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...

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: 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...

 

Precarity and Artificial Intelligence: The Foundations of Modern AI

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.  Today's post, however, will begin to explore a particular emerging impact to employment for everyone, whether formally educated or not.  That impact is the impact of machine artificial intelligence on the future of work.

As I mentioned in a previous post, 

Labor casualization has been part of a larger tactical aim to reduce labor costs by reducing the number of laborers...This reduction of the total number of laborers can be achieved by replacing employees with machines.  That replacement has been occurring from the beginning of the Industrial Revolution onward, but in the last two or three decades it has accelerated greatly due to advances in artificial intelligence (AI).  A long-standing motive behind the recent massive investments in research in artificial intelligence is the desire by many of the world's richest people to eliminate the costs of relying on humans by replacing human laborers with automation.

So it is natural to ask what sort of world is emerging as the result of the use of increasingly sophisticated AI in our present economy.  Here we need to be careful, due to the number of shrill voices shouting either wildly positive or frighteningly negative predictions about the likely impacts of AI.  I think we need to ask the following questions:

• First, what exactly is artificial machine intelligence?  What is the theoretical basis of AI?  How does it work? ...

Today we'll start trying to answer the questions stated above.   And at the outset, I must state clearly that I am not an AI expert, although my technical education has exposed me in a rudimentary way to many of the concepts that will be mentioned in our discussion of AI.

First, let's paint a picture.  One of the original motives for trying to invent intelligent machines was the desire for machines that would reliably do the kind of mundane tasks that humans find to be distasteful or unpleasantly difficult.  This desire actually has a very long history, but was popularized in the science fiction of the mid-20th century.  Think of a kid from the 1950's or 1960's who wished he had a robot that could vacuum the living room carpet or take out the garbage or do homework or shovel snow out of the driveway so that the kid could play without being bothered by parents demanding that the kid himself do the tasks listed above.  What kind of "brain" would the robot need in order to know what tasks needed to be performed and when would that brain know the tasks had been performed to acceptable standards?

The question of the kind of "brain" required was solved by the invention of the first programmable all-electronic digital computer during World War Two.  This computer was itself an evolution of principles implemented in previous mechanical and electromechanical computers.  Once engineers developed digital computers with onboard memory storage, these computers became capable of automation of tasks that had been formerly automated by more crude mechanical and electromechanical means such as relays.  Thus the 1950's saw the emergence of digital control systems for automation of chemical processes at refineries; the 1950's and 1960's saw the emergence of computer-assisted or computer-based navigation for ships, aircraft, missiles, and spacecraft; and the late 1960's saw the emergence of programmable logic controllers (PLC's) for automation of factory processes at industrial assembly plants.

The digital electronic automation systems that have been developed from the 1950's onward have thus formed a key component of the development of modern machine AI.  But another key component consists of the principles by which these systems achieve their particular objectives.  These principles are the principles of mathematical optimization, and they also have a rather long history.

Mathematical optimization is the collection of techniques and methods for finding the maximum or minimum value of a mathematical function of one or more independent variables.  Some of the earliest methods of mathematical optimization were based on calculus.  More complex methods include such things as numerical methods for solving nonlinear differential equations.  These methods were only possible to implement easily once electronic digital computers became available.

The first step in optimizing real-world problems consists of turning a real-world problem into a mathematical abstraction consisting of a function of several independent variables.  Each of the independent variables represents an independently controllable parameter of the problem.  Then optimization techniques are used to find the desired maximum or minimum value of the function.  To put it another way,

"Optimization is the act of obtaining the best result under given circumstances.  In design, construction, and maintenance of any engineering system, engineers have to take (sic) many...decisions.  The ultimate goal of all such decisions is either to minimize the effort required or to maximize the desired benefit.  Since the effort required or the benefit desired in any practical situation can be expressed as a function of certain decision variables, optimization can be defined as the process of finding the conditions that give the maximum or minimum value of a function."  - Engineering Optimization: Theory and Practice, Rao, John Wiley and Sons, Inc., 2009

The function to be optimized is called the objective function.  When we optimize the objective function, we are also interested in finding those values of the independent variables which produce the desired function maximum or minimum value.  These values represent the amount of various inputs required to get the desired optimum output from a situation represented by the objective function.  

