service" jobs) to show up for work during the COVID-19 pandemic.  It was that pressure and the resulting threat of actual physical death which proved to be the final straw for many people who had hitherto surrendered themselves to enduring toxic workplaces.  This is also what pushed the upward trend of the Great Resignation into something of a landslide-in-reverse and which catapulted the Great Resignation into the forefront of the American public consciousness.  The next post in this series will examine the paths taken by workers from various sectors of the American economy after they quit their jobs from 2020 onward.

P.S. While I have enjoyed many of the posts on Captain Cassidy's blog Roll to Disbelieve, I can't say that I agree with everything she has written.  For instance, I am still a Christian, whereas she has deconstructed to such an extent that she has rejected Christianity altogether.  However, I can't say that I blame her as I look at the sorry legacy of white American evangelicalism and its marriage to secular earthly economic and political power.

P.P.S. I have mentioned Donald Trump a few times in today's post.  Some from the Right may assert that I should not speak critically of him since he supposedly recently survived an "assassination attempt."  And I must say that while I despise Donald Trump, I do not condone any attempt to assassinate him.  However, when I read that his injuries were not life-threatening (in fact, some reports state that he was not actually hit by a bullet at all), I have to wonder if the whole "assassination attempt" wasn't some kind of publicity stunt or false-flag operation designed to boost his media profile and polling numbers.  I don't have much sympathy...

Book Recommendation - Flying Blind: The 737 MAX Tragedy and the Fall of Boeing

I recently bought an audiobook copy of Peter Robison's book Flying Blind: The 737 MAX Tragedy and the Fall of Boeing.  It's been a fascinating listen so far.  For those unfamiliar with the story, the 737 MAX is the most recent version of the Boeing 737 aircraft.  It was hastily (and some would say haphazardly) developed by the Boeing Company as a competitive response to the introduction of the Airbus A320neo family of commercial passenger aircraft by European aircraft manufacturer Airbus SE.  Airbus is now larger than Boeing and earns more revenue than Boeing, even though Airbus was founded decades after the founding of the Boeing Company.

One of the reasons why Airbus is now bigger and more influential than Boeing is the Boeing 737 MAX.  The various versions of the 737 have all arisen from an initial design that is nearly 60 years old, and which has been stretched and tweaked in order to compete and remain relevant in comparison to Airbus offerings.  In the case of the MAX, one of the modifications involved increasing the size of the engines and placing them far forward on the wings so that the center of gravity of the airplane was shifted relative to earlier versions of the 737.  This led to a natural aerodynamic tendency of the nose of the MAX to pitch upward at unwanted times during certain maneuvers.  Boeing could have responded to this problem by redesigning the aircraft's control surfaces, but Boeing upper management pushed hard to avoid any modifications that might cost money and slow deliveries of the airplane.  So they resorted to a software "fix" in the aircraft flight control computers that would force the nose of the aircraft down in the event that the computer and its sensors determined that the aircraft was about to enter a stall condition.  There were only a few problems with this solution...  One of these problems was that in budget versions of the aircraft, the computer depended on inputs from only one sensor, and if that sensor malfunctioned, the computer could crash the airplane.  Another problem was that when Boeing sold budget versions of the MAX to airlines (especially overseas airlines operating in the developing world), it did not tell pilots or aircraft owners about this software system.  As a result, there were two crashes of the 737 MAX in 2018 and 2019.  All crew and passengers were killed.  (One other thing to note: this year, in 2024, an emergency exit plug blew off an Alaska Airlines 737 MAX in flight, resulting in an explosive decompression and an emergency landing at Portland International Airport.)

One might ask how such a state of affairs was allowed to develop in an American company that used to be the epitome of American innovation and technological advancement.  Peter Robison's book describes how at the beginning of the jet age, Boeing became focused on being the best, most technically advanced aircraft manufacturer in the world, obsessed with pushing the envelope of aircraft design to produce the world's most advanced and capable passenger aircraft.  For instance, the Boeing 747 was the company's proudest achievement of the 20th century.  But all that changed when Boeing merged with McDonnell-Douglas in 1997, with the result that the focus of the Boeing company became maximizing shareholder value, revenue, and stock price while minimizing costs.  Thus over the next three decades the Boeing Company began to resemble a once proud, strong ox or bull being eaten from the inside by tapeworms.

