“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)
###
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.
“Every happiness is the child of a separation it did not think it could survive”*…
A third and final (for awhile:) post on AI and what it might mean… Sunday’s post was a consideration of what we might lose if AI becomes central to our lives, a modern echo of Socrates’ lament on the spread of writing. Indeed, we are in a moment of high dudgeon over AI: its potential impact on the environment, on jobs. on the economy more broadly, and in the wake of recent statements from foundational model companies, on their governability(“rogue AI,” though do also see here), and thus on the prospect of our very existence (though do also see here).
Yesterday’s post was an example of thinking that starts from the implicit assumption that AI is coming, then asks how we might govern it. It’s worth noting that even if there is an AI Bubble about to burst, AI could still loom large in our longer-term future. Investors hobbled in many earlier bubbles (e.g., railroads, telecom, the dot.com crash, et al.) were directionally absolutely right about the longer-term promise of the technologies that excited them, just wrong to believe that the early entrants (and their specific offers) in which they were investing were the vehicles to realize that promise.
Today, we stick with the assumption that AI will continue to develop and to change the world around it. Some suggest that AI and its impact are moving us from literacy back to orality (the loss of which so concerned Socrates). But others suggest that AI (and the advances that it enables) could usher in a new Axial Age, a time of huge and ultimately positive transformation, on the other side of which we– humanity– will be… different, as different from the people we were in, say 1999, as Shakespeare was from Socrates.
But what might that be like? It’s relatively easy to imagine disasters that amount to the things we know collapsing; it’s much harder to envisage positive futures filled with altogether new things, futures the contents of which are, essentially by definition, things we’ve not imagined, much less experienced.
Following, from Vinkatesh Rao, one of the most thoughtful approaches I’ve yet come across. He starts with mathematics– with the recent successes of AIs in that field– locates that work in the history of math (in particular axiomatics), then suggests a way of understanding AI’s “success”:
… A theorem prover does not search the space of all possible sequences of logical symbols. It plays inside a world humans have painstakingly designed.
This makes recent achievements in AI mathematics look slightly different.
Perhaps we have been too quick to interpret them as evidence that AI has acquired access to the wild source of mathematical intuition. A significant part of the achievement may instead be that humans spent a century converting increasingly large regions of mathematical wilderness into exceptionally good game boards: semi-closed environments in which the relevant state can be represented, legitimate moves can be made, accumulated technique can be reused, and success can be verified…
Rao then explains how similar trajectories have accrued in the application of AI to programming and to physics, concluding…
Across mathematics, programming and physics, the same pattern keeps appearing. First there is clever performance. Then somebody finds a representation that captures what the clever performers are doing. Then general machinery accumulates around the representation. Then the machinery becomes architecture. Eventually much of the play becomes mechanical.
The creativity has not disappeared. It has moved.
There is an obvious objection to this story.
Perhaps what I am describing as progress is merely legibility.
James C. Scott famously described [and here] the tendency of modern states to simplify complicated social and natural realities into representations that administrators can see and manipulate: cadastral maps, standardized names, scientific forests, censuses, planned cities.
The representation is useful precisely because it throws things away. A forest seen by a forester interested in timber yield is not the forest experienced by the people, animals, fungi and plants living in it. The administrative forest has become extraordinarily legible along a narrow set of dimensions by becoming blind along others.
Every game performs a similar operation. Before you can play, you must decide what the pieces are. You must decide what constitutes the state of the game, what counts as a move, what consequences follow from moves, and what counts as winning. Everything else becomes environment.
A rigid body in a mechanical simulation is not actually rigid. A point mass is not a point. A frictionless surface does not exist. A program described by its types still runs on processors with caches and memory hierarchies. A theorem encoded in Lean does not contain the diagram that gave somebody the idea for it.
Formalization is compression.
In physics this is explicit. Reality becomes a model, the model becomes mathematics, and the mathematics becomes a numerical representation. Information is discarded at every step.
The miracle is not that nothing is lost. The miracle is how often we can throw almost everything away and still retain what matters…
…
… This suggests an important asymmetry. A productive mathematical ecology wants porous epistemic boundaries and impermeable verification boundaries. On the way in, metaphor, hallucination and category mistakes can be useful. On the way out, they are forbidden.
