In Hilbert Space, All Things Are Quantumly Possible

Mike's Notes

This one got me thinking. John von Neumann's 5 laws might be useful inside Pipi for determining any state. I need to do a lot more reading.

The 5 Quantum Commandments of John von Neumann

    1. An arrow in Hilbert space shall represent the quantum state of any object.
    2. Altering the object shall make the arrow rotate smoothly through Hilbert space to represent a new state.
    3. Distinct axes in Hilbert space shall reflect different possible sets of properties of the object.
    4. The shadow the arrow casts onto an axis shall encode the odds of that possibility being realised when a measurement takes place.
    5. Upon measurement, the arrow shall randomly and instantaneously jump to align with the axis representing the observed outcome, and the object shall acquire a fully determined property.
- Quanta Magazine

The original article has some SVG dynamic diagrams which are missing here.

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02/09/2026

In Hilbert Space, All Things Are Quantumly Possible

By: Charlie Wood
Quanta Magazine: 26/08/2026

Charlie Wood: Staff Writer, Quanta Magazine.

...

To explore quantum phenomena, we must leave the familiar world and enter the abstract realm of Hilbert space.

...

At the heart of quantum mechanics lie a few sacred rules for how to use the theory. First and foremost is, roughly, that thou shalt not think about ordinary objects presently whizzing through ordinary space. Rather, quantum mechanics predicts — in exquisite detail — all the possible ways that an object might turn out to be in the future. Exploring those possible futures requires tracking an entirely different mathematical object — an arrow known as a vector, one oriented in an expansive, alien domain.

These arrows aren’t pointing at locations. “It’s a much more abstract space than that,” said Lucien Hardy, a physicist at the Perimeter Institute for Theoretical Physics in Waterloo, Canada. They’re “really pointing in a direction in a possibility space.”

This possibility space is called Hilbert space, and it acts as the primary arena for quantum physics.

The early quantum pioneers didn’t realize — at first — that the arcane math that strikingly captured the conduct of atoms had left the real world behind. It took a visionary mathematical physicist, John von Neumann, to recognize and define the quantum world as a Hilbert space. Once he did, exploring the ins and outs of Hilbert space would lead physicists to a deeper, more unified understanding of quantum physics.

Here’s how von Neumann’s first commandment of quantum physics came to be, and how to understand it. 

What Is Hilbert Space?

Von Neumann’s commandments, or axioms, were his way of making sense of the two distinct forms of quantum mechanics developed back-to-back in the 1920s. First came Werner Heisenberg’s “matrix mechanics” in 1925. It used inscrutable tables and, in later formulations, interminable towers of numbers to calculate the odds that an electron circling an atom would jump to a higher or lower orbit. The next year, Erwin Schrödinger introduced his “wave mechanics.” It used waves to track, for instance, the probability of a particle being found at a certain location in space. While the pictures evoked by these two physicists looked completely distinct, they yielded identical predictions. Heisenberg and Schrödinger had come up with two radically different incarnations of one theory. But what was that theory?

The question fascinated David Hilbert, a renowned mathematician who had devoted much of his life to rebuilding physics on a sturdy foundation of crisp axioms. He got von Neumann thinking about the problem in the mid-1920s. In 1927 the 23-year-old prodigy — building on insights from Paul Dirac — solved it in a single-author trilogy of papers.

A bearded man sits in a wicker chair.

The mathematician David Hilbert sought a mathematical structure that would unify the different forms of quantum mechanics.

MacTutor

“The ideas specifically were von Neumann’s, but the inspiration — why do you axiomatize and what for — this is something that he took from Hilbert,” said Leo Corry, a historian of mathematics.

These papers laid out the rules for quantum mechanics, carefully defining the theory’s central objects and how they behaved. Von Neumann showed that Heisenberg’s towers and Schrödinger’s waves were reflections of the same entity, just as 0.5 and ½ indicate the same point on the number line. They both represented the main character in quantum mechanics: the quantum state.

Everything has a state. A coin can read heads or tails. A grandfather clock’s bob can take on any number of positions as it swings. Most of physics amounts to capturing an object’s state and predicting how it will change.

Von Neumann’s quantum rules complicate the notion of a state. Before you observe a quantum object, it does not have a fixed set of properties, such as a specific position. Instead it has a combination of possible properties unique to quantum mechanics — a “quantum superposition.” A superposition combines, for instance, all possible places the particle might end up being. Those possibilities can be precise and informative; perhaps there is a 99% chance you’ll find your particle to your left and a 1% chance you’ll find it to your right. You know how to place your bets, but you can’t know for sure if you’ve won until you check.