Robison's Flying Blind is a gripping, exciting, emotive expansion and elaboration of a theme which was touched on briefly in the third chapter of a much drier and more stuffy academic book which I listened to back in 2022, namely, Obliquity: Why Our Goals Are Best Achieved Indirectly, by John Kay.  In that third chapter, titled "The Profit-Seeking Paradox: How The Most Profitable Companies Are Not The Most Profit-Oriented," Kay tells the story of a few well-known, formerly powerful companies which, to use my own words, began with the main goal to "do beautifully good work in order to meet necessary needs".  As they got really, really good at doing that kind of work, they naturally began to earn lots of money.  But as soon as they shifted their focus from being primarily about doing beautifully good work to making lots of money, they began to destroy themselves.  If you decide to read the book, note that Kay specifically mentions Boeing in this chapter.

Yet this is the character of almost all of American late capitalism in 2024.  This is also the economic philosophy pushed by all of the media outlets of the American Right.  This is not only leading to the hollowing-out of once-iconic American businesses by rich parasites, but is also contributing to the precarity and inequality that define American society at this time.  I can't help but think that this is going to end badly for the parasites at the top.  

The Coping Mechanisms of the Precariat, And Their Effects - Introduction

This series of posts on precarity has nearly finished sketching the outlines of the origins and spread of the precariat, as well as the global composition and local expressions of the precariat.  However, I must admit that one thing these posts have not dealt with in detail is the deliberate, willful attempt by a malignant privileged group in a society to force members of non-privileged groups into menial or precarious employment.  In other words, we have not dealt with the effects of racism and discrimination on precarity.  

There are a couple of reasons why I haven't dealt with this aspect of precarity in detail in this series of posts.  First, I have to confess that dealing with this subject is a real drag.  Let me just say it plainly.  As a Black American, I find it extremely distasteful to have to consider the revival of garbage that I thought had been over and done with by the time I got out of high school.  I find it incredible that so many white supremacist types would cling to their stupid notions of supremacy for decades, and that this desperate narcissism would find expression in political eruptions such as the candidacy of Sarah Palin in 2008, the candidacy of Donald Trump in 2016 and (Dear God, can it really be?!) in 2024, the continued existence of the media empires of Rupert Murdoch and people like him, and the continued efforts of one "special" group of people to Make Themselves Great by ruining everyone else.  Fortunately, the rest of the world seems to be escaping from the thrall of white American supremacists, and the United States is no longer the frontrunner in global peer-polity competition.

One other thing about dealing with this subject is the effects produced by the knowledge of the ways in which the predations of the privileged hurt the members of marginalized groups.  For the malignant narcissists among the privileged, such information serves as a source of narcissistic supply, because these people can point to the damage they do to others and tell themselves that this proves that they themselves are indeed powerful.  For the members of the marginalized, such information can tend to convince them that they have no agency, no ability to change their situation.  Such a notion is false.  To quote from an earlier post in this series, 

The inescapable reality is that the only thing that will reliably alter our situation is our choice to begin to organize ourselves for collective action.  As Maciej Bartkowski said in his book Recovering Nonviolent History, 

"The guilt of falling into . . . predatory hands . . . [lies] in the oppressed society and, thus, the solution and liberation need to come from that society transformed through its work, education, and civility.  Victims and the seemingly disempowered are thus their own liberators as long as they pursue self-organization, self-attainment, and development of their communities."

Or, to quote from Alex Soojung Kim-Pang,

"Collective action is the most powerful form of self-care."  (Emphasis added.) 

This collective action is wonderful when it succeeds.  It is rather depressing when such action is sabotaged or undermined or co-opted by Uncle Toms and Aunt Tammys, or when an oppressed people refuses to do the hard work of building collective self-reliance. 

Yet self-conscious, centrally planned collective action is not the only kind of collective action that exists.  Consider the "social nonmovements" described by Asef Bayat in his book Life as Politics.  Such "social nonmovements" can be described as

"the collective actions of noncollective actors; they embody shared practices of large numbers of ordinary people whose fragmented but similar activities trigger much social change, even though these practices are rarely guided by an ideology or recognizable leaderships and organizations." - Life as Politics, p. 14.