A poem can inspire the theorem. The poem cannot prove it…
… A domain becomes playable when enough of its phenomenological complexity can be compressed into something like a state; when there is some reasonably stable repertoire of actions; when interactions can be repeated; when outcomes provide feedback about better and worse play; and when the environment is stationary enough that lessons learned yesterday remain useful tomorrow.
None of these conditions needs to be perfect. Poker contains hidden information. Markets change their own rules. Hunting happens in an uncontrolled environment. Conversation does not have an explicit score.
Playability is a matter of degree.
Nor is playability the same as formalizability.
Children learn social games without writing axioms. Hunters learn landscapes. Merchants learn markets. Political operators learn institutions. Formalization is merely one unusually powerful technology for increasing playability.
So are money, measurement, domestication, standardization, simulation, bureaucracy and digitization. They do related things. They compress states. They stabilize interactions. They make outcomes comparable. They create memory. They allow experience to accumulate.
This suggests a hypothesis:
Far more of reality than we might reasonably have expected can be carved into semi-closed environments in which experience accumulates, performance improves through repeated play, and success eventually becomes sufficiently legible to automate.
Call this the Curiously Playable Universe hypothesis…
…
… If playability were merely a property of AI-friendly digital environments, none of this would be particularly interesting.
But the pattern is ancient. Consider agriculture.
Hunting and gathering takes place in a relatively wild learning environment. Animals move. Weather changes. Useful plants appear where they appear. The training distribution is supplied by nature.
Agriculture does something profound to the learning problem: It changes the environment.
Fields stabilize locations. Planting stabilizes cycles. Domestication alters organisms. Irrigation alters water availability. Storage alters time. Property regimes and markets stabilize incentives.
Humans did not merely become better at learning nature. They made nature easier to learn.
The wild game becomes domesticated.
Eventually industrial agriculture pushes large portions of the process toward automaticity: standardized breeds, standardized feed, controlled environments, mechanized planting and harvesting, precisely measured yields.
Then the game moves upward.
Instead of merely optimizing how an organism is cultivated, we begin optimizing the organism itself. Selective breeding becomes genetics becomes genomic selection and genetic engineering.
The old player becomes a game piece.
It is tempting to map this progression onto contemporary machine-learning terminology. Hunting looks vaguely like reinforcement learning in a difficult environment. Agriculture introduces something like shaped rewards and a controlled training distribution. Factory farming begins to resemble a regime of relentless verification against measurable outputs.
The analogy should not be pushed too literally. Human cultures have always involved teaching, imitation, norms and complicated reward systems.
But structurally the direction is unmistakable: increasing control over the state representation, action space, feedback signal and training distribution.
Commerce underwent another version of the transformation.
Exchange begins embedded in relationships, obligations, reputation, kinship and local knowledge. Credit can be intensely personal. Value is contextual.
Money performs an astonishing act of compression.
Heterogeneous goods and obligations become comparable through a common medium. Markets then stabilize arenas in which repeated exchange produces prices. Accounting makes states more legible. Contracts formalize future obligations.
Eventually finance builds games on top of the game.
A derivative can be a claim on the future value of another asset. Options put prices on possible future prices. Markets become arenas for expectations about expectations.
Play moves upward.
None of this required artificial intelligence. Humans have been turning wildernesses into games for thousands of years.
What AI changes is the cost of achieving playability in a domain.
Historically, making a domain playable was expensive. Somebody had to invent the categories. Somebody had to measure the variables. Somebody had to standardize the procedures. Somebody had to construct the institutions that made interactions repeatable.
And the resulting representation had to be relatively explicit because ordinary software was brittle.
Machine learning relaxes that requirement…
Rao muses on where this effect might accrue…
Coding is perhaps the clearest domain currently passing through the transition.
Programming was unusually playable before generative AI arrived.
Compilers already supplied verifiers. Type systems constrained legal moves. Unit tests supplied rewards. Version control recorded trajectories. Continuous integration repeatedly evaluated outcomes. Package ecosystems created enormous libraries of reusable moves.
Generative coding systems arrived in a landscape generations of programmers had inadvertently prepared for them.