A simple case of optimization would be figuring out how to catch up with a separate moving object in the shortest amount of time if you started from an arbitrary starting position.  To use optimization techniques, you'd turn this problem into an objective function and then use calculus to find the minimum value of the objective function.  Since the velocity and acceleration are the independent variables of interest, you'd want to know the precise values of these (in both magnitude and direction) which would minimize the value of the objective function.  Note that for simple trajectories or paths of only two dimensions, adult humans tend to be able to do this automatically and intuitively - but young kids, not so much.  Try playing tag with a five or six-year-old kid, and you will see what I mean.  The kid won't be able to grasp your acceleration from observing you, so he will run to where he sees you are at the moment he sees you instead of anticipating where you'll end up.  Of course, once kids get to the age of ten or so, they're more than likely to catch you in any game of tag if you yourself are very much older than 30!

The easiest AI problems are those that can most easily be turned into mathematically precise objective functions with only one output variable.  Examples of such problems include the following: reliably hitting a target with a missile, winning a board game in the smallest number of moves, traveling reliably between planets, simple linear regression, regulating the speed or rate of industrial or chemical processes, and control of HVAC and power systems in buildings in order to optimize interior climate, lighting, and comfort.

Harder AI problems 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.  The machine vision and natural language processing problems are harder because they require the use of logistical regression functions as objective functions, and in order to accurately assign the appropriate "weights" to each of the variables of these objective functions, the AI which implements these functions needs massive amounts of training data.  However, these and other harder problems are now being solved with increasing effectiveness through technologies such as deep learning and other advanced techniques of machine learning.  It is in the tackling of these harder problems that AI has the greatest potential to disrupt the future of work, especially of cognitively demanding work that formerly only humans could do.  In order to assess the potential magnitude and likelihood of this disruption, we will need to examine the following factors:

• The current state of the art of machine learning
• The current state of the art of designing objective functions
• And the current state of the art of multi-objective mathematical optimization.

I'll try tackling these questions in the next post in this series.

Introducing the Main Street Alliance

I'd like to take this opportunity to introduce readers to the Main Street Alliance, an organization which seeks to foster the creation and growth of small businesses in the United States.  As I resume my series of posts on the problem of economic precarity, I will also discuss solutions.  As I mentioned in a previous post, I believe that the eradication of the monopoly power of the rich and the fostering of small business among the poor are two strategic efforts which can reduce or eliminate economic precarity in the United States.  This is what the Main Street Alliance is working to achieve.

Those who read about the activities of the Main Street Alliance will also learn about how the rich and the powerful in the United States are trying to destroy small businesses, especially those run by minorities, and how these bad actors are using Republican-appointed Federal court justices in their attacks against small business.  This should be of great concern to those of you who are small entrepreneurs.  The latest attack against small business consists of judicial challenges to the Federal tax code.  Readers of this blog can learn from the Main Street Alliance website how they can join in the fight to foster and protect small business.

Precarity, Late Capitalism, And Artificial Intelligence: Pinocchio's Mischief

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.  The two most recent previous posts in this series discussed the fact that there are now more college graduates being produced in our society than there are jobs into which to plug those graduates.  The most recent post discussed why this is the case.  As I wrote last week, 

"...the decline in opportunities for college graduates (along with everyone else) is correlated with the rise in the concentration of economic power in the hands of an ever-shrinking elite.  In fact, I will go even farther and assert that the decline in stable employment for college graduates (even those with technical professional degrees) is a direct outcome of the concentration of economic power at the top of society.

Consider the fact that as of 2015, "America's 20 wealthiest people - a group that could fit comfortably in one single Gulfstream G650 luxury jet - now own more wealth than the bottom half of the American population combined..."  These people therefore have an enormous amount of economic and political clout.  And they have used (and continue to use) that clout in order to turn the American economy into a machine whose sole function is to make them as rich as possible.  The increase in precarity, the casualization of increasing types of employment, and the increasing use of task automation and artificial intelligence are typical of the strategies which these wealthy and powerful people have deployed in order to maximize the wealth they can extract from the American economy while minimizing the amount of wealth they give to the rest of us.  The aggressive expansion of the "gig" economy is another such strategy..."