 In other words, social nonmovements consist of masses of people who don't necessarily deliberately associate with each other, yet who find themselves making similar responses to emergent social pressures and threats.  A social nonmovement is like a naturally formed (not manmade) cosmic laser or maser consisting of atoms or molecules which come together under natural forces to produce coherent light.  In the same way, social nonmovements can have disruptive effects on a social status quo.

In the next few posts in this series, we will begin to explore such a social nonmovement.  The forces which produced this nonmovement are the rise of toxic workplaces throughout the industrialized world, but especially in the United States, Britain and Australia.  The social nonmovement we will study is the Great Resignation, and the responses and life adjustments made by those who quit their jobs during the Great Resignation.  We will also examine the effects of the Great Resignation on established businesses.  (Hint: tolerating or deliberately creating a toxic workplace is an excellent way for a business owner to be forced out of business!)  Thus we are about to embark on the next stage in this series, namely, the coping mechanisms of the precariat.  Stay tuned...

Precarity and Artificial Intelligence: What "HAL" Might Do To "Dave's" Future

Note: the title of this post is a nod to an old, rather slow sci-fi movie with a mind-blowing ending that avoided cheesiness while actually being ahead of its time in many ways... 

This post is a continuation of my series of posts on economic precarity.  As I mentioned in my most recent post in this series, we have been exploring the impact of machine artificial intelligence (AI) on the future of  work, whether that work requires advanced education or not.  But perhaps it might be good to start with a more basic preliminary question: what might be the impact of AI on human life in general?  Of course, the answer to that question is dependent on two factors, namely, the kinds of predictions that are being made concerning the development of AI, and the likelihood of those predictions coming true.  Over the last several years, prognosticators have predicted massive disruptions to human life resulting from the massive and rapid development of AI capabilities.  The tone of these predictions has varied between the optimistic and the dystopian.  Let's limit ourselves to the optimistic for now and ask whether we would want to live in a world in which the most optimistic predictions came true.

One of the more optimistic points of view can be found in a book published in 2021 titled, AI 2041:

Ten Visions for Our Future, by Kai-Fu Lee and Chen Qiufan.  Dr. Kai-Fu Lee holds a PhD from Carnegie Mellon University and has founded or led a number of tech companies as well as doing extensive research and writing in the field of artificial intelligence.  Chen Qiufan is Chinese science fiction writer who formerly worked for tech companies Google and Baidu before launching into a full-time creative career.  AI 2041 is a multifaceted picture of Kai-Fu Lee's predictions of the evolution of AI capabilities from now to the year 2041, combined with Chen Qiufan's short stories portraying fictional settings in which each of these predictions comes true.  Among the things which Dr. Lee believes we are most likely to encounter are the following:

• The use of deep learning and big data paired with social media to guide customers of financial products into decisions and lifestyles which have the least risk of adverse outcomes and the greatest chance of net benefit as calculated by an AI objective function.  (See the story "The Golden Elephant.")
• The use of natural language processing and GPT as tools for creating customized virtual "teachers" for children.  (See the story "Twin Sparrows.")
• The use of AI tools for the rapid analysis of pathogens and the rapid development of drugs for emerging new diseases, as well as the use of automation in management of epidemics and pandemics.  (See the story "Contactless Love.")
• The displacement of skilled manual laborers by AI, and the use of AI to create virtual solutions for this displacement which return some sense of purpose to workers who have lost their jobs.  (See the story "The Job Savior.")
• The ways people cope with the likely displacements and disruptions which will be experienced by societies in which having one's basic needs met becomes decoupled from having to work to earn a living. (See the story "Dreaming of Plenitude.") 

Note that I have listed only five of the ten possible scenarios sketched by Dr. Lee.  However, these five are most relevant to the topic of today's post.  It is already becoming possible to design AI-powered virtual "life coaches" to guide people in their life decisions.  (In fact, if you really want to let your bloody smartphone tell you how to run your life, you can find apps here, here, and here for starters.)  However, when using these apps, one must remember that at their heart they are simply machines for optimizing objective functions which have been designed by humans and which have been tuned by massive amounts of human-supplied training data.  Thus these "coaches" will be only as smart (or as stupid) as the mass of humanity.  And they can be made to encode and enshrine human prejudices, an outcome which is especially likely whenever decisions involving money or social power are involved.  This is illustrated in the story "The Golden Elephant."  (For a harder-edged, more pessimistic view of this sort of AI application, please check out the short story "The Perfect Match" by Ken Liu.)

