Humans were the network long before software and hardware ever existed. In the human network, each person is a node, of course, and each connection with other people is a link. The links are not just what make us a network, they are what make us human.
You might deem those links as threads. Of course, sometimes they are literally like threads, the wires and the cables that make up our astonishing, often befuddling communication networks.
Other times, the threads are invisible (to us) radio waves and microwaves and what-not. But the most important invisible threads are those forged by love between family members and friends and lovers. Love is invisible but indisputably real.
This multitude of threads make up a much larger, ever-changing tapestry. A tapestry reflecting who we are, collectively, globally. A tapestry woven into the world’s far more expansive ecosystem, one we should be bent on not just preserving but sustaining and growing.
Amid all this, keep in mind you are not alone. We are not alone. Far from it.
Productivity is, or at least should be, the most important factor in American financial well-being. So, it’s a big deal when we suffer dwindling labor productivity. Last year, we saw the second largest annual drop in U.S. labor productivity history. I don’t think the media put a lot of effort into reporting it, but productivity shrank by 1.6%, the largest decline since 1974, when there was a similar plummet of 1.7%.
Is annual productivity going to snap back this year? Maybe. After all, it did back in 1975. But the first quarter of 2023 was not at all heartening, with quarterly productivity shrinking by 2.1%! So, let’s hope for good news when data from the second quarter is published on August 3rd.
What Happens If the Bad News Continues?
If that second quarter news is also bad, we can expect to see a lot of hand-wringing in the U.S., especially on the part of economists and business leaders. The debates about return-to-work and quiet quitting will grow more vociferous, and economists will warn that inflation is going to reemerge if things don’t change. After all, prices go up if it costs more to produce things. In the good times, productivity is what helps keep higher prices at bay.
That’s one reason I think a lot about the subject of productivity. It’s not just another economic metric. It’s a grand indicator of whether or not our whole socioeconomic system is working, both in the physical and the financial sense.
But How About that AI Boost?
Of course, many are now predicting that the new generative AIs will soon result in massive increases in productivity. But that’s not a given. For one thing, it often takes workplaces a long time to figure out how to adequately harness new technologies. This happened with everything from electricity to personal computers.
Maybe it’ll be be different this time around. People like Ray Kurzweil argue that AI will speed up the whole process of change. It’s all a matter of exponential rates of increasing returns.
Others are more dubious. Ezra Klein, for example, points out that the Internet should have resulted in a much larger boost in productivity than it did. But what wasn’t accounted for is that the Internet came with a very large dose of diversion. Suddenly people’s computers became distraction machines, and productivity was diluted as a result.
Klein thinks that this could happen with AI. For example, we may end up in deep conversations with our AI companions even as we fall behind on our work. Or, artificial intelligence will become such a major factor in everything from diverting movies to video games to virtual worlds that we will become more distracted than at any other time in history.
Time will, of course, tell. Personally, I make no predictions, but I can imagine several different scenarios. Maybe those will be a subject for a future post.
According to research I’ve helped conduct in the past, HR professionals tend to think that AI will be more widely used for the automation rather than the enhancement of work. But, I think that’s the wrong way to view it. For the most part, these AIs can’t actually take over many jobs. Rather, they help people be more productive at those jobs. So, generative AI is better for augmentation than automation.
Jobs Could Be Lost
This does not mean, however, that jobs can’t be lost. If you can triple the productivity of a nine-person team, for example, then you could potentially lose six of those people and maintain the same production as before. So, yes, jobs could potentially be lost.
On the other hand, it very much depends on the job and how it’s managed. Let’s say that we’re talking about software developers. In a firm that sells software products, the sticking point in the past may have simply been the cost of labor.
But Let’s Be Specific
Let’s assume a team of nine developers creates and maintains a product that brings in $3 million dollars of revenue per year, and let’s assume that the cost of employing this team is $1.5 million per year. Let’s also assume some form of generative AI can triple productivity so that the team can be reduced to just three people. So, yes, the company could save $1 million dollars per year by terminating six of those positions.
Leverage the Wealth-Creation Machine
Or the company could earn many times that amount by keeping them and assigning them to other revenue-earning projects.
Let’s now assume those six developers can be reallocated to create and implement two other products, both of which also can bring in $3 million per year. At this stage, the revenue earned by these six employees will be $6 million dollars, or $1 million per employee.
This is, of course, how productivity works. It’s a system with positive feedback loops that, if harnessed correctly, becomes a wealth-creation machine.
Oh, I know my arithmetic is over-simplified. Salaries, revenues and profits are never that straightforward. But you get the idea. Depending on the job and the business model, generative AI could actually increase the demand for certain skills because it can massively boost productivity, which boosts revenues and profits.
This Could Change, Of Course
Of course, this could change if generative AI (or whatever AI comes next) can fully automate most white-collar work, but we’re not there yet and, from what I can see, we’re not that close. These AIs are still prone to hallucinations and mistakes, and they require trained professionals to be able to detect those mistakes as well as engage in more creative and strategic work.
So, my advice for now is to leverage these technologies for augmentation rather than automation. Get while the getting’s good. Ultimately, that’s how economies and labor markets thrive.
In theory, we all know that humanity is part of the Earth’s ecosystem. When we impact the system, we impact ourselves. But knowing is one thing, feeling it is another. Sure, we know the global system of which we are part is ever hotter. But lately a lot of Americans, including myself, have gotten a real feel for it.