A basic strategic aim in capitalism is that business owners should maximize profit.  A basic tactic for the achievement of this aim is to maximize profit per unit of goods sold by lowering the cost of production for each unit of goods sold.  Lowering costs can be achieved by attacking the cost of materials, capital machinery, energy, and labor.  In the limit, at the extreme of optimization, this leads to extremely flimsy goods sold for extremely high prices, goods that are produced by extremely poor laborers.

The labor part of this tactic is what we have been discussing in our consideration of precarity.  By making employment casual and temporary, with no fixed covenant between businesses and laborers and no benefits (other than a wage) granted to laborers, businesses have succeeded in driving down the cost of labor.  As mentioned in last week's post, that pressure on labor costs has reached even technical professions requiring a baccalaureate degree or above.  This is leading to an increasingly unsustainable situation in which, for instance, you might spend more than $40,000 to earn a four-year engineering degree - only to find yourself working for an engineering temp agency after graduation!

Labor casualization has been part of a larger tactical aim to reduce labor costs by reducing the number of laborers.  If you're the CEO of a large company, the progression of this tactic can be sketched as follows: First, destroy any expectation of stable employment or decent wages among your labor pool.  Then, reduce the actual number of laborers you use.  This reduction of the total number of laborers can occur by a number of means (including working employees to death by giving each employee the amount of work that should normally be handled by two or three such employees).  It can of course also be achieved by replacing employees with machines.  That replacement has been occurring from the beginning of the Industrial Revolution onward, but in the last two or three decades it has accelerated greatly due to advances in artificial intelligence (AI).  A long-standing motive behind the recent massive investments in research in artificial intelligence is the desire by many of the world's richest people to eliminate the costs of relying on humans by replacing human laborers with automation.

So it is natural to ask what sort of world is emerging as the result of the use of increasingly sophisticated AI in our present economy.  Here we need to be careful, due to the number of shrill voices shouting either wildly positive or frighteningly negative predictions about the likely impacts of AI.  I think we need to ask the following questions:

• First, what exactly is artificial machine intelligence?  What is the theoretical basis of AI?  How does it work?
• What can AI do and not do?
• What countries are at the forefront of AI deployment in their societies?
• How will AI capabilities likely evolve over the next few decades?
• What effects might AI have on human life and human societies over the next few decades?
• How will AI affect the world of work over the next few decades?

The next few posts in this series will attempt to tackle these questions.  I must warn you that what you'll get in those posts are merely my guesses at an answer.  However, because I want the guesses to be educated guesses, I'm going to need to do some research.  So these guesses might be slow in coming.

The Educated Precariat: Why The Mismatch?

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.  The most recent previous post in this series discussed the university system as a machine that produces graduates for use within the larger machinery of modern late-stage capitalism, and what is happening to those graduates because of the fact that there are more graduates being produced than there are jobs into which to plug those graduates.

That previous post highlighted the fact that from at least the 1990's onward (and possibly starting from the 1970's onward), there has been a growing number of college graduates who have found themselves underemployed after graduation.  Moreover, as time has passed, the number of college graduates who have entered long-term underemployment after graduation has increased as a percentage of total college graduates.  Note that to be underemployed as a college graduate means to hold a job that does not require the knowledge, skills, and abilities that a person would acquire as part of a college education.  As a hypothetical example, think of a gas station cashier with a recent baccalaureate degree in organizational psychology.  Moreover, the sources cited in that post listed the types of college major most likely to lead to underemployment and precarious work.  From those sources it would seem that baccalaureate degrees in STEM (science, technology, engineering, and mathematics) offer the greatest likelihood of full employment and decent wages.  However, note that a 2018 Canadian study titled, "No Safe Harbour: Precarious Work and Economic Insecurity Among Skilled Professionals in Canada" cited the fact that a technical professional degree is no longer an ironclad guarantee against precarious employment.  

Why then is there such a huge mismatch between the number of people obtaining degrees and the number of available jobs which would utilize the skills implied by these degrees while paying the degree holders a decent living wage?  That is the question which today's post will try to answer.  