Von Neumann rendered the quantum state as a mathematical arrow called a vector. This arrow points in some direction through a space capturing all the possible futures of any quantum object — a Hilbert space.

Imagine a quantum traffic light with three possible states — red, yellow, or green. Its arrow exists in a three-dimensional Hilbert space, where the three axes represent the three possible future colors. Until the moment the light is observed, it doesn’t have a color, but rather a mixture of possible colors. So its arrow points into the space’s central region. The more closely the arrow aligns with, say, the red axis, the more likely the light is to shine red.


Diagram

Mark Belan/Quanta Magazine

The state of any object, from an electron to a galaxy, can be captured by such a vector, pointing in some direction through such a Hilbert space. This is von Neumann’s first rule of quantum mechanics. 

What Happens in Hilbert Space?

An arrow moves through Hilbert space in one of two ways. Von Neumann’s other commandments specify how.

The first possibility corresponds to what happens before an observation. As the world influences the object, changing its state, the arrow turns smoothly through Hilbert space. It might get closer to the green axis, which would make our traffic light more likely to be measured as green, or to red or yellow. The point is that all this happens smoothly and predictably.

Then, if you actually observe the system, the vector will instantly and randomly snap onto either the red, yellow, or green axis. The more aligned it is with one axis, the more likely it is to snap to that axis instead of the others, but its fate is ultimately unpredictable. Let’s say it goes green. You’ll observe a green light, and there is now a 100% chance that it will still be green in subsequent measurements, because the arrow is fully aligned with the green axis. The quantum superposition is no more.

Diagram

What Properties Define Hilbert Space? 

The more possible futures an object has, the bigger its Hilbert space. A coinlike particle with two possible futures is a “qubit,” the computational building block of quantum computers. It has a two-dimensional Hilbert space. Our three-color traffic light has a three-dimensional Hilbert space. But that’s just the beginning. A freely floating particle could be found in any location in the universe, so its Hilbert space must span an infinite number of dimensions.

This size — whether it’s two dimensions or an infinite number — is the only fundamental feature of a Hilbert space, according to von Neumann’s rules. The axes are arbitrary and imagined by us; they aren’t intrinsic to the space.

Consider an electron. It has one state, one arrow, pointing in a vast Hilbert space. Its Hilbert space spans all possible measurements — energy, position, momentum, etc. If you are curious where the electron might be, you can mark the space with the axes that represent possible positions. If you are wondering where the particle might be going, you apply a different set of axes, those representing possible momenta. No matter which measurement you intend to make, the underlying Hilbert space remains the same.

This freedom to carve up Hilbert space as we see fit is what allowed Heisenberg and Schrödinger to come up with two distinct versions of the same theory. Heisenberg’s picture essentially put in axes and let them rotate around the vector, while Schrödinger’s picture did the opposite: It put in a fixed set of axes and let the vector rotate relative to them. They were two completely different mathematical perspectives on the same arrows, in the same Hilbert spaces.

A man sits wearing a suit and tie.

John von Neumann developed an underlying structure for quantum mechanics that involves arrows moving inside an abstract Hilbert space.

US Department of Energy

In the first of his 1927 papers, von Neumann laid out two mathematical criteria that defined such a space. First, it had to be “complete.” It couldn’t be missing any regions or points. And second, you had to be able to calculate the alignment between a state and an axis, which you can visualize by imagining a light shining straight down onto an arrow so it casts a shadow on an axis. (The longer the shadow, the more aligned with that axis the arrow is.) The space had to allow for this operation, known as an inner product. Any space with these two features, no matter its size or origin, was a Hilbert space.

“This was a major step in creating what we call Hilbert space quantum mechanics,” said Miklós Rédei, a philosopher of physics at the London School of Economics. “It’s a beautiful example of how mathematical generalization or abstraction takes place.”

Von Neumann referred to these abstract spaces as Hilbert spaces because his mentor Hilbert had been the first mathematician to explore specific spaces with infinite dimensions in the early 1900s. Hilbert’s work had relied on those spaces being complete and having an inner product, but he didn’t think of them as examples of a more general class of spaces until his protégé grouped them together. The older mathematician may have been surprised to find his name attached to this new mathematical structure. “Dr. von Neumann, I am really curious to know what these Hilbert spaces are, after all,” Hilbert reportedly asked during a 1929 lecture. 