As implementation becomes cheaper, the game moves upward toward specifications, architectures, product decisions and the increasingly important question of what software should exist in the first place.
Robotics is a more difficult frontier.
The physical world is phenomenologically unruly. Objects deform. Friction varies. Things break. Lighting changes. People walk into rooms. Drawers stick.
Simulation, cheap sensors, multimodal models and increasingly capable world models are gradually making physical environments more playable, but reality continually leaks through the representation.
This is why robotics may be one of the most important tests of the Curiously Playable Universe hypothesis.
Can enough of ordinary physical reality be compressed into stable learned representations to permit the same cycle of domestication and automation?
Science will probably fragment according to playability rather than according to our traditional rankings of intellectual difficulty.
Protein structure turned out to be unusually playable.
Drug discovery is less so because chemical promise must survive biology, organisms, clinical trials and human heterogeneity. Materials discovery may become increasingly playable through the combination of simulation and automated laboratories. Ecology may remain stubbornly wild because every useful abstraction excludes interactions operating at another scale.
Medicine contains both extremes. Image interpretation and molecular design can be made relatively game-like. Caring for an elderly person with five interacting conditions, family constraints and changing preferences is another matter.
Governance may become a particularly strange frontier.
States have spent centuries making populations legible through names, addresses, laws, property records, taxes, censuses, bureaucratic categories and standardized procedures. In that sense the modern administrative state is already a vast game-making machine.
AI will make more of its internal operations automatic.
But political systems contain an unusual source of wilderness: the pieces know they are pieces.
People respond strategically to measurements, categories and incentives. A rule changes the behavior it was intended to regulate. A metric becomes a target and stops being a good metric (Goodhart’s Law). Political actors learn to play the machinery designed to make them legible.
This may push governance upward toward new games involving states themselves: transnational protocols, financial systems, supply chains, standards regimes, platform governance and other structures that do not fit comfortably inside the old nation-state game board.
Some domains may resist for much longer.
Child-rearing is difficult to make playable because the objective changes as the child changes. Friendship has no stable score. Diplomacy involves adversaries whose models include models of your model of them. Entrepreneurship often consists precisely of discovering a game nobody realized existed.
Frontier science operates where we do not yet know which measurements matter. Cultural creation is evaluated by audiences who change partly because of the works being evaluated. Political legitimacy is altered by attempts to measure and optimize it.
These are not necessarily domains AI cannot enter. They are domains in which the wilderness fights back…
He continues…
… We keep asking what it means that machines can now play chess, write programs, fold proteins, prove theorems, steer robots and discover molecules. The question presumes that the remarkable new object is the machine—that some mysterious substance called intelligence has finally become “general” enough to flow from one human province into another.
But perhaps the stranger discovery concerns the provinces: Wildernesses can be reliably turned into game boards. We domesticate the territory, become skilled at its game, build machinery that plays it better than we can, and move outward to construct another game around the first, domesticating a larger scope.
This happened to fields and livestock long before it happened to chess. It happened to trade before it happened to programming. It happened to mechanics before it happened to mathematical proof.
Artificial intelligence did not invent this strange property of reality. It is merely making it difficult not to notice.
The deepest surprise of the AI era may turn out not to be that intelligence was easier to manufacture than we thought. It may be that the universe is far more playable than we imagined…
And he concludes with a consideration of writing…
… Writing sits somewhere awkwardly in the middle of the playability spectrum. It is obviously more playable than friendship or political legitimacy. There are stable artifacts, accumulated techniques, recognizable genres, repeatable operations and abundant feedback. Sentences can be revised. Arguments can be tested for consistency. Stories can be checked for continuity. Editors can compare two versions and usually say something useful about why one works better.
But writing lacks the feature that makes Lean such an extraordinarily good game board: a verifier. There is no kernel that accepts Middlemarch and rejects a bad novel. The relevant state is incompletely represented, the available moves are effectively unlimited, and the reward function wanders around outside the text in readers, institutions, historical circumstances and cultures that change partly in response to what gets written. Recent attempts to extend verifiable-reward techniques to writing therefore have to manufacture approximate evaluators out of principles and pairwise judgments rather than simply checking an answer. Writing is playable, but imperfectly and unevenly so.