First, let's consider the answer offered by people like Peter Turchin, the well-fed Russian emigre to the United States whom I mentioned in another post in this series on precarity.  Turchin asserts that the supposed "excess" of college graduates, the supposed "mismatch" between the number of college graduates and the number of appropriate jobs for these graduates, is the result of an imbalance between the higher education sector and the rest of the economy.  He also asserts that the "excess" of college graduates is increasing the likelihood of instability in society caused by the radicalization of these "excess" graduates.  To put it in the language of Wikipedia, 

"Elite overproduction is a concept developed by Peter Turchin, which describes the condition of a society which is producing too many potential elite members relative to its ability to absorb them into the power structure. This, he hypothesizes, is a cause for social instability, as those left out of power feel aggrieved by their relatively low socioeconomic status." [Emphasis added.]

Note the first sentence and its mention of the capacity of a society to absorb newly educated citizens into an existing power structure.  I will return to the notion of existing power structures later in this post.  Note also that Turchin's "solution" to this problem of "overproduction" is to limit access to higher education.  This "solution" is remarkably similar to the "solution" proposed by Richard Vedder, Christopher Denhart, and Jonathan Robe in their 2013 report titled, "Why Are Recent College Graduates Underemployed? University Enrollments and Labor-Market Realities" which I cited in the previous post in this series.  To quote their report,

"The mismatch between the educational requirements for various occupations and the amount of education obtained by workers is large and growing significantly over time. The problem can be viewed two ways. In one sense, we have an “underemployment” problem; College graduates are underemployed, performing jobs which require vastly less educational tools than they possess. The flip side of that, though, is that we have an 'overinvestment' problem: We are churning out far more college graduates than required by labor-market imperatives. The supply of jobs requiring college degrees is growing more

slowly than the supply of those holding such degrees. Hence, more and more college graduates are crowding out high-school graduates in such blue-collar, low-skilled jobs as taxi driver, firefighter, and retail sales clerks..."

In evaluating whether these assertions are valid, it is helpful to consider the present-day structure of the American economy as a representative of the typical economies of the Global North.  It is also helpful to consider the background of the people who have made these assertions in order to glimpse something of their possible motives.  As I mentioned previously, Peter Turchin is an academic who is already both tenured and well-established (thus well-fed, with multiple income streams), and his assertions of the need to limit access to higher education are not likely to hurt him in any way.  As for Vedder, Denhart, and Robe, Vedder is an adjunct member of the American Enterprise Institute (AEI).  Denhart is one of Vedder's former students.  I don't know how much of Vedder's ideology was passed on to Denhart and Robe, but I do know that Vedder is a strong supporter of big business even when it pays exploitative wages to workers, as seen in his support of Wal-Mart and of the 2008 taxpayer bailout of American businesses deemed to be "too big to fail".  (Note that that 2008 taxpayer-funded bailout is one of the biggest reasons why the richest Americans are now so rich!) Moreover, the AEI itself has the policy goal of supporting big business at the expense of small businesses, going as far as advocating that the role of the American government should be to help big businesses grow bigger.  The AEI wants further to eliminate all government support for small business, especially small business incubation, as I pointed out in a previous post.

From such observations, it is possible to move to a consideration of the structural reasons for the mismatch between jobs requiring a college education and the supposed "excess" of college graduates.  I will once again state my belief that high-quality, advanced education should be made available to as many people as want it - regardless of race, creed, national origin, or economic status.  Moreover, I once again assert that education is one of the great equalizing factors in a society, as it is a key component in the struggle of historically oppressed peoples to liberate themselves from historical and ongoing oppression.  This, for instance, was the motivation for the Polish underground "flying universities" which were organized in the 1800's when Poland had been partitioned by Germany, Austria, and Russia, and these nations had forbidden Poles from having access to higher education.  This was also the motivation for the underground "freedom schools" which sprang up in the American South during the antebellum days when white Southern power made it illegal to teach Black people (my people) to read.

But education alone is rather impotent without an opportunity to use it.  And the opportunities for the use of education are constrained by the structure of the society in which that education must operate.  Too often, the structure of a society is dictated and constrained by the dominant power-holders in that society.  I will therefore suggest that the decline in opportunities for college graduates (along with everyone else) is correlated with the rise in the concentration of economic power in the hands of an ever-shrinking elite.  In fact, I will go even farther and assert that the decline in stable employment for college graduates (even those with technical professional degrees) is a direct outcome of the concentration of economic power at the top of society.