The use of AI tools in medicine for discovery of pathogen structure and rapid drug development is a fine example of the emerging use of machine implementation of multi-objective function optimization.  I truly have nothing but praise for this sort of application, as it has saved countless lives in the last half decade.  For instance, this sort of technology was instrumental in the rapid development of safe and effective COVID vaccines.  However, when we get to the use of AI to replace the kind of skilled labor that has historically depended on the development of human cognitive capabilities, I think we're headed for trouble.  Consider the case of teaching children, for instance, as exemplified by Chen Qiufan's short story "Twin Sparrows."  Teaching in modern First World societies has evolved into the delivery of a standardized curriculum by means of standardized methods to children, and the evaluation of the learning of these children by means of standardized tests.  

Now I know a little about teaching children, as I volunteered for a few years to be an after-school math coach.  And I can tell you that teaching arithmetic to one or a few children requires more than just knowing arithmetic.  It also involves emotional intelligence and the skill of careful observation as well as a certain amount of case-by-case creativity.  We must ask whether these things can be captured by an AI application that has been "optimized" to maximize learning.  How does one measure things like student engagement?  For instance, do we write some polynomial regression function in which one of the terms stands for whether the kid's pupils are dilated, another term stands for whether a kid's eyes are open and looking at the teacher or whether they're closed, another term captures whether a kid is sitting quietly or throwing a fit, etc.?  And what happens when we move beyond a standard curriculum?  How, for instance, do you make an AI "virtual" art teacher?

I won't attempt to answer these questions here, although I will mention that China has already begun to deploy AI in primary school education, as noted in the 2020 Nesta article titled, "The Future of the Classroom? China’s experience of AI in education" and the 2019 article "Artificial intelligence and education in China," which is unfortunately behind a Taylor and Francis paywall.  It will be interesting to see comprehensive, multi-year studies which document whether the use of AI in education is actually living up to its promise.  

But let's say that the deployment of AI in education really does turn out to be effective.  What happens to the human teachers in such a case?  Kai-Fu Lee says in AI 2041 that teachers will still be needed to be confronters, coaches, and comforters.  In fact, this seems to be a rather stock answer given whenever the potential massive occupational disruptions promised by the widespread deployment of AI are mentioned.  We are told that when jobs that formerly required powers of observation, quick assessment, logical reasoning, computational or motor skills, or memorization are taken over by AI, the newly-displaced workers can be retrained as "compassionate caregivers."  But it might be good to confront the fact that the widespread deployment of AI under an optimistic scenario would certainly mean the de-skilling of large numbers of people.  What possibly unforeseen effects would this de-skilling have on the displaced workers even if they were retrained as "compassionate caregivers?"

Consider, for instance, what might happen to London cab drivers if they were replaced by self-driving taxis.  To become a London taxi driver, a person must memorize a huge amount of London metro local geography, then pass a special test administered by the British government.  (From what I hear, you can't cheat on the test by using a GPS!)  All that memorization (especially visual memorization of London streets and intersections) induces strong development of key regions of the brains of aspiring London taxi drivers.  If this challenge is taken away from a London cabbie, he or she will lose that brain development.  Consider also the personnel who comprise flight crews of airliners.  Up to the 1960's, one of the positions on the flight deck of an airliner was the navigator.  But the navigator position was eliminated by autopilots.  So flight crews shrank from four to three people.  But then, further advances in automation eliminated the position of flight engineer.  So now flight crews consist of only two people.  What development was lost in the brains of the navigators when they were replaced by machines?  (What navigational feats are humans capable of when those humans are pushed to their cognitive limits?  Consider for instance how the peoples of Oceania learned to sail between their islands reliably and successfully without needing maps or a compass.)

AI has eliminated not only aircraft navigators and flight engineers, but an increasing number of other degreed professionals including medical radiologists, as well as receptionists, telephone operators, fast-food cooks, waiters, and waitresses.  AI "expert systems" are threatening the jobs of an increasing number of skilled, educated technical professionals, as noted here and here, for instance.  An increasing number of news stories are documenting the ongoing erosion of human labor markets by AI.  It must be asked what will happen to people whose jobs required the development of hard cognitive skills when those skills are replaced by AI.  Preliminary answers to that question are not encouraging.  For instance, the British Journal of Medicine published a 2018 article titled, "Intellectual engagement and cognitive ability in later life (the “use it or lose it” conjecture): longitudinal, prospective study," in which the authors concluded that lifelong intellectual engagement helps to prevent cognitive decline later in life.  There is also a 2017 article published in the Swiss Medical Weekly whose authors concluded that "low education and cognitive inactivity constitute major risk factors for dementia."  In other words, by ceding to AI the hard cognitive challenges which have traditionally been the hallmark of many kinds of paying work, we may well be at risk of turning ourselves into a society of de-skilled idiots.