The unevenness is becoming easier to see because AI is revealing which kinds of writing were already more game-like than we realized. A corporate memo, SEO article, product description, technical explanation or conventional news report has relatively strong constraints: purpose, audience, format, facts, length, house style, perhaps measurable outcomes. Formulaic genre fiction has a looser but still recognizable game board of beats, tropes, pacing, character functions and reader expectations. Even fiction once assumed to depend heavily on irreducible human voice is proving surprisingly susceptible to systematic generation and variation. Writing is not one game but a family of wilderness activities at very different stages of domestication.
At the other end lies writing whose purpose is partly to alter the terms by which it will be judged. As Walter Benjamin observed, “all great works of literature establish a genre or dissolve one.”
A genuinely new literary form, a strange essay, a foundational work of philosophy, or a piece of criticism that gives its readers a concept they did not previously possess cannot simply optimize against an existing reward function. Its success may consist in creating a new one. This is writing at its most wilderness-like, and perhaps why arguments about AI writing become confused when “writing” is treated as a single capability. Producing competent prose, satisfying a genre contract, developing an argument and inventing a form are different games with different degrees of playability…
… The meta-game emerging above writing may therefore involve constructing—and strategically revealing—the game boards on which execution takes place. If competent prose becomes cheap, more resources can be directed toward finding the question, assembling unlikely source domains, inventing the useful distinction, designing sequences of inquiry, recognizing when an analogy is productive, deciding what belongs together, and establishing the criteria by which a finished object ought to succeed. The writer becomes somewhat less like a person manufacturing sentences and somewhat more like an architect and player of generative constraints.
That does not mean prose becomes irrelevant. A game board badly realized is still a bad essay, just as a brilliant architectural plan does not eliminate the need for a building. Nor does it mean the higher-level activity is permanently reserved for humans. AI systems may themselves become increasingly capable of inventing representations, designing probes, discovering genres and constructing new games. The point is only that automaticity at one level does not end the activity. It displaces its frontier.
Writing may therefore be undergoing the same transition described throughout this essay, only messier and in public. Some of its old games are becoming startlingly playable. Some are approaching automaticity. New games are forming above them. And beyond those remains the poorly mapped territory from which the next game board might emerge…
Eminently worth reading in full: “The Curiously Playable Universe,” from @vgr.bsky.social.
And further: “Our Eukaryotic Moment,” in which Rao urges us to keep our minds open– to resist “premature ontological closure in conceptualizing AI.” A taste:
… About two billion years ago, life underwent a change in architecture. Until then, the planet was ruled by relatively simple prokaryotic cells: bacteria and archaea, tiny packets of chemistry bounded by membranes, carrying their genetic material directly in the same cellular space where much of the business of life took place.
Then something happened, or rather, several things happened in an order that remains uncertain. At some point, an ancestral archaeon cell entered into a permanent symbiosis with a bacterium capable of unusually powerful energy metabolism.
That bacterium eventually became the mitochondrion.
Somewhere in the same long evolutionary transition, genetic material became enclosed within a nucleus, separating the storage and regulation of hereditary information from much of the cell’s everyday chemistry. Internal membranes proliferated, cytoskeletons became more elaborate, and a new kind of cell emerged: the eukaryote….
… Eukaryotic cells became larger, more internally differentiated and capable of forms of organization unavailable to their ancestors. Much later, and independently in several lineages, some evolved multicellularity. Cells themselves became components of larger entities, differentiating and cooperating in increasingly elaborate ways. From the eukaryotic architecture eventually came forests, mushrooms, octopuses, hummingbirds, whales and us.
Eukaryotes were more than just “better bacteria.” They represented a different organization of life, capable of sustaining far more complex structures..
We may be living through an analogous transition now. The emergence of AI, in the particular form that it has appeared (deep learning), is arguably the eukaryotic moment in human cultural evolution, understood in memetic terms, with humans playing the role of mitochondria, and AI the role of the nucleus…
Meantime: “After We Stop Grieving AI: Cyborgs, Centaurs and Cyberpunks” (via Patrick Tanguay) See also: “Diving Deeper Into Digital Transformation” from John Hagel (source of the image above).
