Posts Tagged ‘business’
“In a hierarchy, every employee tends to rise to his level of incompetence”*…
As we ponder the impact of the defenestration of seasoned, competent leadership in so many corners of the executive branch (c.f., the Defense Department, the Department of Justice, and… well, sadly, pretty much across the board), an unfortunately timely observation from Mandy Brown (in her lovely newsletter, A Working Library)…
The Peter Principle posits that people are promoted until they reach their level of incompetence. What happens after that point? The implicit assumption is that they stop getting promoted and linger at whatever level they’ve arrived at, and surely all of us can point to mediocre leaders who seem to exhibit exactly that circumstance. But in fact, that situation is not stable but subject to career pressure, as Christian Gläßel and Adam Scharpf define it in Making a Career in Dictatorship. Unable to move up, and very possibly unable to meet expectations, the person who is subject to the Peter Principle is at risk of losing their job, or even their entire career. Which means they might take an even greater risk to save it. Gläßel and Scharpf write:
The logic of career pressure also offers a compelling twist to the Peter Principle. The Principle posits that individuals in hierarchical organizations are promoted until they reach their level of incompetence, where they can no longer perform effectively and cease to advance. In contrast, our logic shows how these individuals may surpass this point and climb the hierarchy even further. Career-pressured individuals often take on high-risk, labor-intensive, or morally contentious tasks. By loyally executing the organization’s dirty work, they can reestablish their value, regain competitiveness, and even outpace peers who were previously ahead of them. Alternatively, they may resort to sabotaging superiors or higher-ups by making false allegations or revealing secrets to create vacancies above them and forcibly advance their careers.
Gläßel and Scharpf, Making a Career in Dictatorship, page 182
In other words, someone promoted up to their level of incompetence, sensing a threat to their own livelihood, becomes a threat to everyone else. I’m reminded here of David Graeber’s theory in Bullshit Jobs, that a great many management jobs are basically feudal retainers—people kept around in order to make the folks above them look good. Very often, making someone else look good may well mean tackling the dirty work for them so that they can keep their hands clean.
The petered leader has in effect been made an imposter—a real imposter, not a “syndromed” or perceived one. They must seem competent, to preserve the appearance that they belong in the role; while in fact they are not capable of delivering on their actual responsibilities and may have been quietly tasked with unrelated services both illegal and unethical. As a result, one of the primary tools of the real imposter is obscurity. Imposters cloak themselves in a fog of obfuscation and busyness, so that the people around them can never say for sure what it is they are doing, or how well (or not) they are doing it.
What we call imposter syndrome was originally termed imposter phenomena, a term that makes plain that it is something that can be observed, not merely felt. The transformation into a syndrome shifted the experience of being impostered by a patriarchal, white supremacist society into a medicalized disorder, something for which the impostered person must be treated. That’s a neat trick: to shift the locus of treatment to the mistreated person, rather than the system that caused her harm. But perhaps there’s even another, more diabolical move here. What if the popularity of imposter syndrome is, in part, a means of obscuring the actual imposters in our midst? What are we missing when we look inward instead of around?
On the dangers of promoting people past their levels of competence at work: “Petered” from @aworkinglibrary.com.
Pair with: “How to Fix a Broken Hierarchy from the Inside.”
(Image above: source)
* Dr. Laurence J. Peter, The Peter Principle (written with Raymond Hull)
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As we fumble with fakers, we might recall that it was on this date in 1874, in Harpers Weekly, that editorial cartoonist Thomas Nast gave the Republican Party its mascot, the elephant. (Contrary to popular belief, Nast did not create Uncle Sam [the male personification of the United States Federal Government], Columbia [the female personification of American values], nor the Democratic donkey, though he did popularize those symbols through his artwork.)
“Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them.”*…
The estimable Tim O’Reilly argues that, while we’re right to be concerned with AI as it’s currently being developed, we needn’t resort to a Butlerian Jihad…
In 2019, the computer scientist Richard Sutton wrote a short essay called “The Bitter Lesson.” Looking back over seventy years of AI research, he concluded that general methods that scale with raw computation beat methods built on human expertise, and by a large margin. The AI industry as a whole is bitter-lesson-pilled, to borrow the internet’s slang for a true believer. I am human-pilled.
I believe in the bitter lesson. Brute force wins out in the end, and I have wrestled with that fact throughout my career. But we humans still have to figure out our role in the AI economy, and I find it rather disturbing that the AI community isn’t spending more time asking how to make this future friendlier for humans. The labs think about how to keep AI from going rogue, and their chief economists study job losses from AI. They don’t study what mechanisms we could introduce to create new economic opportunities for humans.
The question matters to us at O’Reilly Media. We don’t think of ourselves as a publisher, a conference producer or an online learning platform so much as a company that transfers knowledge from the people at the cutting edge to the people who want to follow them. So we are very interested in what human expertise is worth, even in the face of AI.
Human expertise was used to train AI, which makes AI a fabulous cultural technology. Thousands of years ago, the only knowledge people had was what they could keep in their heads. Since then we have built better and better ways to share knowledge across time and space, and AI continues that. With each of these transitions, we have to ask again what the truly human is still worth. People often say judgment, or taste. That’s true, but I think it goes deeper:
AI is a sea of possibility. All of that knowledge sits in the model, and nothing sets it in motion except a human. (That is changing a little as we start giving AI agents long-running tasks, and we will have to come to grips with it.) If you and I used Claude or ChatGPT for the same research project, we would each bring different experience to it, tell it what we want and why, and give it context in different ways. I see that as the unique focusing power of human consciousness and attention.
Earlier this year I spent an hour or so talking this question over with Claude. When it offered to write up a first draft for me, I said no. I wanted an essay from its own point of view about what it took away from our conversation. The result, “Why AI Needs Us”, opens, “He came in through Powys,” and a few paragraphs later says, “The choice of entry point was already the argument.” That captured what I’m getting at. I had come into the conversation through a book I love, John Cowper Powys’s The Meaning of Culture, which distinguishes culture from education. Powys quotes one definition: “Culture is what is left over after you have forgotten all you have definitely set out to learn.” It is what you absorbed. Each of us absorbs a small and particular slice of everything. Large language models (LLMs) try to absorb everything, and so become overly general; we give them focus. Human knowledge provides them with a lens...
[Tim unpacks his thesis, explaining both what he means and how he came to believe it…]
… I think a lot about a concept from economics called mechanism design, which has also been described as reverse game theory. Game theory asks how players will act under a given set of rules. Mechanism design starts from the outcome you want and works backward to rules that will tend to produce it as people pursue their own interests. Al Roth, who shared a Nobel Prize for this kind of work, used it to design a kidney exchange that more patients and donors would join. Economists mostly apply the idea to auctions and similar markets, but I think every new technology requires new mechanisms, and the best ones are not invented by economists. We tend to fall into them without really understanding them.
Think about the economic mechanisms that made earlier technological revolutions work. The web gave us the ability to connect computers, but it took inventions like PageRank to help people find what they were looking for, pay-per-click advertising to connect businesses with customers, and online payments to make transactions easy. Smartphones gave us GPS, but somebody had to invent the matching marketplaces that made Uber and Lyft possible. YouTube’s Content ID turned copyright infringement from a problem of takedowns into an opportunity for creators and rights holders to get paid. These weren’t simply technological advances. They were new arrangements of incentives that made entirely new markets possible.So what are the equivalents for AI? How will agents discover the best expertise and skills, establish their quality, and pay the people who created them? How will they find services without the platform steering them toward whoever pays the most? We don’t know the answers yet. And that’s precisely the point. We’re spending enormous amounts of money making AI more capable, but comparatively little effort figuring out the mechanisms that will turn those capabilities into opportunities for everyone else.