Over the last several days, the earth has suffered the hottest days in recorded history. On July 3rd, we set a record of 17.01°C, or 62.62°F. That was calculated by taking into account the average temperatures of the land, the oceans, the poles, and the night and day cycles.
But the record didn’t last. On July 4th, there was a new record, 17.18°C this time.
And then on July 6th, yet another record: this time 17.23°C.
What makes this all more remarkable, and more alarming, is that Antarctica is in the heart of its winter season. It should be helping to keep things cooler. Well, to be fair, it probably is. But it’s not enough to overcome the stress that we’re putting on the system.
My Very Hot Home
Coincidentally, our central air conditioning pooped out on July 1st. It was the start of a weekend and, when we called the air conditioner repair people, they gave us a number that was only for “emergencies.” What is an emergency, however? They didn’t define the term.
I thought maybe emergencies were for when there’s a bedridden elderly person in an AC-less house. So, we didn’t call. Saturday night was a bit rough sleeping in a 90°F bedroom, but we managed. On Sunday, I toughed it out at home while C went to work. It reached 91°F in the house with a “feels like” index of 100+ outside. Hot enough that it felt as if the air were closing in, as if I could somehow see the heat itself in a darkened room. And not a “dry heat,” of course. We live in a rainforest (without much rain, lately). Such is Florida.
Sunday night was tougher than Saturday night. The heat was more pervasive. All the objects in the house were hot as well. There was no more residual coolness in the furniture. The bed itself was hot. Thermodynamics, baby.
We called the air conditioning folks on Monday. The woman on the phone half scolded and half laughed. “When your AC goes off on a hot Florida summer day, it’s an emergency no matter who you are,” she said. We didn’t argue. A guy came, replaced a capacitor, and had the AC fixed in about 10 minutes.
Just in time. It was Monday, the hottest day in recorded history (at the time).
To Concentrate the Mind
We are in a system that is getting hotter by the year and, lately, by the day. The most recent record won’t hold, not unless there’s a nuclear war or supervolcano explosion or some other disaster that would be worse than the global warming itself.
It takes a lot for us humans to give up our self-centered foolishness, to stop our inane but often deadly chimp-like bickering among ourselves. It takes a lot to pull us together into a single human tribe. A deadly pandemic certainly couldn’t do it. Indeed, in the U.S., it only intensified our hominid nescience.
But if we could bring all of humanity together into an AC-less Florida amid high humidity and feels-like temperatures of 107°F and keep everyone here until we collectively figured out how to properly address global warming, maybe we’d finally get ‘er done. No more excuses or half measures or procrastination.
Maybe we would finally become avid and careful systems thinkers. Our minds would be concentrated as our bodies sweltered. We would realize that there’s no easy answer to solving the issue of global warming. It’s a system, after all. But we’d soon come up with compromises on a solution that would require sacrifice from everyone, a solution that would please no one but would stand the best chance of getting something real done.
At least, that’s the pipedream. The fevered dream of a hot man lying on a hot sofa under a blurred fan blowing hot air. A man who knows with a palpable certainty that it could be even worse. No, that it will be even worse. And that it is already worse for millions if not billions of people living with far few cooling resources than we have.
The AC is back on for now and for us.
But the memory of just a couple of days without AC will live on a while. It’s just a prelude. And a reminder that when you punch the planet, the planet punches back.
The other day, I spoke in front of a considerable crowd of HR professionals in LA. I asked how many had used ChatGPT and a couple of dozen or so raised their hands. Then I asked how many had used the Bing chatbot, and hardly any hands went up. I imagine this is typical across the profession, which is why I wanted to share a few thoughts about which AIs HR professionals should use.
Of course, there’s no one answer to this question. People should use the bot they like most. But, as a person who does a lot of HR-related research, I’ve formed some opinions.
There Are Two General Type of HR Questions
Generally speaking, there are two types of HR queries. The first type is about ideas and practices. The second is about factual information that often requires specific data as part of the response.
Type One Questions
When it comes to the first type of questions, all three of the most accessible AIs – that is, ChatGPT, Bing and Bard – are similar in performance. Here’s on such question I asked: “”I want to reduce the turnover the voluntary turnover rates of our developers. How do I accomplish that?”
All three of them offers tips such as offer competitive compensation, developmental opportunities, positive work environments, etc. All of the advice seemed fine if a bit bland. All three are, I think, a good place to start for an HR professional just beginning to think though some particular issue.
Type Two Questions
When it comes to the second type of question, however, there are some sizeable differences. Here’s what I asked as a demonstration: “I have a fast-food chain of restaurants in Tampa. What is the expected turnover rate for my employees?”
ChatGPT’s Response
The expected turnover rate for employees in the fast-food industry can vary depending on various factors such as location, industry norms, employee demographics, and specific organizational practices. While I don’t have access to real-time data or specific information about your fast-food chain, I can provide you with a general idea of turnover rates in the industry.
In the fast-food industry, turnover rates tend to be relatively high compared to other sectors due to factors like low wages, demanding work conditions, and limited career advancement opportunities. According to data from the Bureau of Labor Statistics (BLS) in the United States, the accommodation and food services industry, which includes fast-food establishments, had a voluntary turnover rate of around 81.9% in 2020.
However, it’s important to note that turnover rates can vary significantly based on factors like geographic location, employee engagement practices, and company-specific efforts to improve retention. Some fast-food chains have implemented effective strategies to reduce turnover rates, such as offering competitive wages, providing employee benefits, implementing career development programs, and fostering a positive work environment.