Ahh, but there's more.  Let's consider the obvious fact that when AI takes over a job, one or more humans is thrown out of work.  Let's consider the response of various politicians to this fact.  For instance, let's consider the rhetoric spouted by crooks like Donald Trump and other Republican Party politicians (as well as their millions of adoring fans) in the run-up to the 2016 election.  Let's also consider the "scholarly" articles, ethnographic studies and books such as Hillbilly Elegy which sought to "explain" the Trump phenomenon.  One of the key assertions of the Trump crowd in 2016 was that the reason why the white American working class was becoming increasingly poor was the threat posed by immigrants (especially dark-skinned immigrants) taking jobs away from "real" Americans.  Thus America needed to build walls - made both of barbed wire and cement, and of policies and legislation -  in order to keep the great unwashed from stealing what "rightfully" belongs to America.  In other words, one of the biggest drivers of the growth of Trumpism was the loss of jobs and income among the white American working class.  But if concern about job losses was really so bloody important to the architects of Trumpism, why is it that they did not utter a single word in protest against the threat to jobs posed by the deployment of AI?  Why is it that NO ONE in the Rethuglican Party nowadays has anything bad (or even cautionary) to say about the use of AI by American businesses?  The silence of the Rethuglicans regarding the disruptions of AI can be explained quite simply.  AI helps business owners increase profits while reducing labor costs.  Thus AI helps the rich get richer.  Also, Trumpism is not and never was about bringing jobs back to the "working class".  It was rather always an expression of collective narcissism.  Thus all the talk about jobs, like all the rest of the rhetoric of the American Right, was and is utter crap.

To be sure, we do need to start having urgent conversations, both locally and on a wider scale, regarding the deployment of machine artificial intelligence in society.  Such conversations need to ask what AI can reasonably be expected to be able to do, as well as asking whether we really need machines to do what AI is promised to do.  If we decide that it is actually in our best interest to continue the massive development and deployment of AI, we need to figure out how to do this in such a way that we maximize the benefits of AI while minimizing our exposure to the potential downsides and negative externalities of AI.  Lastly, we need to start asking whether it might make sense to establish a basic universal income and other social structures which allow the people in our societies to develop their full human potential even in an era of the expanding use of AI.

Precarity and Artificial Intelligence: A Four-Wheeled Reason to be Skeptical about AI Optimism

The most recent post in my series on economic precarity hinted that the wildly optimistic claims of what artificial intelligence can do or is about to be able to do may be a bit overblown.  A case in point just surfaced this week: the Tesla Corporation (and its CEO Elon Musk in particular) are now being investigated by Federal prosecutors about claims made by Musk that Tesla's "self-driving car" AI technology has actually produced cars that drive themselves without any human input.  It seems this claim is not quite true, as "hundreds of crashes and dozens of fatalities" have proven over the last few years.  Musk may soon find himself the target of State-sponsored vengeance - a vengeance carried out by human prosecutors, plaintiffs, judges, and juries instead of robots.  They may optimize their "objective function" to return a guilty verdict.  Could this be the start of a rocky road for Musk ... ?

The "Principled" Boneheads

Today I ran into some members and organizers of the protests against Israeli violence in Gaza.  I had known about the war between Israel and Hamas, and had heard of the extremely disproportionate response of Israel, but I had not been personally involved in any protest action.  And on a certain level, today was no different for me in that my contact with the protesters was entirely a chance encounter that came about because we just happened to be in the same place at the same time.  During my encounter I saw that the protesters had printed a bunch of flyers urging voters in the Democratic primary to write "Uncommitted" in the ballot choice for the Presidency of the United States.