Take open source software. Most people think its mechanism was the free software licenses, but I was around before those licenses, and people were already sharing software. Ken Thompson and Dennis Ritchie created Unix, the predecessor of Linux, at Bell Labs under AT&T’s proprietary license. They designed it to run on many kinds of hardware, and without meaning to, they built what I came to call an architecture of participation…
[Tim explains Model Context Protocol (MCP) and the way that it can bring participation to AI…]
…
With MCP, which lets any model connect to anyone’s tools and data, the emerging web of AI agents began to take on the shape of the participatory economy of open source software and the web. People can bring their own models and infrastructure as an alternative to the big centralized players.
This is another side of grappling with the bitter lesson: what are the missing mechanisms of the human-AI economy? Some of them are standards, because standards and protocols are a big part of what makes an architecture of participation. I have a group working on portable memory, so that what your AI knows about you can move with you from one tool to another.
That story is just beginning. We are in an exploratory phase of mechanism design for AI, and it would be a mistake to settle on answers too early. Somebody is going to figure out the next mechanism, and we’ll all say, “Oh, that’s so damn obvious.”
Mechanism design starts by asking what outcome you want. So what game ought we to be playing in the AI market?
We’ve been told that the game is the quest for superintelligence: a winner-takes-all contest that will bring untold riches to the winners and maybe impoverish, or even kill, everyone else. We’ve seen mechanisms designed to support that game. Massive investments in data center capacity mean that only the well-capitalized can play. There is colonialist-style appropriation of existing intellectual property. Access to models is sold through expensive APIs, and closed models come with terms of service that forbid others from learning from their outputs. Advertising may be creeping into answers, and memory locks your context inside one provider’s walls. Is that the game we ought to be playing?
I think the game we should be playing is human flourishing, and I think the game of human flourishing is creating new affordances for people to create and share with each other. We’re a social species. History teaches us that we will find our way there, but we can get there more quickly or more slowly depending on the choices we make. Getting there takes mechanisms that create a circulatory economy of value exchange between humans and their agents, with humans in partnership with AI rather than replaced by it.
The big labs, whatever the motives of the people who run them, are all driven by the imperatives of our financial system. They have raised enormous sums that they somehow have to pay back, so building a hugely successful business has to be their overriding goal, and that shapes all their incentives. In some ways AI was enshittified from the beginning, because it was built on the backs of so much capital that has to be repaid. That’s why open source AI matters so much, and sovereign AI too, built and controlled within individual countries. The open models, which anyone can download and run, aren’t as capable as the best closed models yet, but they keep getting closer, and they are already good enough for a lot of productive work.
Open source, open protocols, and open memory give users the freedom to shape and control the technology they use. The game I want us to play is one in which all of us build the future together, so that we control this wonderful new wave of technology for ourselves. I’m starting to see the shape of an economy where AI belongs to people rather than to the big labs, where we have personal AI agents working for us and with us, and we build an economy around that. I think it’s going to happen not because of the big labs, but in spite of them…
AI for everyone- the Bitter Lesson meets Architectures of Participation: “Human-Pilled,” from @timoreilly.bsky.social.
Pair with this consideration (from Henry Farrell) of AI’s current “flavor of the month”: “Agentic AI is myth set in motion.”
* Frank Herbert, Dune
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As we open AI, we might recall that it was on this date in 1952 that the National Security Agency– the NSA– was formed by President Harry Truman. Created from a unit that deciphered coded communications in World War II, it had predecessors (with names like “Cipher Bureau,” “Black Chamber,” and “MI-8”) that date back to World War I. Charged with global monitoring, collection, and processing of information and data for intelligence and counterintelligence purposes, specializing in a discipline known as signals intelligence (SIGINT), the NSA is also tasked with the protection of U.S. communications networks and information systems.