Consider the fact that as of 2015, "America's 20 wealthiest people - a group that could fit comfortably in one single Gulfstream G650 luxury jet - now own more wealth than the bottom half of the American population combined..."  These people therefore have an enormous amount of economic and political clout.  And they have used (and continue to use) that clout in order to turn the American economy into a machine whose sole function is to make them as rich as possible.  The increase in precarity, the casualization of increasing types of employment, and the increasing use of task automation and artificial intelligence are typical of the strategies which these wealthy and powerful people have deployed in order to maximize the wealth they can extract from the American economy while minimizing the amount of wealth they give to the rest of us.  The aggressive expansion of the "gig" economy is another such strategy, as is the crafting of laws and regulations (especially by Republicans) which disadvantage small businesses (and all the rest of us, especially those of us who are not of their "tribe") while giving breaks to big business.  

What would a society look like if it provided citizens with the maximum optimal education and the maximum optimal opportunity to use that education in the pursuit of meaningful work?  I'd like to suggest that first, such a society would have a mechanism in place to prevent any one person or entity from concentrating more than a very small fraction of economic output into one set of hands.  Second, I suggest that such a society would be composed largely of artisans, artists, and small businesses owners who exercised their knowledge, education, and creativity to a maximal extent.  In other words, this society would be largely composed of "yeoman entrepreneurs" similar to the "yeoman farmers" idealized by Thomas Jefferson.   Some might say that such a society would be impossible in the 21st century, but I'd like to suggest that some positive aspects of what such a society might look like can be found in the depiction of the fictional Mars City in Hao Jingfang's novel Vagabonds.  I will mention that novel again in a future post. (Note also that although there was much to like about Mars City, it was not exactly a perfect utopia - there were indeed a few flies in that ointment, so to speak.)

Lastly, I suggest that such a society would be resilient - much more so than a more stratified, unequal society would be.  This is because such a society would have a much higher degree of decentralized group intelligence than would exist in a society of stratification and inequality.  This would make the more egalitarian society much more able to respond to emergent threats and opportunities than the more stratified society.  Consider the late 19th century and early-to-middle 20th-century history of Britain as a stratified society of the Global North.  Consider how its rigid class hierarchy and caste system prevented some of its principal actors from seeing the big picture and acting appropriately in the face of challenges.  Cases in point include the failure of Robert Scott's Antarctic expedition in comparison to the successful expedition of Roald Amundsen, as well as failures in World Wars 1 and 2 that resulted from a hidebound British system of honor, privilege and caste which blindsided British leadership.  The strident attempt by the Republican Party and other right-wing elements in the United States to re-establish an American system of caste and privilege constitutes the real threat to the "existing power structures" cited by Turchin, because it is leading to the "fragilization" of these structures.

The Educated Precariat: The Seedlings Of Early Trees

This post is a continuation of my series of posts on economic precarity.  As I mentioned in recent posts in this series, we are now starting to delve the subject of the educated precariat - that is, those people 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.  I suggest that the troubled lives of the educated precariat are a symptom of the troubled state of higher education generally - especially in the First World (also known as the Global North).  Two troubled groups come immediately to mind, namely, academics (college professors or salaried researchers) and college or university graduates.  We will explore the plight of new college professors and researchers later.  But suffice it to say that the guaranteed career of a tenured professor is increasingly out of reach for this group.  (See also, "Tenure Track for Professors In States Like Texas May Disappear," USA Today, 13 April 2023.)  A third group that may not know it's in trouble consists of new and continuing college and university students whose necks will one day be broken by the mousetrap of student loan debt.  A fourth group consists of the administrators and employees of the system itself.  Their trouble arises from the fact that they are running out of a key resource, namely, new students!  This is due to a number of factors, such as declining birth rates, as well as a sober realization on the part of young men and women that college education itself has begun to yield sharply diminished returns even as it has become unbearably expensive.

In considering the historical role of higher education in the development of global civilizations, it is natural to ask how things got to this state in which American higher education has begun to crumble. Where exactly did we come from that we have arrived at this destination?  To answer that question, we need to look at where we started from - in other words, it's time to look at the historical origins of education in general and of higher education in particular.