Now I can quite understand public outrage among many people in the United States over Israel's actions in this present war.  I can also understand why many people would characterize those actions as attempted genocide.  The brunt of Israel's violence has fallen on poor Palestinian civilians, especially women and children, and Israel has caused many tens of thousands of casualties among these.  Let me say right now that although I am a Christian and I believe Israel is God's earthly people, I most emphatically do not believe that God has created Israel to be a special pet who gets to trash the other peoples of the earth.  There is no nation on earth that has a right to make itself great by oppressing the powerless.  Therefore to the extent that I can, I am committed to such things as researching the country of origin of the things that are offered to me for sale, so that I can boycott those products which are made by nations that oppress.  That includes boycotting Israel.

However, when protesters in this country begin to urge withdrawing support from the Democratic party as a means of pressuring the Biden administration to withdraw military aid from Israel, I am reminded of how Russian operatives and propaganda organs managed to weaken and depress the Democratic vote in 2014 and 2016 by promoting "principled" spokespersons who pointed out to us all the weaknesses of Barack Obama and of Hillary Clinton.  I am also reminded of how the candidacy of Bernie Sanders weakened the candidacy of Hillary Clinton in 2016.  (Although Hillary won the 2016 popular vote by 2.7 million, evidently this was not enough of a margin to prevent Donald Trump from capturing the White House.  Go figure!)  I am also reminded of how operatives from the Right used mouthpieces such as Umair Haque in 2020 to try to do the same thing to Biden.

Such things as this make me wonder what it is that some of the present protesters really want.  If they really want to put the powerless into a better position to resist the predations of the powerful, they should consider what will happen to the powerless in the United States in the event of a wave of Republican victories in the November elections.  Among the things we are all likely to receive from such victories are the following:

• The continued erosion of the rights of women and dark-skinned ethnic minorities in the United States
• The continued concentration of wealth and corruption among the richest Americans
• The continued impoverishment of the poor and the continued expansion of economic precarity in the United States
• The continued expansion of fascism and the continued development of an American police state
• The continued erosion of American democracy
• The continued expansion of Russian power (including the possible loss of Ukraine to Russia)
• Oh, and by the way: the Republicans will also continue to support Israeli militancy, in case no one noticed.

Given these possible outcomes, why are the antiwar protesters trying to tamper with the 2024 American elections?  I can think of only two possible reasons.  First, they may be incredibly stupid in their idealism.  It is far too easy to make an emotional, yet senseless response to an evil situation.  Then, when one's emotional response turns out to have evil consequences, the person who made the response can try to comfort himself by claiming that he is paying the price of martyrdom.  To such people I say, please go to school and enroll in a crash course in strategic thinking.  Then think of some other way to put irresistible pressure (including economic pressure) on the powers that be in this country to force them to withdraw military support for Israel without engaging in actions that endanger the 2024 U.S. elections.  Just to be clear: I am all for pressuring the powers that be to withdraw military support for Israel as long as Israel continues to attempt genocide against the Palestinian people.

But maybe the attempt to tamper with the 2024 elections is itself an example of fine strategic thinking - although the strategy in question has an actual aim that is very different from the aim which its creators claim they want to see.  In that case, maybe some of the antiwar protesters are themselves being disingenuous.  

Precarity and Artificial Intelligence: Review of Objective Functions, and A Contrarian Perspective

This post is a continuation of my series of posts on economic precarity.  As I mentioned in recent posts in this series, we have been exploring the subject of the educated precariat - that is, those people in the early 21st century who have obtained either bachelors or more advanced graduate degrees from a college or university, yet who cannot find stable work in their chosen profession.  However, the most recent previous post in this series began to explore the impact of machine artificial intelligence (AI) on the future of all work, whether that work requires advanced education or not.  

In my my most recent post in this series, I wrote that the greatest potential for the disruption of the future of work through machine AI lies in the development of machine AI tools that can tackle the increasingly complex  tasks that are normally associated with human (or at least highly developed animal) intelligence.  Such tasks include machine vision (including recognizing a human face or an animal), natural language processing, construction of buildings, navigating physically complex unstructured and random environments (such as forests), and optimization of problems with multiple objectives requiring multiple objective functions to model.  Today I'd like to amend that statement by saying that there is another potentially massive disruptive impact of machine AI on the future of work, namely, the ways in which the wide deployment of machine AI in a society might condition and change the humans in that society.  I'll have more to say on that subject in another post.  What I'd like to do right now is to take another look at mathematical objective functions and their place in the implementation of machine AI.  But even before that, let's review the two main types of applications we are talking about when we talk about "machine AI".  Also, let me warn you that today's post will move rather deep into geek territory.  I'll try to have some mercy.