As the estimable Tim O’Reilly suggests, we’d probably be wise to take a breath, to treat AI as a “normal technology”: “Technology doesn’t force us… it merely opens the door.”
* Rainer Maria Rilke, Sonnets to Orpheus, Part Two, XII
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As we crane to conceive what’s beyond our cocoon, we might recall that it was on this date in 1947 that the Association for Computing Machinery (or ACM) was founded. In one sense, computer science dates back to the 17th century and the mechanical calculators of Wilhelm Schickard and Gottfried Leibniz; in another, to the Difference and Analytical Engines of Charles Babbage (and the “programming” of Ada Lovelace). But the ACM can claim to be at least a key stone in the foundation of the field we now knw as “Computer Science”– the first modern “learned society” devoted to the emerging field. (Computer Science began to be established as a distinct academic discipline in the 1950s and early 1960s. The world’s first computer science degree program, the Cambridge Diploma in Computer Science, began at the University of Cambridge Computer Laboratory in 1953. The first computer science department in the United States was formed at Purdue University in 1962.)
“The future is dark, which is, on the whole, the best thing the future can be, I think”*…
Further (and in some ways, contra) to yesterday’s post, Marek Poliks, Roberto Alonso Trillo, Tyler Dunn, Hugh Scott-Douglas, and Jay Springett ponder one extrapolation of AI…
Imagine the most extreme possible factory: a factory that drives itself, a factory that determines its own objectives and optimizes itself to accomplish them. It is something like a fully automated firm, a company with no people inside, a recursively self-optimizing body that decides what to make, how to make it, and why. This is a very special kind of factory, a factory built with the intention of producing something strictly in excess of its creator’s abilities, something so productive, so efficient, and so capable that it exceeds any possibility of its prescription in advance, something that belongs to the set of possible factories that are, at present, unthinkable. How might one build such a thing? What would it mean to steer or govern such a thing? We call this factory a superdark factory, a factory whose insides churn too quickly to comprehend, whose logics branch into tangles too dense to reconstruct, and whose standards of performance are too ephemeral to direct. We believe that it is possible to build such a factory now, with existing tools, should one decide to do so.
Toward such an end, we propose the dark stack—a collection of design principles an architect could use to bring about such a factory and, further, to contribute to this factory’s ongoing governance after its initialization, all while remaining unable to understand almost anything about it. The most significant intervention available to an architect building a superdark factory with the dark stack is a single committed move, made before the factory is ever turned on, that sets four key principles into motion: (1) an array of structural primitives that enable the factory to learn; (2) a versioning regime that measures what the factory does rather than what it is; (3) an adversarial evaluation layer that serves as the factory’s primary sensory organ; and (4) a charter that functions as a negotiated site of governance, cowritten with the factory itself. After this initial move is complete, the dark stack’s most consequential implications involve the foreclosure of human control from factory design and the subsequent instatement of the human architect as a game-theoretical strategic player engaged with the emerging totality of production…
The most extreme possible automated factory- how would you build it? How would you manage/govern it? “The Superdark Factory,” from https://x.com/antikythera_xyz and @mitpress.bsky.social.
See also: “What is a black box? A computer scientist explains what it means when the inner workings of AIs are hidden” (from whence, the image above)
* Widely attributed to Virginia Woolf’s private journal
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As we feel our way forward, we might recall that it was on this date in 1716 that the first lighthouse in America, Boston Light, was lit on Little Brewster Island, marking the entrance to the Boston, Massachusetts harbor. (Canada’s first lighthouse, the Louisbourg Lighthouse, located on Lighthouse Point at the entrance to Louisbourg Harbour on Cape Breton Island, was lit in 1734.)
During the American Revolution, the original lighthouse was held by British forces and was attacked and burnt on two occasions by American forces. As the British forces withdrew in 1776, they blew up the tower and completely destroyed it.
The lighthouse was eventually reconstructed in 1783, to the same 75-foot height as the original tower. The reconstruction is the second oldest working lighthouse in the United States (after Sandy Hook Lighthouse in New Jersey) and is the only lighthouse still actively staffed by the U. S. Coast Guard (despite its automation in 1998).