To determine the expected turnover rate for your specific fast-food chain in Tampa, it would be best to analyze your historical turnover data and compare it with industry benchmarks. Additionally, conducting employee surveys and exit interviews can provide valuable insights into the reasons for turnover within your organization, allowing you to identify areas for improvement and develop targeted retention strategies.
Bard’s Response
The expected turnover rate for fast food employees in Tampa is high. According to a study by HourWork, only 54% of QSR employees (quick-service restaurants) reached 90 days of working before quitting in 2022. Prior to the pandemic, the segment’s 90-day retention rate hovered at 58%.
There are a number of factors that contribute to the high turnover rate in the fast food industry, including:
Low wages: Fast food workers are often paid minimum wage or close to it, which can make it difficult to make ends meet.
Unpredictable schedules: Fast food workers often have to work unpredictable schedules, which can make it difficult to balance work and personal life.
High-pressure work environment: Fast food workers are often under a lot of pressure to meet customer demands, which can lead to stress and burnout.
Lack of opportunities for advancement: Fast food workers often have few opportunities for advancement, which can make them feel like they are not being rewarded for their hard work.
If you want to reduce the turnover rate at your fast food chain, you can consider:
Offering higher wages: This is one of the most effective ways to attract and retain employees.
Offering flexible schedules: This can help employees balance work and personal life.
Creating a positive work environment: This can help reduce stress and burnout.
Providing opportunities for advancement: This can help employees feel valued and motivated.
By taking steps to address the factors that contribute to high turnover, you can create a more attractive workplace for your employees and reduce the turnover rate at your fast food chain.
As a follow up question, I asked, “What is the source of this information?” It responded as follows:
The information I provided about the expected turnover rate for fast food employees in Tampa is from a study by HourWork. The study was conducted in 2022 and surveyed employees at over 8,000 quick-service restaurants (QSRs) in the United States. The study found that the average turnover rate for QSR employees was 61%, but the turnover rate for employees in Tampa was slightly higher at 63%.
The study also found that the turnover rate for QSR employees was higher for younger workers, with employees under the age of 25 having a turnover rate of 70%. The turnover rate was also higher for part-time employees, with part-time employees having a turnover rate of 65%.
The study’s findings suggest that the fast food industry in Tampa is facing a high turnover rate. This is likely due to a number of factors, including low wages, unpredictable schedules, and a high-pressure work environment. If you are a fast food employer in Tampa, you may want to consider taking steps to address these factors in order to reduce your turnover rate.
So, depending on how you run your fast-food chain of restaurants in Tampa, you can expect a different turnover rate for your employees. If you follow the industry average, you might lose more than 100% of your workers every year. But if you follow the example of Pal’s Sudden Service, you might be able to retain more of your staff and reduce your turnover costs. I hope this information helps you. 😊
Which Performed Best?
ChatGPT did about as well as it could given that it has no access to data after September 2021. At least it cited the BLS, which is pretty much the gold standard for workforce-related data.
Bard arguably did better, citing a specific study from an article as recent as January 2023. Of course, I had to specifically ask for the source and there was only the one article.
Bing did the best. It cited multiple sources and provided links to those sources. It also cited an appropriate best practice article from a good, if somewhat dated, source: the Harvard Business Review.
It’s not that Bing is perfect. Sometimes the links it provides to articles don’t actually contain the information that has supposedly been cited. But it remains the best of the three for now and so, based on my informal poll, seems underutilized among HR professionals.
Why? Probably because Google’s Chrome still has a virtual monopoly on the browser market and search space. Using the Bing chatbot on a desktop requires the use of the Microsoft Edge browser, for now, and it probably just seems like a hassle for most HR professionals to download and use it, especially if much of their computing lives are spent in the Google browser/email/applications ecosystem.
I get that. I work for a “Google shop” and much of my work and personal life exists on Google.
On the other hand, I don’t find it too complicated to keep two different browsers open on my laptop, and using the Bing chatbot on my phone is easy.
So, if you’re an HR professional who conducts online research and wants to use a chatbot to locate verified sources, then I recommend bringing Bing into your rotation of AI tools. Bard may well catch up. It’s shown a lot of improvement over time. But Bing wins for now.
In the U.S. we are about to celebrate Independence Day. Although it’s intended to commemorate our sovereignty as a nation, independence is also a good description of each one of us Americans, trained as we are to think of ourselves as individuals. To the extent we really think about others, it’s most often our immediate family members. Seldom do we conceive of ourselves as a part of a humanity that exists as a superorganism.
Enlightenment at the LAX
But when I travel, the reality of what I am—of what all of us are, I think—becomes clearer. A few days ago, for example, I flew out of LAX (aka, Los Angeles International Airport), one of the busiest airports in the world. Not only is LAX a virtual beehive of human activity, complete with the constant drone of aircraft coming and going, but there’s an amazing amount construction being done right now. This heightens the aura of hubbub and constant change one experiences there.
Amid this sometimes baffling swath of semi-controlled chaos, it’s impossible to feel like an independent self-contained universe. Rather, it becomes clear that we are, for all our sense of selfhood, individual parts of an immense network, components in a dynamic system.