The first thing we notice is that there are records on almost every continent from almost every civilization describing the origins and evolution of formal education and of the creation of higher education systems. Ancient places of higher learning can be found in places such as these (this is a very partial list, by the way):

• Mali, African Continent (University of Timbuktu containing the Sankore Madrasah)
• Tunisia, African Continent (University of Ez-Zitouna)
• China 
• Academy of the White Deer Cavern (白鹿洞書院); 
• Songyang Academy (嵩陽書院) (This was a private rather than public institution.)
• Taixue (太學) ("National University" established by Emperor Wu)
• Greece, European Continent
• The school of Isocrates
• The school of Plato (Also known as the "Academy", which was the first historical use of this term in the West.)  (See also Higher Education In Greece, Kyriazis and Asderaki, CEPES, 2008)
• The school of Pythagoras
• The Byzantine Empire
• The Pandidakterion (known today as the University of Constantinople)

Note that although some of these institutions are called "universities," the actual entity known as the modern university did not come to being until the Middle Ages in Europe.

The entire educational process including both primary and higher education has been documented for the Greco-Roman and Chinese cases, and so it is useful to examine these cases in more detail.  First, let's consider the Greco-Roman case.  And in the case of Greece, we must consider the distinction between education in the Athenian city-state and education in Sparta.  According to Wikipedia, formal education in Athens was reserved for boys who were free-born.  The education of slaves was forbidden.  Formal education was conducted by either public schools or by private tutors.  I was not able to find out how much access to public schooling depended on family wealth, but the sources I have found do indicate that the extent of this formal education did depend on how much a family could afford to pay.  Access to higher education was strictly on the basis of a student's ability to pay, and it appears that the system of higher education was largely created and run by private individuals with sufficient means for leisure.  Thus figures such as Aristotle and Plato could be considered a kind of educational entrepreneur.  As for Sparta, while both free men and free women could participate, the purpose of Spartan education was solely to train the nation for war-fighting.

A funny thing happened to educated Athenian Greeks who had enjoyed the status of free-born intellectuals: when the Greek city-states were conquered by and absorbed into the Roman Empire, these free-born intellectuals became slaves themselves.  However, these educated slaves were able to lighten the burden of their slavery by becoming tutors and founding their own private schools (often with very slim profit margins).  This system of private education began to assume the role which Roman fathers as heads of households had traditionally held as the educators of their children.  In the Roman empire, there was no state-funded public education, either at the primary or the secondary level.  Yet those who wanted to participate in Roman politics were required to obtain a formal higher education.  This limited participation in Roman politics to the wealthy.  Also, whereas in Greece, higher education was seen as an activity of leisure which should not be tainted by any practical application (From Formal to Non-Formal: Education, Learning and Knowledge, pages 8 and 9), in the Roman empire the situation was different.  For Romans insisted that all education should have some practical purpose.  

In China, primary education began as an informal, communal process.  According to Dr. Ulrich Theobald, "The oldest word for "school" is xiang 庠, which actually means a building for livestock with two facing walls, where elderly people reared sheep, pigs or cattle and at the same time were entrusted with the duty to watch children and instruct them."  Primary education in China eventually evolved into a system of both private and public schools.  The public schools came into being during the Tang and Ming periods.  These schools, along with private primary schools and tutors, prepared students to enter the Chinese academy system, which then prepared promising students for posts in the Chinese civil service.  A couple of noteworthy facts regarding these academies is that there were times when private academies were either outlawed, disbanded, or taken over by the state as exemplified by the emperor.  Also, there were periods in which the state created or funded public academies in the academy system.  Lastly, some of the academies of the 18th and 19th centuries assumed research duties in addition to teaching.  The Taixue 太學 "National University" had already assumed a research role during the Southern Dynasties period from 420 to 589 AD.  

From the Chinese and Greco-Roman cases we can see that a key function of ancient higher education was to produce an elite class - that is, people who could either participate in politics and governance as ruling practitioners of statecraft, or as people who could serve as competent administrators/bureaucrats under these ruling elites.  Therefore the function of many ancient institutions of higher learning was not primarily research, although, as noted above, exceptions to this did exist in both ancient Greece and in China.  Stronger examples of a focus on both research and applied knowledge can be found in the Academy of Gondishapur in what is now modern Iran.  This academy was a center for the learning of medicine and science, among other subjects, and the modern hospital system owes much of its inspiration and foundational philosophy to this academy.  The Sankore Madrasah on the African continent also evolved a research function, although its main original purpose was Islamic education.  We don't have time today to explore the beginnings of the modern European university, but suffice it to say that the modern university system seems from the outset to have had the dual purposes of research and teaching.  Thus the early modern universities took over the function of producing the clerics of the Roman Catholic Church (the Western form of the mandarin administrator) in addition to producing research.