By the end of the Cold War, the NSA had became the largest of the U.S. intelligence organizations in terms of personnel and budget. While it appears that CIA may now have surpassed it, the NSA has some 32,000 employees.
The NSA’s actions have been a matter of political controversy on several occasions, including its role in providing intelligence during the Gulf of Tonkin incident, which contributed to the escalation of U.S. involvement in the Vietnam War. Declassified documents later revealed that the NSA misinterpreted or overstated signals intelligence, leading to reports of a second North Vietnamese attack that likely never occurred. The agency has also received scrutiny for spying on anti–Vietnam War leaders and the agency’s participation in economic espionage. In 2013, the NSA had many of its secret surveillance programs revealed to the public by Edward Snowden, a former NSA contractor. According to the leaked documents, the NSA intercepts and stores the communications of over a billion people worldwide, including United States citizens. The documents also revealed that the NSA tracks hundreds of millions of people’s movements using cell phone metadata. Internationally, research has pointed to the NSA’s ability to surveil the domestic Internet traffic of foreign countries through “boomerang routing.”
“What are Americans still buying? Big Macs, Campbell’s soup, Hershey’s chocolate, and Spam– the four food groups of the apocalypse”*…
Rachel Handler weighs in with a deep dive on the enshittification (and here) of a couple of storied brands…
… Everyone in my life I spoke to agreed that something was amiss, that Reese’s — and commercial chocolate in general — didn’t taste the way it used to. Plus everything from Smartfood popcorn to HVAC to mattresses to car-door handles sucked now. Why wouldn’t Reese’s Peanut Butter Cups, whose entire reason for being was to taste good, also suck? And why was it so hard to know how — and why — they tasted worse?
I would spend the next several months tumbling down a chocolate vortex, encountering dozens of eccentric characters, all disagreeing about the fundamental question of what chocolate even is and who it should be for. I entered a terrifying fun house of American enshittification. I learned that — not to sound like a LinkedIn guy — what’s going on with chocolate is a parable for the modern world. That at this particularly bleak end stage of capitalism, everything we once treasured has or will lose its essential essence and MrBeast will always be involved somehow…
Into the molten belly of our crumbling world: “The Chocolate Conspiracy” (archived version), from @rachelhandler.bsky.social in @nymag.com. Eminently worth reading in full.
Via the ever-illuminating Ingrid Burrington (@lifewinning.com) and her wonderful newsletter, Perfect Sentences, the most recent edition of which included these bangers from Handler’s article:
It was a classic American story about men and the things they will do to cheat death (betray their fathers) and how, with enough drugs, you could lose the ability to taste or feel things.
Polling the average Hersheypark consumer had taught me a lot about individual taste preferences within the increasingly deteriorating conditions of the imperial core and about how hot I could get before I started to weep.
As we remember the real, we might note that today is National Chocolate Day.
“Traveling booksellers balanced the need to garner sales with the specter of artful deception”*…
We noted in a post last month the extraordinary market in the 19th century U.S. for subscription books (a direct-sales approach to publishing with a long history); even after subscriptions subsided, their infrastructure– sales teams that contained an appreciable number of women– was used to sell books in locations too remote for bookstores. Livia Gershon reviews the risks and rewards of life on the road as a traveling bookseller…
In the second half of the nineteenth century, before the rise of public libraries, many people in remote parts of the United States got their hands on new books thanks to traveling salespeople. As literature scholar and archivist Jolie Braun writes, drawing on three memoirs from this era, women in that line of work had some distinct advantages but also faced serious hardships, both physical and social.
The booksellers visited homes and businesses with sample books, took orders, and then later delivered the books and collected payment. Braun writes that popular volumes included Bibles, farming and health manuals, biographies, and fiction. Ulysses S. Grant’s memoirs and the works of Mark Twain were popular choices. Many books published using this “subscription” model sold hundreds of thousands of copies.
While the vast majority of book peddlers were men, some publishers recruited women with the idea that they would be well suited to sell books on health and hygiene to female readers, and also that customers would find them sympathetic.