Our technologies are part of this network, of course. We individual human beings buzz like bees, engaged in our countless versions of waggle dances in order to communicate with one another. Each of us has our special areas of knowledge: not just our work specialties but our unique knowledge of our families, friends, passions, property and community. Whether via our electronic technologies (like the Internet) or just our backyard chats with friends and family (language, after all, is the most important and powerful human technology of them all), we are in a continuous process of information analysis, distribution and reception.
What Is the Human Superorganism?
But what is the superorganism of which we are a part? We can call it the “human race,” of course, but that doesn’t explain much. We could also label it as a technosphere, technium, anthroposphere or infophere, but I’m not sure how helpful that is, either.
The terminology is less important than the nature and purpose of the uber-being. After all, our individual biological cells can’t possibly know what we, as the superorganisms of which they are a part, are truly up to. So, it seems possible that we don’t truly know what our superorganism is doing – what its intentions are, if any.
Of course, maybe the Collective doesn’t think at all, being just a complex network of selfish genes expanding in all directions for the sole purpose of doing what genes do: multiply and vie for continuance. In this scenario, humanity is just a big, dumb giant with no more purpose or agency than a moth drawn to light.
Does the Collective Have a Plan?
So, is there some uber-plan beyond the schemings of ultrawealthy types like Musk or Bezos? I don’t know, but I certainly hope so. I hope that there’s something far larger and more intelligent than our unpleasant packs of plutocrats and politicians.
Because from this one cell’s perspective, those folks too often seem like bearers of uncontrolled and meaningless tumult. Greedy. Vain. Ignorant. More like the god Moloch than anything else I can think of.
But maybe that’s how the superorganism known as humanity actually works. The worshippers of Moloch are a part of the system, but so are the worshippers of Gaia, of Reason, of Buddha, of Jehovah and more. Maybe trusting that we’re part of a plan that’s larger than we can grasp is the very definition of faith. It could be where our spiritual impulses come from. In my case, not from a cathedral but from a gargantuan travel hub made up of concrete and steel, plastic and glass, flesh and fuel, ego and money. And also, of course, bytes and bits flowing like charged particles between nodes and subnetworks that, together, may have purposes of which we are only dimly aware.
Perhaps even forming a more perfect union in the end. Yes, that’d be lovely.
In one recent posts in this series, I argued that the latest forms of AI will play a unique role in the history of humanity and technology. Un this one, I want to drill down on that idea by showing how we’ll increasingly treat generative AI as coworkers, collaborators and more.
AI as Flawed Tool
One of the ironies of today’s generative AIs like ChatGPT is that, in many ways, they make for lousy tools in the traditional sense. What you expect from a good tool is consistency, dependability, durability and accuracy. At least for now, the today’s generative AI, especially the large language models, often fail to meet these criteria.
As I said in my last post, “If we held these AIs to the same standards as the literal tools in our toolboxes, we’d probably toss them. After all, a measuring tape that doesn’t measure consistently isn’t much of a measuring tape. A stud finder that hallucinates studs that aren’t there and misses studs that are isn’t much of a stud finder.”
Let’s get into some of the problems.
Inconsistent
Top Five HR Functions
If you ask the generative AIs the same question multiple times, they may well give you a different answers in different instances. For example, let’s say I ask one of these AIs, “What are the five most important HR functions?”
I asked Bard this question three times. It gave me the same answer the first two times and a different answer the next day. ChatGPT gave me the most consistent responses, while Bing performed more like Bard: giving me two virtual identical answers and later a somewhat different answer.
Generally speaking, though, the five most common answers were:
Recruitment and Selection
Training and Development
Performance Management
Compensation and Benefits
Employee Relations
This is, of course, a subjective question, so who really cares if Bard throws in “outsourcing” and Bing throws in “culture” or “talent management” sometimes? Well, not me, unless I’m trying to create a training module that needs to teach a consistent lesson. I’m not saying that issue can’t be fixed, even with generative AI, but the point is that these AIs have an unpredictability that must be taken into consideration by users and developers.
The Forces of Nature
In contrast, these AIs are much better at consistently providing information that has been well codified, such as scientific information. For example, they will consistently say that there are four forces or nature and identify them correctly. The definitions may be slightly different from response to response, but generally speaking they’ll be the same.
Undependable and Inaccurate
I have experienced AI “confabulations” many times. I’ve seen these AIs make up names of fictional scientists, tell me stories about things that could not have happened, and just get the facts wrong about basic things such as chronological order.
In my last post, I gave a detailed account of AI hallucinations and inaccuracies in regard to the topic of a famous poet. I’ve also experienced AI getting basic mathematics wrong. In fact, as I was write this, I asked ChatGPT to multiply two four-digit numbers. Not only did it give me the wrong answer twice, it gave me two different answers to the same problem!
This is common for these AIs, so when I hear that ChatGPT will soon be responsible for things like bookkeeping, I have to shake my head. The firm that carelessly turns its finances over to generative AI has best be prepared for a visit from the IRS.
That Could Change
Of course, what’s true today may not be true tomorrow. ChatGPT may become flawless at mathematics as its maker, OpenAI, forges alliances with firms such as Wolfram|Alpha. By using plug-ins and APIs, ChatGPT might be able to go from mathematical moron to savant.
Still, my point remains. Without a lot of testing, do not assume the responses coming from one of these AIs are accurate. And, if you’re purchasing an external system, be sure the vendor of the software that utilizes generative AI has a very sound explanation of how the system will be made consistently accurate and dependable.
AI as Intern
So, if these AIs are still pretty shaky as tools, what good are they? Well, that depends. What do you actually want from them?