Is Hilbert Space Real or Just an Abstraction?

A century after the birth of quantum mechanics, the theory has left physicists in an awkward position. We live in a world where objects change position as they move through three dimensions of physical space. But our most fundamental theory takes place somewhere else, in von Neumann’s vast realm of possibilities. What does that imply about the reality of our world, or that of Hilbert space?

Mathematically Crucial Fine Print

In quantum physics, Hilbert spaces use complex numbers, which involve the imaginary number i, and negative regions. But von Neumann’s fourth commandment guarantees that the odds of any possibility will always come out as a real, positive number.

To Sean Carroll, a philosopher and physicist at Johns Hopkins University, the message is clear. If quantum mechanics is the fundamental theory of nature, then Hilbert space should be considered the fundamental theater of reality, he argued in a 2022 paper(opens a new tab). One of his lines of research seeks to distill our familiar world from the disorienting Hilbert space that encapsulates all the ways the universe could possibly be.

Other physicists take a more pragmatic stance. Jonathan Sorce, a physicist at Princeton University, says that Hilbert space is a handy mathematical construction that is remarkably useful for describing many quantum systems — but not all of them. He belongs to a community of researchers searching for a mathematical construction that can describe the fabric of space and time as a quantum object. Such a theory is a prerequisite for answering big questions such as what goes on at the heart of a black hole.

Physicists asking these questions have recently focused on an even more abstract space that seems especially well suited for their purposes. This kind of space is made up of the things you could do to a Hilbert space, such as slicing it up in different ways or rotating one slice into another. In this arena, they have found that black holes seem a bit less mysterious.

This sort of über-space is known today as a von Neumann algebra. Von Neumann himself helped develop it as a potential remedy for some logical inconsistencies(opens a new tab) with Hilbert space that troubled him. “I would like to make a confession which may seem immoral: I do not believe in Hilbert space anymore,” he wrote in a 1935 letter while exploring the virtues of algebras.

Sorce, for his part, doesn’t share von Neumann’s desire for one space to rule them all. He’s content to use whichever mathematical construction best suits the quantum object he’s studying. Often it’s a Hilbert space. Sometimes it’s a von Neumann algebra. And occasionally it might even be one of the many other spaces mathematicians have cooked up over the last century.

“There’s a whole zoo of these things,” he said.

The 5 Quantum Commandments of John von Neumann

  1. An arrow in Hilbert space shall represent the quantum state of any object.
  2. Altering the object shall make the arrow rotate smoothly through Hilbert space to represent a new state.
  3. Distinct axes in Hilbert space shall reflect different possible sets of properties of the object.
  4. The shadow the arrow casts onto an axis shall encode the odds of that possibility being realised when a measurement takes place.
  5. Upon measurement, the arrow shall randomly and instantaneously jump to align with the axis representing the observed outcome, and the object shall acquire a fully determined property.

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Books Have Always Been Destroyed. But Never Like This

Mike's Notes

I love print books and libraries. Books in print are precious. AI needs to benefit humanity, not be a destructive force.

Trinity College, Ireland

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01/09/2026

Books Have Always Been Destroyed. But Never Like This

By: Hana Lee Goldin
Card Catalog: 25/08/2026

Your personal librarian for the AI age. Forever in the pursuit of exploring how we find, filter, and feel about information.

We’ve entered the third era of libricide.

...

Quick summary:

An AI company has been buying up used books by the million and destroying them, scanning the pages for training data and pulping what’s left. Court filings unsealed this year describe the program and an internal note asking that the work be kept from becoming known. A court has ruled that buying and scanning the books this way is legal, and once the work is done, nothing survives to show a book was ever there to lose.

Key takeaways:

  • The books are bought through anonymous middlemen, so the sellers filling the orders rarely know where their stock is headed. From every angle the buying looks like ordinary commerce, which is exactly what keeps the destruction from being seen.
  • Book destruction has a long history, and it changes each time the technology of copying changes. It has shifted twice before. This is the third shift, and it looks nothing like the book burnings most of us picture.
  • This shift is set apart by leaving nothing behind. The words are kept, the object is discarded, and no record says which titles were taken, so the loss can be neither proven nor traced to anyone who might answer for the harm.
  • That reaches past books to anyone who wants to know things firsthand. When the only surviving copy is sealed inside a system no outsider can consult, verifying what it says becomes impossible, and we are left trusting whatever summary we are given.