In an 1868 account of her work selling books across northern Illinois and Indiana, Annie Nelles Dumond described taking up the work out of necessity. She wrote that, seeking to escape a difficult childhood, she married young to a man who turned out to already have a wife and children. A second husband abandoned her. So Dumond sought ways to support herself.
When she began selling books, she wore a mourning bonnet. She acknowledged that this was deceptive but wrote that it was “almost necessary to my self-preservation that my past should not be known.”
Dumond described pride in her work’s “dissemination of light and knowledge among the masses.” By contrast, in an 1874 book on her own work as a bookseller, J. W. Likins of central California wrote that, despite knowing it was “honorable and legitimate” employment, “it seemed to me very much like begging.”
Likins, who took the job to support her family after her husband became too ill to work, wrote that some potential customers seemed curious or pitying. But a few men were angry to see a woman out on her own, demanding that she go home to take care of her family or incorrectly assuming that she was a suffragist.
More disturbingly, in a third autobiographical account Braun considers, published in 1881, a northern California peddler named Harriet Wasson Styer described a man telling her that, if she were his sister, he would murder her rather than allow her to canvass.
But much of Styer’s account suggested a fascinating experience. She recounted traveling to lumber camps and mining towns, admiring the wild landscapes, and coping with bed bugs and dirty stagecoaches.
Braun notes that all three peddlers were well aware that they were doing something many people believed was improper for a lady, but they took on the work out of a combination of necessity and a chance for adventure…
“The Women Who Sold Books Door to Door,” from @liviagershon.bsky.social in @jstordaily.bsky.social.
Pair with: “Building a Library in Wartime Paris” (“how librarians brought books to U. S. troops during World War I”)
* John Jude Garcia, American Biography, the History of Books, and the Market for Nationalism, 1800-1855
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As we get a foot in the door, we might send grateful birthday greetings to Herman Melville; he was born on this date in 1819. A writer, he is best known for Moby-Dick (1851), Typee (1846), and his posthumously published (1924) novella Billy Budd, Sailor. The efforts of the booksellers described above notwithstanding, Melville was not well known to the public at the time of his death in 1891. But the centennial of his birth was the starting point of a Melville revival; Moby-Dick is now, of course, considered one of the Great American Novels.
“And what is good, Phaedrus, and what is not good”*…
Keyana Sapp maintains The Brand Ledger…
Tracking the brands that got worse on purpose — and the ones that didn’t.
The Brand Ledger tracks 397 brands, from the tools in your garage to the pans in your kitchen. Who owns them, what they used to be, whether they’re still worth buying. Updated as things change…
On his blog, Worse on Purpose, he considers what quality is and how it is degraded. He begins with the story of his (now 15-year-old) Gibson Les Paul Studio guitar (a la the one pictured above), an exemplar, for Sapp, of “quality”…
… Everyone owns something like this: the old pair of boots, the battered wrenches, grandma’s cast iron pan, the 400,000 mile truck that will not die, etc…
Objects that wear in instead of wearing out. The ones whose quality you can feel the moment you pick them up.
Now try to explain what it is you feel.
Some quality lives in the guitar, the boots and the pan. You are able to detect it intuitively within seconds. But try to put to words precisely what it is you detect and you’ll discover a dilemma.
Your first instinct is to point at the materials. It’s mahogany. It has real humbuckers, a set neck, the best nitro lacquer. But that answer collapses the moment you notice the description fits every guitar on that wall. Two instruments leave the same factory in the same month, matching spec for spec down the line, and one sings while the other sounds weak. Every guitarist knows this, which is why nobody buys the model, they buy the individual guitar, only after noodling on it for a while. The spec sheet names everything the guitar is made of, and whatever quality is, it is not on the list of materials.