Let’s say what you really want right now is someone who can give you a hand in a professional field in which you have pretty good skill sets yourself. Basically, you want a bright but green intern who is eager and works with remarkable speed.
In my case, for example, I know a few things about how to write a good survey about HR and work-related issues. So, I want a first draft of a survey on the topic of employment law compliance. Here’s an example of the kind of prompt I could use:
Please write a 10 question survey on the topic of employment law compliance. I’m going to survey HR professionals on this topic.
Then let’s say it gave me 10 questions that are pretty good but, because I’ll need to run a multiple regression analysis, I want some Likert-scale questions. So this time I write:
Now please provide me with five more related questions that use five-point Likert scales
And voilà! I now have a 15-question survey I can use as good draft on which to build. Knowing my job, I can spot problems and make improvements. I am, in the end, grateful for my splendid intern.
AI as Collaborator
Now let’s say I have less use for an intern than for a bright coworker with whom I can collaborate And let’s say I’ve been asked by my Chief HR Officer to look into the issue of pay transparency, which no one in on HR our team has a good handle on yet. So, I turn to a virtual coworker who, while obviously not an expert in employment law compliance, is quite knowledgeable about a wide range of issues.
Collaborating with Bing
So, I ask this virtual coworker (let’s call it Bing in this case) some questions starting with “What can you tell me about pay transparency laws in the US?”
Bing responds as follows:
“Pay transparency laws are regulations that require employers to be more transparent with salary ranges and benefits, and they aim to help promote fairness and equity in the workplace³. Depending on the jurisdiction, employers may be prohibited from asking candidates for their salary history, or they may be required to disclose a wage range for current and/or open positions². Some states and local jurisdictions also have pay data reporting requirements that mandate employers to report pay data by gender, race, ethnicity, and other categories².
“According to the U.S. Department of Labor, pay transparency can help improve pay equity, trust, talent acquisition, and employee engagement¹. However, there is no comprehensive federal pay transparency law in the United States, and the laws at the state and city level vary widely in their scope and enforcement³⁴⁵. You can find more information on federal and state-level equal pay and pay transparency protections for workers on this map¹.”
Then Bing provides me with the following sources that I can use to verify what its saying:
Once I’ve studied the sources, I learn that, although there is no comprehensive federal law in this area, it might make more sense to create a national policy since our company has major operations in California, New York and Illinois, where there are existing or pending laws.
So, Bing and I bat this idea around, listing the pros and cons of a national policy. Ultimately, with Bing’s help, I put together an internal report on the existing laws and the advantages and disadvantages of a national policy. Ultimately, although I’m collaborating with Bing, I’m responsible for the report and must verify every fact Bing provides.
From here, I know this is going to become an organizational decision that involves lots of different stakeholders as well as legal counsel. But my collaborator and I have done a pretty good job, I’d say, of quickly putting together a balanced report that will help the company make the best decisions about a complex issue.
AI as Contractor
Now let’s say I’m part of a small communication team that’s tasked with communicating our company’s guidelines and best practices around hybrid and remote work. The policies are already developed so our job is to make them as accessible, educational and engaging as possible.
One way to do this is via visual elements. Our team brainstorms some possible fun illustrations but there’s not much that matches our vision in the clipart files and there’s no budget to contract with an outside artist on this project.
A member of the team says she might be able to use one of the new AIs to generate the illustrations we have in mind. By the end of the day, she’s shared 40 different images with the team, and we select 6 for the guidelines document.
Someone makes the comment that he wished all their graphic artist contractors worked so quickly and cheaply. This gets a bit of nervous laughter. After all, as writers, we’re well aware that the large language models work a lot cheaper and faster than we do.
AI as Dance Partner
Ultimately, these generative AIs don’t easily fit any pre-existing categories. Technically, they are tools but historically unique ones. Because of this, it often makes more metaphorical sense to view them as playing roles more similar to other human beings, with known strengths and weaknesses.
There’s the role of smart and fast intern who, nonetheless, is prone to making potentially serious mistakes. There’s the role of a eager collaborator who brings many talents and total open-mindedness to the table. You can bat ideas around with this person but, ultimately, you will be responsible for the outcomes of that collaboration. And, of course, there’s the role of contractor with special skill sets.
In all cases, though, there needs to be a growing familiarity with these AIs as they become regular “dance partners” in the workplace. You must get to know their tendencies and cadences, and you are responsible for taking the lead in whichever virtual dance you’re doing. Because, although these tools will certainly be used for automation, they are best at augmenting and complementing people with existing skill sets.
Or, at least, that’s how things stand today. Who knows what tomorrow brings?
Many have stated that artificial intelligence (AI) will change the world. When you ask them how it will, they’ll have hundreds of different answers. Here, however, I’m only going to talk about one way it’ll change the world, the most important way: that is, AI will transform the technium.
The Difference Between the Technium and the Technosphere
As far as I can tell, author Kevin Kelly coined the word technium in his 2010 book What Technology Wants, though perhaps he’d used it before then. He has defined the technium as the “greater, global, massively interconnected system of technology vibrating around us.” It not only includes hardware and software but also culture, art, social institutions, and intellectual creations of all types.
This makes the technium more inclusive than any list of technologies, such as the one cited in the previous post in this series.