What is interesting to note is how systems of higher education fare in societies undergoing decline.  The Byzantine system of higher education is a key example.  The vicissitudes of the Byzantine empire in the 7th and 8th centuries and in the 13th century dramatically decreased the central government's ability to fund higher education and led to the privatization of higher education.  It is certain that this influenced the supply of competent practitioners of statecraft as well as competent administrators.  It is also true that declining Byzantine imperial power also produced declines in the number of jobs available to would-be mandarins who graduated from any Byzantine program of higher education.  This has significant implications for the American system of higher education, as the process of accelerating inequality continues in the United States, and as the rich parasites at the top of the food chain continue to suck nutrients from the rest of society.  More on that in another post.

The Educated Precariat - A Preview

It is now nearly time to consider a particular subset of the precariat, namely, those people who have college degrees yet who have been forced into precarious employment - especially, those who are in low-wage jobs.  In this consideration we will move beyond the United States to look at the surplus of college graduates and the lack of appropriate employment for these graduates as a global phenomenon.  We will find that among the ranks of these are coffee shop baristas with graduate training in fields such as psychology as well as technical professionals hired by temp agencies and the legions of adjunct professors at public and private universities throughout the United States.  We will consider the underemployed college graduate in both American, European and Chinese contexts, and compare these to the ranks of underemployed graduates in the developing world.  We will also try to examine the phenomenon of college graduate precarity as it exists in Russia.  However, examining the Russian case may prove difficult if one wants a recent and accurate snapshot, due to the fact that the regime of Vladimir Putin has been trying as hard as possible over the last few years to patch up the fig leaf dress which Russia has sewn to cover up its putrid nakedness.  (In fact, it has become much easier to obtain an accurate picture of daily life for ordinary people in China than in Russia.  China is actually more open and honest!)

Today's post will ask some preliminary questions.  First, how did we get to this present place in which a four-year or advanced college degree is no longer a guarantee of stable, middle-class employment?  To answer this question, we will need to answer the following questions:

• What was the original purpose of college?  Note that the word "college" comes from the Latin word collegium, defined by Wiktionary as "colleagueship (connection of associates, colleagues, etc.", guild, corporation, company, ... (persons united by the same office or calling or living by some common set of rules), college (several senses), school ..."
• What did the world's first colleges look like?  You may not know this, but one of the world's oldest continuously operating universities is the University of Ez-Zitouna, which was founded in Tunisia on the African Continent.  What was the mission of the world's first and earliest universities, and how was that mission funded and carried out?  How did the roles of education and research interact?
• What was the origin of the system of public universities in the United States?  (For instance, what was the role of the presidency of Abraham Lincoln in the birth of American public universities?)
• What are the origins of the for-profit college or university, and how did these institutions cause the purpose of college to mutate over time?
• How has the decline in public and private funding for basic research affected the employment landscape for academics?  (You may not know this, but the United States no longer has any major corporately-funded laboratories dedicated to pure research.  Bell Labs, which was responsible for the discovery of radio astronomy and many other scientific breakthroughs, is now a wholly-owned subsidiary of Nokia, a Finnish corporation.)
• What is the impact of declining numbers of youth and declining college enrollment on universities and colleges?
• How will the defunding of public colleges and universities affect the future of those nations such as the United States which pursue rabidly conservative "free-market" principles?  See, for instance, "Modeling research universities: Predicting probable futures of public vs. private and large vs. small research universities", 2018.
• What can college-educated members of the precariat (especially those college-educated who have been historically marginalized, such as people of color) do both individually and collectively to create a better situation for themselves?  For the present-day contraction of opportunities for the college-educated is being orchestrated by the present masters of our society in an attempt to maintain and amplify existing inequality.  What steps can we therefore take to create our own alternative spaces of collective self-reliance?

I hope to answer these questions (maybe with a little help from some friends) during the next few posts in this series.  I'd like to end with something that's somewhat related to this series of posts and to other posts which I've written for this blog over the last four or five years, namely, another link to a short fiction story which I recently enjoyed.  The name of the story is "Tempus Fugit" and the author is Ketty Steward.