Fall back on “I just like it” and you sell the knowledge short, because standing in that store I wasn’t just voicing an arbitrary preference, I was detecting something. “This is a thing of quality. It is the one I want.” Fifteen years of ownership keep proving my instinct that day right.
That gap, between knowing good on contact and being able to say what good is, is one of the oldest open problems in Western philosophy. It is also the fundamental question this newsletter is attempting to answer.
Every investigation I’ve published documents the same story: a product stripped of what made it good while everything a shopper can check stayed intact. Same logo, same spec sheet, same four and a half stars, same price or higher. What got swapped out, the steel gauge, the stitch count and the years of service life, sits in the parts you can’t check from the aisle.
That gap between what you can verify and what actually matters is the whole game, for them and for you. They use it to slowly diminish the quality of the products you once loved without tripping an alarm. You can use it to watch the theft happen…
… In the late 1950s, a rhetoric instructor at Montana State College named Robert Pirsig noticed that his contract required him to teach “quality”. He asked around the faculty at the university and discovered that nobody could tell him what the word meant.
Teachers had been passing and failing students on the basis of quality for centuries without a definition. The problem ate at him for fifteen years, ultimately driving him to insanity. Pirsig’s best thinking on the nature of quality was eventually set down in Zen and the Art of Motorcycle Maintenance. It stands alone as the most formative book I have ever read.
To illustrate the problem, Pirsig relays an experiment he ran with his students. He read four student papers aloud and had everyone rank them by quality on slips of paper. He ranked them himself, collected the slips, tallied the results on the blackboard, and set his own ranking next to the class average. His ranking and the students’ matched almost every time, across classes and semesters. A room full of undergraduates who could not define quality independently agreed on where it lived and where it didn’t.
So Pirsig landed on this problem statement:
“Quality is a characteristic of thought and statement that is recognized by a nonthinking process. Because definitions are a product of rigid, formal thinking, quality cannot be defined.”
Then he added the sentence that should be nailed above the door of every product team in America.
“Even though Quality cannot be defined, you know what Quality is.”
When colleagues demanded proof that an undefinable thing existed at all, he offered this subtraction:
Pull quality out of the world and street noise ranks with symphonies, slop ranks with dinner, and no made thing is worth choosing over any other. A world without quality would still function. You just wouldn’t want to live in it.
Similar threads have appeared across disciplines.
The architect Christopher Alexander encountered the same problem from a different angle. A career spent asking why some buildings feel alive and others feel dead ended in the same non-definition, a quality that in his words “is objective and precise, but cannot be named.”
What Alexander did about it is the useful part. If the thing itself could not be written down, the places where it reliably appears could be, so he and his collaborators catalogued 253 of them, pulled from centuries of buildings people love, and handed architects and builders a working method for producing quality in buildings and towns.
That is what every craft tradition is: a transmission system for the unnameable, carried in people rather than paper. It is why apprenticeship survives every technology invented to replace it: the judgment that produces quality transfers only by demonstration and correction, through thousands of supervised repetitions across years, from a person who has it to a person who does not yet.
That fact cuts both ways. The judgment that can only be carried in people is also the one thing no spreadsheet can measure.
Whatever resists definition resists measurement. Whatever resists measurement vanishes from the dashboard. And in a company run from dashboards, what vanishes from the dashboard vanishes altogether.
That blindness is not an accident of modern business. It was designed, it has an inventor, and the tragedy is that it was invented to do the opposite job. During World War II, a General Electric engineer named Lawrence Miles was tasked with scaling turbo-supercharger production for B-24 bombers from 50 a week to 1,000 while steel, copper and nickel were rationed to hell. He hunted substitute materials, and noticed the surprising fact that substitutes often made the part cheaper and better at the same time. In 1947 he formalized the method and called it value analysis. Identify the function a part serves, then find the best possible way to serve that function. Function first, cost second. Through this process, Miles built a machine for producing quality cheaper.