I’m not sure why Kelly created technium when the word “technosphere” was readily available. That term was coined by either control engineer John Milsum or by geologist and engineer Peter Haff. Sometimes it’s also called the anthrosphere, a term originally attributed to 19th century geologist Eduard Suess.
Technium and technophere are similar and, I suppose, both are flexible enough to be used in a variety of contexts. Geologist Jan Zalasiewicz writes:
The technosphere…comprises not just our machines, but us humans too, and the professional and social systems by which we interact with technology – factories, schools, universities, trade unions, banks, political parties, the internet. It also includes the domestic animals that we grow in enormous numbers to feed us, the crops that are cultivated to sustain both them and us, and the agricultural soils that are extensively modified from their natural state to carry out this task.
Making the Two Words More Complementary
Given the overlap of the concepts, I’ve been thinking about whether technium is redundant. One interesting way to think about the difference between technosphere and technium came to me via Google’s Bard, which argued that “the technosphere refers to the entire system of human-made objects and structures, while the technium refers to the specific processes and activities involved in creating and using these objects and structures.”
I like that distinction and I suspect Kelly himself might agree with it. After all, he writes that “the technium is a tendency, not an entity. The technium and its constituent technologies are more like a grand process than a grand artifact.”
Bard asserts that “the technosphere is the physical manifestation of the technium.” That is, the technosphere is the built environment and the technium is the humanactivity that creates and sustains it via engineering, manufacturing, maintenance, etc.
I don’t know if this is exactly what Kelly had in mind since he doesn’t go into detail about how the technium differs from the technosphere in his book, but I find it a useful distinction.
AI’s Role in the Technium
The reason I focus on the differences is because I think AI potentially plays an important role here. AI is obviously a growing part of the technosphere, but it’s also starting to play a role in the technium that, up till now, only humanity has played. That is, until this moment in history, human activities have made up “the grand process” that is the technium, but that’s now changing. This marks it as a major shift in the history of technology.
AI-Generated Art
In a rather minor example, I increasingly use generative AI software to create the graphic elements for my posts. For example, they are used to create all the images in the “Illustrated Version of Edgar Allan Poe’s ‘The Raven'” post.
I’m not an illustrator but I was able to use AI to generate a series of images that I thought went fairly well with the poem. It was more of an experiment than anything else but it demonstrated, at least to me, the ability of AI to create a significant portion of the technosphere.
AI-Generated Software
“But a piece of digital artwork is not part of the technosphere,” you might argue. Well, that becomes a matter of semantics, so let’s go with something a little more along the classic lines of built infrastucture: that is, software development.
We know that the new generative AIs are quite good, if not perfect, at generating computer code in a wide array of computer languages. So, let’s say a human being uses this capability to create 90% of the code behind a new app that finds its way onto the Apple store.
Could you argue that that’s not part of the technosphere? I doubt it. But let’s keep going anyway.
AI-Generated Machinery
As I’ve argued before, there’s no reason that generative AI can’t be used to generate thingsmade of atoms rather than just digital objects made of bits and bytes. It’s already a trivial matter, for example, to hook up a generative AI to a 3D printer and create a sculpture or a machine part. This is only going to get easier, with more and more complex machinery being designed by AI and built by forges, 3D printers and other components of the technosphere.
This Key Issue Is Agency Rather Than Capability
So, generative AI is not just part of the technosphere but, increasingly, the technium. That is, it begins to play a role that, up till now, only humanity itself has played. Unless the technology becomes highly regulated very quickly, this role will grow at extraordinary rates.
There will be those who assert that these AIs are only one tool along a continuum that creates the technophere. For example, there are plenty of machines that create other machines, and there is plenty of software that is used to create other digital artifacts. As with other software, these AIs don’t create anything at all until they are prompted to do so.
Maybe so, but I’m arguing that there’s a qualitative difference here. In the creation of my previous post called “A Brief History of Human Technology,” I simply typed the title of the post into Microsoft Bing Image Creator. Otherwise, I gave it no direction at all. It generated two images, both of which I thought were quite good and yet quite different from one another. I used the first of the images in that post and used the second one as the featured image in this post (see above).
Yes, I know that the AI art generators are using existing art on the Internet that got pulled into their training models and that there are ethical issues involved, which I’ve examined elsewhere. Even so, these are still original, if derivative, pieces of art that the algorithm created with minimal guidance from me. This is a different thing than when I use an Adobe application to create triangle or blur a detail. Like it or not, this is creation.
AI and what it produces isn’t just part of the technosphere, it now plays a role similar to that of humanity in the “grand process” and “tendency” that is the technium. (There’s a whole teleological debate here that I’m mostly going to forego for now.)
Similar but Not the Same
Yes, there are still large differences between humanity and these new AIs that have been built via the neural network idea cribbed from our own brains. But I think the primary difference in this context boils down to agency.
In this case, the AI is certainly more capable than I am as an illustrator. What it lacks, at least in this context, is the initial spark of agency to take the action to create the image. But, and I think this is important, this doesn’t mean it lacks any agency. Indeed, all I did was create an initial abstract and inchoate concept, and then it “decided” how to approach the creation of the graphic.
If I’d done the same with a human artist, we certainly wouldn’t say that person lacked agency. Quite the contrary. We’d be amazed at their ability to take such an abstract concept and turn it into a work of art! Sure, I ultimately chose and curated the final product, but that’s something that a customer or patron of the arts always does.
So, no, this isn’t the same as any other technology we’ve ever created. It’s qualitatively different. We now have a partner in the technium dance.