“… preferring their own ease and sloth before the general improvement of their country”*…
Dónal Gill, a college professor grappling with AI, looks to Jonathan Swift for guidance. Three hundred years ago, the satirist warned of a world in which reading and writing are replaced by the flashy simulation of human knowledge…
I have spent a significant portion of my life studying the Irish satirist Jonathan Swift — and in particular, studying Gulliver’s Travels, which turns 300 this year. While working on my Ph.D. dissertation, I was primarily interested in what Swift had to say about the politics of travel. Now, as a college professor teaching Canadian politics and battling to keep generative artificial intelligence out of my classrooms, I’m thinking a lot about what Swift had to say about technology. In his day, as in ours, a dominant narrative portrayed scientific development as both benevolent and inevitable. Swift’s skepticism about that narrative exposes some of our own most deeply held assumptions about the meaning of progress.
Born in Dublin in 1667, Swift is frequently described as the greatest satirist in the English language. This reputation is tied to the biting wit and unruly imagination found across his prose, poetry and political writings. In “A Modest Proposal” (1729), he notoriously suggests that the Irish poor should eat their own children for subsistence. Swift was born into the Anglo-Irish Protestant elite, and the text satires both the callousness of his class and a then-ascendant approach to morality and politics that held that all aspects of life could be cooked down to a pure numerical value. That this approach ended up predominating in the boardroom and halls of government is one reason why the brisk and shocking “Modest Proposal” still speaks to readers today.
Gulliver’s Travels is a much lengthier work. Over the course of Gulliver’s four voyages to fantastical lands, Swift gives the reader wild and varied depictions of political corruption and human hubris. Gulliver’s third voyage takes him to the flying island of Laputa, which rules tyrannically over several territories situated below. On his visit to one of these territories, Gulliver tours the “Academy of Lagado,” where scientific experimentation and deciphering the patterns of mathematics and music are valued above all else. The scientists he meets are profoundly unconcerned with whether their experiments have any useful outcome.
At the time Swift was writing, the experimental scientific method as we know it today — data collection and the development of hypotheses from empirical evidence — was becoming prominent, thanks to the work of Sir Isaac Newton and the Royal Society. Critics and readers have often viewed Gulliver’s third voyage as a moment when Swift chose the wrong subject to satire. The achievements of Newton and the Royal Society are, after all, still celebrated today. But I think Swift knew exactly what he was doing. Far from merely raising a reactionary fist in the air at the new science, Swift identified an attitude that is still around today — and one that has created many of the greatest challenges now facing educators.
Centuries before the internet infiltrated almost every aspect of daily life, Swift described how scientific and technological advances could lead to chronic distraction by producing an obsession with what could be rather than what is. At the heart of Swift’s diagnosis is the idea of speculation, or as Gulliver describes it, “speculative learning.” Gulliver sees a society totally consumed by impractical and abstract scientific interrogation. This plays out in bizarre and frequently doomed projects and experiments: Gulliver incredulously witnesses people attempting to extract sunbeams from cucumbers and turn excrement back into edible food.
Not all the scientific projects in the territories of Laputa fail to achieve their purpose. Gulliver is shown a large wooden contraption that allows “the most ignorant person … [to] write books in philosophy, poetry, politics, laws, mathematics, and theology, without the least assistance from genius or study.” [See the illustration above.] Swift, who was deeply learned, was envisaging a nightmare world in which reading and writing — the essential foundation of wit and wisdom — are replaced by a flashy simulation of human knowledge. Since the advent of ChatGPT, this scenario has become all too familiar. The device Gulliver encounters is massive, requiring 40 individuals to rotate iron handles attached to multiple words, which are then assembled into sentence fragments and called out to a scribe. Reading Swift’s description, I think both of the autocomplete nature of modern large language models and of the underclass of precarious workers whose labor is necessary for the seemingly lean, seemingly all-digital process of generative AI to work.