As I mentioned in the most recent post in this series, AI applications can be broken down into two main categories.  The first category consists of the automation of repetitive or mundane tasks or processes in order to ensure that these processes take place at the proper rate and speed and thus yield the appropriate steady-state or final outcome.  This sort of AI has been around for a very long time and first came into existence in entirely mechanical systems such as steam engines with mechanical governors that regulated the speed of the engines.  An example of a mid-20th century electromechanical control system is the autopilot with mechanical gyroscopes and accelerometers and simple electronic computers which was invented to guide airliners and early guided missiles during long-distance flights.  Other early electronic examples include the programmable logic controllers (PLC's) which were developed in the 1960's to regulate assembly line processes in industrial plants.  In his 2022 paper titled "The two kinds of artificial intelligence, or how not to confuse objects and subjects," Cambridge Professor Alan F. Blackwell characterizes these systems as servomechanisms, which Merriam-Webster defines as "an automatic device for controlling large amounts of power by means of very small amounts of power and automatically correcting the performance of a mechanism" and which Blackwell himself defines as "...any kind of device that 'observes' the world in some way, 'acts' on the world, and can 'decide' to 'behave' in different ways as determined by what it observes."  

The construction (and hence performance) of servomechanisms can be increasingly complex as the number and type of processes regulated by the servomechanisms increases, but that does not mean that the servomechanisms possess any real native intelligence.  Consider, for instance, a very simple servomechanism such as the thermostat from a heating system in a house built during the 1950's.  Such a thermostat would regulate the timing and duration of the burning of a fuel in a heating furnace, and would most likely consist of a simple switch with a movable switch contact and a stationary contact with the movable contact attached to a bimetallic strip.  Because the shape of the bimetallic strip is regulated by the temperature of the air, when the air temperature drops, the movable switch contact eventually touches the stationary contact, closing the switch and turning on the flow of fuel to the furnace.  We can say that the thermostat "decides" when the furnace turns on or off, but that's all this thermostat can "decide" to do.  You certainly wouldn't want to rely on the thermostat to help you decide what movie to watch with your spouse on a weekend!  Servomechanisms are what Blackwell calls "objective AI" which "measures the world, acts according to some mathematical principles, and may indeed be very complex, even unpredictable, but there is no point at which it needs to be considered a subjective intelligent agent."  In other words, all a servomechanism can do is to mechanically or electronically regulate a physical process on the basis of process measurements provided to the controller by means of physical sensors that sense a process variable.  It can't think like humans do.

The second type of AI is designed to make value judgments about the world in order to predict how the world (or some small subset of the world) will evolve.  In the most optimistic cases, this AI uses these value judgments to generate the most appropriate response to the world which is supposedly evolving according to prediction.  But is this really a native intelligence created by humans, yet now embodied in a machine and existing independently of humans?  A possible answer to that question can be found in another paper written by Blackwell and published in 2019, titled, "Objective functions: (In)humanity and inequity in artificial intelligence."

The value judgments and predictions made by the second type of AI are made by means of objective functions. These objective functions are mathematical abstractions consisting of functions of several independent variables.  Each of the independent variables represents an independently controllable parameter of the problem.  If the purpose of the objective function is to predict the numerical value of an outcome based on historical values of independent input variables, then optimizing the function means making sure that for a given set of historical inputs, the objective function yields an output value that is as close as possible to the historical outcome associated with the particular historical inputs. This ensures that for any set of future possible inputs, the objective function will accurately predict the value of the output.  Two levels of objective functions are needed: the first level, which makes a guess of the value of an output based on certain values of inputs, then a second supervisory level which evaluates how close each guess is to a set of historical output values based on corresponding sets of input values.  The output of this second supervisory objective function is used to adjust the weights (in the case of a polynomial function, the coefficients) of the primary objective function in order to produce better guesses of the output value. 

Objective functions are mathematical expressions; hence, the second type of AI is a primarily mathematical problem which just happens to be solved by means of digital computers.  This also includes the implementation of multi-objective optimization, which is really just another mathematical problem even though it is implemented by machines.  Thus, the second type of AI is really just another expression of human intelligence.  This is seen not only in the development of the objective functions themselves, but also in the training of the supervisory objective function to recognize how close the output of the primary objective function is to the a value that actually reflects reality.  This training takes place by several means, including supervised learning (in which humans label all the training data), and partially-supervised and unsupervised learning (in which the training data is out there, but instead of it being labeled, a human still has to create the algorithms by which the machine processes the training data).  