This changes our ontological position in the world. And, more practically, it changes the meaning of human work, which is the topic I’ll cover in my next post in this series.
PS – Note that I asked Kevin Kelly if he’d like to comment on the analysis and he clarified as follows: “I was not aware of the term technosphere when I first blogged. If I had been I might have used it. I define the technium in the same inclusive broad meaning of tangible and intangible.”
Ron likes to brand things, to hear the sizzle of his power singeing then searing the hides of others, scenting the smoke sweetly suffused with the suffering of those daring to defy him.
He especially loves leaving his black mark on wobbly-kneed youngsters before they learn to resist and are lost. Before they seek solace and strength in books or bodies, ideas or selves that disturb Ron. Disgust him, really.
Ron brands them for their own good (and for his, of course), a great circle of lookers-on cheering as Ron lifts his bony knee, releasing the calves that dart away, in pain, afraid, into the corral of Ron’s staunch ranch, his control unquestioned, a strong and mighty man; “Just see how strong,” they whisper.
Ron brands the old ones as well, brands them with an ancient acid, two parts fear, three parts rage, five parts blinding bigotry. Ancient, yes, but still so vividly effective; They all receive, in fact, their branding like a benediction. “This will keep you safe,” he says, “And free.” Though maimed, they cheer, happy to now be captives in Ron’s mighty corral. “Ron loves liberty!” they sing, and Ron winks, thrilled by their bleating, despising their stink.
In the dead of night, though, mighty Ron is fearful and frail within, dreaming of a brimming poisonous puss that threatens to pop off his head as his face turns red, witnessing a nightmare rush of crazed calves grown and vicious, nipping, half-blind sheep busting down Ron’s mighty corral, turning the ranch to splinters, masses yearning to be free, a disastrous stampede.
Ron reels and shouts and brandishes his once red iron, now black as death. Stubbornly, stupidly trampled, he explodes like a lanced boil, spewing a noxious white goo that cascades like slick sleet over Ron’s once staunch ranch, to forge an infected wasteland, a lasting legacy of mighty Ron, strong Ron.
Featured image: Colorado. Branding calves, a photochrom print by the Detroit Photographic Co.
Does more labor productivity raise people’s living standards? The conventional wisdom is still “What’re you kidding? Of course it does!” But the evidence on that is pretty sketchy and has been for a while now. So, let’s do a little myth-busting as we explore the so-called productivity-pay gap.
Investopedia nicely sums up the standard line on productivity: “The level of productivity is the most fundamental and important factor determining the standard of living. Raising it allows people to get what they want faster or get more in the same amount of time. Supply rises with productivity, which decreases real prices and increases real wages.”
Just one little problem, of course. The data indicates it’s not true, at least not in the ways it has usually been explained.
We’re A Lot More Productive, But Not Much Richer
In the U.S., productivity has been going up for many years. In fact, it rose a little faster between 2019 and 2022 than it did the previous 12 years. Have a look at this data from the Bureau of Labor Statistics (BLS):
Productivity Change in the Nonfarm Business Sector, 1947-2022
Productivity hasn’t grown as quickly over the last 15 years as it had the previous 17. But from 2019 to 2022, it was still growing at a similar rate as it did from 1973 to 1990. Overall, despite an occasional dip here and there, there’s been steady growth.
So, if it’s true that “productivity is the most fundamental and important factor determining the standard of living,” then surely our standard of living also doubled in that same time period, right?
The Productivity-Pay Gap
Well, no, not by a long shot. But the answer requires more nuance than that. After all, there’s no clear definition of “standard of living” and productivity itself comes in various flavors. Let’s stick with labor productivity, which compares growth in output to the growth in hours worked, and let’s use inflation-controlled compensation as a more measurable version of standard of living.
The idea, of course, is that productivity and compensation rose pretty much in parallel up until the early 1980s and then split off from one another. In fact, productivity rose 3.7 times faster!
Which suggests that something’s wrong with the whole conceit and with the fact that so many trusted sources keep claiming they rise in virtual tandem despite solid evidence to the contrary.
How Do We Explain What Happened?
So, how can we explain the productivity-pay gap? There are various theories, but here are three that, while not necessarily contradictory, stress different facets of the gap.
Theory 1: Policymakers Tore Out the Coupling
The EPI itself, which has a somewhat left-leaning orientation, explains it like this: “Starting in the late 1970s policymakers began dismantling all the policy bulwarks helping to ensure that typical workers’ wages grew with productivity. Excess unemployment was tolerated to keep any chance of inflation in check. Raises in the federal minimum wage became smaller and rarer. Labor law failed to keep pace with growing employer hostility toward unions. Tax rates on top incomes were lowered. And anti-worker deregulatory pushes—from the deregulation of the trucking and airline industries to the retreat of anti-trust policy to the dismantling of financial regulations and more—succeeded again and again.”
In other words, the government allowed the system to get misaligned. Let’s use the metaphor of a coupling. In machinery, a coupling is a device for joining two rotating shafts at their ends so as to transmit torque from one to the other. The goal, of course, is to transmit power fairly evenly. In the coupling of productivity and compensation, however, things fell badly out of whack. One shaft kept spinning like a champ while the other started moving in slow-mo. If the economy were a machine, we’d send it to the shop.
Theory 2: We’re Not Measuring It Right
Another theory is that the pay-compensation gap is real but maybe not quite as large as the consumer-price-indexed compensation rates suggest. The BLS provides the following chart.