The method worked so well it quickly became universal. The Navy adopted it in the 1950s and renamed it value engineering. The Pentagon eventually wrote it into federal procurement rules. Miles’s 1961 handbook was translated into a dozen languages, and within a generation nearly every large manufacturer ran a version of the program. Continuous, itemized, never-ending review of everything a product is made of, scored in dollars saved. That framework is now as ordinary as accounting.
Unfortunately, his descendants now run the machine backward. The modern cost-down program starts from the spec sheet and asks what can be removed without a statistically significant change in buyer perception this quarter. The thinner steel passes the test. The glued joint passes. The plastic gear where the brass one was, the foam that loses a third of its resilience in two years etc…
Each change is approved in isolation, and each is too small for any buyer to notice on its own. That is the trick. No test compares the product to what it was ten years ago. Each product version is measured against last quarter’s, comes back as “no detectable difference,” and ships. So the degradation compounds beneath the threshold of every individual measurement, invisible at each step and enormous in total.
It is by this mechanism that quality, the unmeasurable property, erodes as a company places a greater insistence on measurement.
When we ask what quality actually is, only two answers exist. Either quality is objective, meaning a physical property located in the object itself. Or quality is subjective, meaning an opinion located in the person judging.
Take the first answer seriously. If quality is a physical property of the object, then instruments should detect it. We can measure a guitar’s weight, its neck relief, its fret height, its finish thickness down to the micron. No instrument has ever measured whether it is good. If quality sat inside the object the way mass sits inside the object, quality control would be a solved engineering problem and a factory could certify goodness the same way it certifies tolerances.
Now take the second answer seriously. If quality is only an opinion, then a quality judgment reports a fact about the judge and no fact about the object. Two things follow. First, quality judgments should distribute evenly, because nothing in the object itself would constrain them. Second, no quality judgment could ever be right or wrong, because there would be nothing objective in the world for it to be right about.
Ultimately, both answers fail.
Pirsig’s blackboard example demonstrates the failure of the first: his classes converged on the same rankings, semester after semester, with no criteria handed to them. They agreed independently on some notion of quality, recognized it, but could not explain it.
The second fails on an experience everyone reading this has had: being wrong about quality. Nobody has ever been wrong about liking vanilla, because a preference claims nothing factual about the world. A quality judgment claims plenty. When I decided that Les Paul was good, I was predicting that the neck would stay straight, the frets would survive the abuse, and the guitar would still be worth reaching for in fifteen years. Every one of those predictions could have failed, and with other guitars, for other players, they have. Boots that looked right have come apart in one winter, and every buyer of a bad pair has said the sentence that pure subjectivism cannot explain: “I was wrong about those boots.”
So if quality is not an objective property, since no instruments can detect it, and it is not just a subjective opinion because we are able to make predictions about the quality of an object that are verified in time, then what is it?
Pirsig’s answer was that the question itself smuggles in the false assumption that quality must be located in one place or the other. He argued instead that quality is a feature of the relationship between the person and the object. Quality occurs when a person and a thing meet in use: the weight settling onto the shoulder, the wrench loaded to its limit and holding. Before they meet there is only a guitar and a player. Quality exists in the connection.
Apply this idea in the realm of consumer products, and the slow decline of quality starts making sense.
Every measurement a company takes lands on one side of the objective/subjective divide or the other. Spec sheets, tolerances, and materials testing measure the object alone. Surveys, star ratings, and focus groups measure opinions alone. And they sample the opinion at the wrong moment. A star rating gets filed in the first week of ownership, while the surface still shines, and the failures arrive in year three, when almost nobody returns to amend it.
Nothing measures the relationship, because the relationship only exists in use, in the hand, on the road and across years. So when quality drains out of a product, it drains from the one place no instrument points at. Both sets of numbers can hold perfectly steady while the thing between them disappears. That is how a product gets worse without a single metric moving. That is also how the people doing it stay convinced that nothing was lost.