An example that illustrates what we have been considering is the development of large language models (LLM's) such as ChatGPT which predict text strings based on inputs by a human being.  A very, very, very simple model for these AI implementations is that they consist of objective functions that guess the probability of the next word, phrase, sentence, or paragraph in a string on the basis of what a human has typed into an interface.  These AI implementations must be trained using data input by human beings so that they can calibrate their objective functions to reduce the likelihood of wrong guesses.  Cases like these lead scientists such as Alan Blackwell to conclude that the second type of AI is not really a separate "intelligence" per se, but rather the embodiment and disguising of what is actually human intelligence, reflected back to humans through the intermediary of machines.  The calibration of the objective functions of these AI deployments (or, if you will, the training of this AI) is performed by you every time you type a text message on your smartphone.  For instance, you start by typing "Hello Jo [the phone suggests "Jo", "John", "Joe", but the person you're texting is actually named "Joshiro", so as you type, your phone keeps making wrong guesses like "Josh", "Joshua", and "Josh's" but you keep typing until you've finished "Joshiro"]. You continue with "I'm at the gym right now, but I forgot my judo white belt [the phone guesses almost everything even though you misspelled "at" as "st" and the phone auto-corrected.  However, the phone chokes when you start typing "judo" so you have to manually type that yourself].  You finish with "Can you grab it out of my closet?"  The next time you text anyone whose first name starts with the letters "Jo", your phone will be "trained" to think you are texting Joshiro about something related to judo - or more accurately, the generative LLM in your phone will have determined that there is a statistically higher likelihood that your message will contain the words "Joshiro" and "judo".  Your phone's LLM is thus "trained" every time you correct one of its wrong guesses when you type a text.

Of course, the longer the predicted phrase or sentence or paragraph the AI is supposed to return, the more training data is required.  The boast of the developers of large language models and other similar AI implementations is that given enough training data and a sufficiently complex statistical objective function, they can develop AI that can accurately return the correct response to any human input.  This unfortunately leads to an unavoidable conclusion of the second type of AI: the assumption that the universe, reality, and life itself are all deterministic (thus there is no free will in the universe).  Why? Because the kind of intelligence that can accurately predict how the universe and everything in it will evolve and thus generate the most appropriate local response to the moment-by-moment evolution of your particular corner of the universe can always be modeled by an appropriately elaborate statistical objective function trained on an appropriately huge set of training data.  In other words, given enough data, a statistical objective function can be derived which accurately predicts that your spouse's sneeze at the dinner table tonight will provoke an argument which ends with you sleeping on the couch tomorrow night, and that this will lead to the invention of a new technology later in the week that causes the stock market of a certain country to crash, with the result that a baby will cry on a dark and stormy night five days from now, and that this cry will enable a computer to predict all the words that will be in the novel you sit down to write a week from today...  This is my rather facetious illustration of the "generative AI" of chatGPT and similar inventions.  The fallacy of determinism is that life, the Universe, and reality itself are full of phenomena and problems that can't easily be modeled by mathematics.  Scientists call them "wicked problems."  Thus the claims made about the second kind of AI may be overblown - especially as long as the implementation of this second kind of AI remains primarily dependent on the construction and optimization of appropriately complex statistical objective functions.

Yet it can't be denied that the second type of AI is causing some profound changes to the world in which we live, and even the first type of AI - the implementation of servomechanisms, or the science of cybernetics - has had a profound effect.  The effects of both types of AI to date - especially on the world of work - will be the subject of the next post in this series.

P.S. Although I am a technical professional with a baccalaureate and a master's degree in a STEM discipline, I am most definitely NOT an AI expert.  Feel free to take what I have written with a grain of salt - YMMV. 

P.P.S. For more commentary on ChatGPT and other LLM's, feel free to check out a 2021 paper titled, "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" by Emily Bender and others.  The authors of this paper were Google engineers who were fired by Google for saying things that their bosses didn't want to hear regarding LLM's.   Oh, the potential dangers of writing things that give people in power a case of heartburn...