In this graph, the bottom dotted line is compensation adjusted using the consumer price index, but the light blue line above that is compensation that’s adjusted using something called the output price index, which is arguably more accurate. The authors of the article “Understanding the labor productivity and compensation gap” explain:
Workers are compensated based on the value of goods and services produced, not on what they consume. Using an output price deflator, a measure of changes in prices for producers, instead of the CPI is an alternative that better aligns what is produced to the compensation that workers receive. Each industry has its own unique output deflator that matches the goods and services that are produced in that industry.
By using these “deflators” for a variety of industries, they find that the size of the productivity-compensation gap “decreased in 87% of industries that previously showed productivity rising faster than compensation.”
To be clear, the gap isn’t going away if you use this technique, but it does typically shrink in most industries.
Theory 3: The Rich Got Most of the Pay Raise
The third and, to me, most convincing theory is that average folk had their productivity lunch eaten by their better off brethren.
This is clear when you look at the work by economists such as Erik Brynjolfsson and Andrew McAfee of MIT. In their book Race Against the Machine, they comment on a graph that shows the amazing and growing disparity between real median household income and real GDP per capita (which is one measure of productivity). Below is a more up-to-date version of the one they point to in their book:
They call it “striking” and then make this observation:
There have been trillions of dollars of wealth created in recent decades, but most of it went to a relatively small share of the population. In fact, economist Ed Wolff found that over 100% of all the wealth increase in America between 1983 and 2009 accrued to the top 20% of households. The other four-fifths of the population saw a net decrease in wealth over nearly 30 years.
Ouch. So, yes, the productivity paychecks are real. And they do raise the standard of living — but not for everybody. Or even most people.
Were Gains by the Rich Earned or Stolen?
Of course, this raises another question: “Did those folks at the top earn that paycheck, or steal it?”
If that’s incendiary phrasing, don’t blame me. Blame the purveyors of conventional wisdom mentioned above. The implication has always been that we all benefit from productivity increases, but, in practice, as Brynjolfsson and McAfee say, “There is no economic law that says that everyone, or even most people, automatically benefit from technological progress.”
Maybe that makes sense? Let’s say a bunch of tycoon types invest in robotics to boost the productivity of the average worker on the line of some manufacturing plant. After the inevitable layoffs of many workers, do the rest of those surviving employees divvy up the compensation of the people who were laid off minus the cost of the machines?
Probably not. Instead, the benefits accrue to the investors and the senior managers (especially CEOs) who made the decision to invest in the robots. That is, the rich get considerably richer while the surviving workers only get a modest increase. And the folks who were laid off? How much of a cut do you think they’re getting?
Yeah, bubkis. Or, in many cases, they actually lose economic ground.
Multiply this dynamic many times over the course of decades, and median incomes stay flat while GDP per person (which is an average rather than a median) goes up.
So, to answer our question, “They earned it, kind of, sort of, in a way, if you squint hard enough and quash any human instinct for justice and fairness.”
But at least we now have a clue about where benefits of the productivity increases go. That is the beginning of wisdom — and a fine antidote to fiscal fairy tales.
Productivity Chickens Coming Home to Roost
Recently, there has been a decline in U.S. productivity. In fact, some analysts claim that the U.S. has now seen five consecutive quarters of year-over-year declines.
The big question is why. There’s lots of finger-pointing. Some high-profile CEOs blame lazy work-at-home employees for the decline. Others argue, to the contrary, that it is the return-to-work policies that are most strongly linked to productivity declines.
There are plenty of other suspects as well. For example, many people switched jobs during the “great resignation” and so stepped into roles where they had to learn the ropes to become more productive again. Or there’s the rapid return of many employees back into the workforce, a dynamic often associated with temporary reductions of productivity.
There’s also the possibility that higher inflation — combined with pay increases that are insufficient to keep up with it — are simply demoralizing workers. Why should they worker harder for smaller paychecks?
And, of course, there’s the idea that younger generations just aren’t as eager as their older baby boomer counterparts to keep their proverbial noses to the grindstone. It’s less that they’re “lazy” and more that they just aren’t as willing to put up with bossism and toxic workplaces.
CEOs Venting Their Spleen
Meanwhile, CEOs have been venting their spleen about declining productivity, so much so that it feels as if there’s a new “leaked video of a CEO having a meltdown each week,” writes AJ Hess in Fast Company.
On one hand, I get their frustration. Their jobs are, of course, to boost the performance levels of their organizations.
On the other hand, what make these meltdowns both funny and sad is the extraordinary pay gaps between typical employees and their bosses. For example, recent figures indicate that S&P 500 CEOs averaged $18.3 million in compensation in 2021. That’s a whopping 324 times the median worker’s pay!
How did their pay get so exorbitant? Well, one answer is, of course, productivity. That is, they (and other upper-class Americans) have enjoyed the fruits of the productivity bumps of workers whose wages have largely stagnated over the last 40 years.
Which makes you wonder: If the typical worker had been receiving their full share of the benefits of productivity increases since the early 1980s, would we be in a position where “quiet quitting” was even a thing?
Maybe not. What we could be seeing is the productivity chickens come home to roost. If the rich get most of the monetary benefits of productivity increases, then let them do most of the work.
Or, at the very least, they — in partnership with the government — should stop whining and figure out a way to make productivity increases benefit everyone in their organizations, not just the investors and executives at the top.