What produced quality in the first place was care. Pirsig again: “Care and Quality are internal and external aspects of the same thing. A person who sees Quality and feels it as he works is a person who cares.”
A good object is a fossil record of care. Thousands of small selections made by people who could tell the good facts from the bad ones and picked the good, even when not doing so was cheaper or easier. The extra ounce of brass, the second coat of lacquer and the tolerance held a hair tighter than the drawing demanded. Every one of those selections survives for exactly one reason, which is that somebody with power over the object gave a shit.
That is why extraction works the way it does. When outside capital buys a great brand, nobody issues a decree that the product shall now be garbage. Instead, the people who previously made those thousands of selections either get laid off, retired out, or reorged into irrelevance, and the decisions migrate to a floor in an office building where nobody has ever actually used the product. Care cannot be exercised at that distance. Quality follows care out of the building, and the whole thing happens without any individual ever choosing badness directly. Absence does the job on its own…
…
… There are two ways to make money on quality. You can make a thing so good that people pay for it, keep it, and hand your name to their kids. That is the Miles road: quality found cheaper, profit as the receipt for care. Or you can buy the name after the caring is done, spend down four generations of accumulated trust, and be gone before anyone’s memory catches up…
Eminently worth reading in full. An ode to Pirsig: “On Quality,” from @worseonpurpose.bsky.social.
(Image above: source)
* Plato’s Phaedrus (which means, as Sapp observes, that the question of quality is at least twenty-four centuries old)
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As we interrogate enshittification, we might recall that it was on this date in 1908 that Buick Motor Company head William Crapo Durant incorporated General Motors in New Jersey…
Durant, a high-school dropout, had made his fortune building horse-drawn carriages, and in fact he hated cars–he thought they were noisy, smelly, and dangerous. Nevertheless, the giant company he built would dominate the American auto industry for decades.
In the first years of the 20th century, however, that industry was a mess. There were about 45 different car companies in the United States, most of which sold only a handful of cars each year (and many of which had an unpleasant tendency to take customers’ down payments and then go out of business before delivering a completed automobile). Industrialist Benjamin Briscoe called this way of doing business “manufacturing gambling,” and he proposed a better idea. To build consumer confidence and drive the weakest car companies out of business, he wanted to consolidate the largest and most reliable manufacturers (Ford, REO, his own Maxwell-Briscoe, and Durant’s Buick) into one big company. This idea appealed to Durant (though not to Henry Ford or REO’s Ransom E. Olds), who had made his millions in the carriage business just that way: Instead of selling one kind of vehicle to one kind of customer, Durant’s company had sold carriages and carts of all kinds, from the utilitarian to the luxurious.
But Briscoe wanted to merge all the companies completely into one, while Durant wanted to build a holding company that would leave its individual parts more or less alone. (“Durant is for states’ rights,” Briscoe said. “I am for a union.”) Durant got his way, and the new GM was the opposite of Ford: Instead of just making one car, like the Model T, it produced a wide variety of cars for a wide variety of buyers. In its first two years, GM cobbled together 30 companies, including 11 automakers like Oldsmobile, Cadillac, and Oakland (which later became Pontiac), some supplier firms, and even an electric company.
Buying all these companies was too expensive for the fledgling GM, and in 1911 the corporation’s board forced the spendthrift Durant to quit. He started a new car company with the Chevrolet brothers and was able to buy enough GM stock to regain control of the corporation in 1916, but his profligate ways got the better of him and he was forced out again in 1920. During the Depression, Durant went bankrupt, and he spent his last years managing a bowling alley in Flint.
– source
In the early 1900s public outcry over weak government regulation of gasoline-powered horseless carriages was significant. Durant clocked this public anger, and rather than relying on government regulations to improve their safety, he saw it as an opportunity to create a company which could improve the quality and safety of this new class of transportation. Fast forward just over a century and General Motors seems to have fallen prey to the extractive impulse that Sapp describes: the company and its cars are beset by myriad quality issues.
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