There are also many Laputans who are given over to a strange life of the mind, “so taken up with intense speculations, that they neither can speak, nor attend to the discourses of others, without being roused by some external taction upon the organs of speech and hearing.” Servants called flappers are required to shake rattles in their ears to keep them from falling or bumping into things. A quick rattle might also be used to rouse them from the somnambulant abyss of speculative abstraction, if conversation is necessary. Again, sounds rather familiar. Sure, people today wandering the streets head down in the infinite distractions of their phones are hardly absorbed in the life of the mind — but they are still absent from the things and the people around them. And isn’t the addiction to scrolling predicated on speculation of sorts? What will come next? What will I find if I look at just one more post?
Modern technology, and AI in particular, has created another Laputa: a society characterized by rampant speculation, blind faith in technology manipulated by cynical charlatans, and the casual disregard of old-fashioned virtues like truth, beauty, imagination and knowledge acquired the hard way. In many cases, the presence of AI in the classroom is a solution in search of a problem — just like the ludicrous, pointless innovations by the Laputan scientists. Swift’s sharp diagnosis of Laputa’s ills is motivating me to push back against the encroachment of AI into the work of education — and to instill in my students the belief that they, rather than the forces of technology, can determine the future…
Do read on for more: “Jonathan Swift v. AI,” from @donalgill.bsky.social in @thedialmag.bsky.social.
* Jonathan Swift, Gulliver’s Travels, Part III (“A Voyage to Laputa, Balnibarbi, Luggnagg, Glubbdubdrib and Japan”)
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As we shape our tools, we might recall that it was on this date in 1870 that a patent was granted to Daniel C. Stillson for the modern adjustible pipe wrench (an adaptation of/improvement to the monkey wrench) that he had invented the prior year.
“Let us leave a splendid legacy for our children… guard it well, for it is far more precious than money… and once destroyed, nature’s beauty cannot be repurchased at any price.”*…
From the National Archives, an extraordinary collection of Ansel Adams‘ photos of National Parks in the U.S…
In 1941 the National Park Service commissioned noted photographer Ansel Adams to create a photo mural for the Department of the Interior Building in Washington, DC. The theme was to be nature as exemplified and protected in the U.S. National Parks. The mural project was halted because of World War II and never resumed, however the photographs remained.
Much later in 1962 the photographs were accessioned into the holdings of the Still Picture Branch at the National Archives and Records Administration. The original prints created for the project now reside within the series 79-AA: Ansel Adams Photographs of National Parks and Monuments, 1941 – 1942, while the negatives were originally retained by Adams at the time. Ansel Adams would later visit the Still Picture Branch and review his photographic prints in 1979.
The holdings of the National Archives’ Still Picture Branch include 226 photographs taken for this project, most of them signed and captioned by Adams. They were taken between 1941 and 1942 at the Grand Canyon, Grand Teton, Kings Canyon, Mesa Verde, Rocky Mountain, Yellowstone, Yosemite, Carlsbad Caverns, Glacier, and Zion National Parks; Death Valley, Saguaro, and Canyon de Chelly National Monuments. Other pictures were taken at the Boulder Dam; Acoma Pueblo, NM; San Idelfonso, NM; Taos Pueblo, NM; Tuba City, AZ; Walpi, AZ; and Owens Valley, CA. Many of the latter locations show Navajo and Pueblo Indians, their homes and activities.
The Kings Canyon photographs were taken in 1936 when the establishment of the park was being proposed. These prints were added by Adams to the mural project. The one photograph of Yosemite (79-AAU-1) was a gift from Adams to the head of the Park Service, Horace Albright, in 1933.
In addition, there are eight photographs taken by Adams of Yosemite in the General Photographic Files of the National Park Service (79-G)…
Many, many more at: “Ansel Adams Photographs of National Parks.”
“It is horrifying that we have to fight our own government to save the environment” – Ansel Adams
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As we wonder, we might recall that it was on this date in 1940 that an ancestor of the art featured above was discovered: a teenager named Marcel Ravidat found the entrance to cs in southwest France when his dog investigated a hole left by an uprooted tree. Ravidat collected three friends, then followed his dog down what turned out to be the narrow entrance into a cavern, where they came upon (part of) the now-storied collection of wall markings— 15,000- to 17,000-year-old paintings, consisting mostly of animal representations– that are among the world’s finest examples of art from the Upper Paleolithic period.
See also: “To practice any art, no matter how well or how badly, is a way to make your soul grow.”
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