...

“We don’t want it to be known that we are working on this.”

The sentence appears in an internal Anthropic planning document made public through court filings in Bartz v. Anthropic, the copyright class action that three authors filed in 2024. Anthropic, the maker of Claude, called the program Project Panama. Its stated mission fit in one sentence: “Project Panama is our effort to destructively scan all the books in the world.”

Destructive scanning means cutting a book from its binding, feeding the loose pages through a scanner, and discarding the book afterward. According to the court, Anthropic spent millions of dollars buying millions of print books, often used. Its service providers removed the bindings, cut the pages, scanned them into searchable digital files, and discarded the books. Anthropic kept the scans in an internal digital library and used books from that library to train the AI systems behind Claude.

The lawsuit initially concerned another source of Anthropic’s library: more than seven million pirated book files the company had downloaded. The court treated the two acquisition paths differently. It held that Anthropic could lawfully buy print books, convert them into searchable digital files for internal use, and discard the physical copies; because the resulting files remained inside the company rather than being redistributed, that practice was fair use. It reached the opposite conclusion about the books Anthropic had downloaded from pirate sites and retained.

Companies announce the programs they are proud of; Anthropic tried to keep this one invisible. The January unsealing supplied the project’s codename and the instruction to stay silent. The instruction anticipated what the company did not want authors, booksellers, and the reading public to see: books bought by the million, cut apart, scanned, and discarded. Anthropic didn’t publicly announce Project Panama before court records made it public.

The silence has no precedent. Books have been destroyed for as long as they’ve been made: by conquering armies and offended churches, by censors with lists and mobs with torches, by floods and fires, and by budgets that let the roof leak. Some of it was fast and public. More of it was slow and official. All of it, loud or slow, could be recognized for what it was. A person watching knew that books were being lost. History kept what record it could. But what’s happening now carries no such signature. From the outside, nobody could tell that books were being destroyed at all.

Inside Project Panama

Anthropic hired a man named Tom Turvey in February 2024 and gave him a mission the court record repeats in one sweeping phrase: obtaining all the books in the world. Turvey came to the job from Google Books, the project that spent the 2000s digitizing library collections on machines engineered to turn pages gently, so that every book survived its own scanning. Anthropic took the opposite approach. Gentle machinery never entered the plan. Over roughly a year, the company spent tens of millions of dollars buying millions of print books, sheared off their spines, fed the loose pages through high-speed scanners, and pulped what remained. Vendor proposals in the court records described converting up to two million books in six months, some eleven thousand a day.

Destroying the books solved a financial problem first. A bound book must be scanned page by page, slowly and at cost. Once cut apart, the same volume becomes a stack of loose paper that can run through a sheet feeder at speed. Across millions of volumes, the difference in output determined the method.

The legal significance emerged later. In June 2025, Judge William Alsup ruled on the authors’ claims. For the books Anthropic had bought and destroyed, he found the copying to be fair use: the rule in American copyright law that allows limited copying without an author’s permission (the way a critic can quote a novel in a review). His reasoning turned on replacement. Anthropic bought one physical copy, made one digital copy, and destroyed the original—so the number of copies in the world never grew. In the law’s eyes, the scan simply took the book’s place, a change of format. If Anthropic had kept both the book and the scan, the number of copies would have doubled and the fair-use argument would have weakened. Pulping the originals helped make the copying legal.

The pirated downloads fared differently in the same ruling. For those more than seven million files, Alsup rejected Anthropic’s fair-use defense. He emphasized that Anthropic had built a permanent, general-purpose library it expected to keep indefinitely, not a temporary collection for a defined training task. “None is even offered here except for Anthropic’s pocketbook and convenience,” he wrote.

The ruling left the company facing a trial over damages, and Anthropic settled instead: the company agreed to pay $1.5 billion. A judge granted the deal final approval on July 20, the largest copyright class settlement in American history. Under the deal, Anthropic will delete the pirated digital files. Critically, the deal concerns those files, not the millions of physical books the company bought and pulped. Copyright protects the text of a book—the creative expression of an idea—not the paper on which those words were printed. Once Anthropic lawfully owned a physical copy, copyright law generally didn’t prevent it from destroying that object. The books could be destroyed without creating the kind of copyright violation at issue in the case.

But books don’t enter a library by themselves: somebody had to sell the company all those books. The court records describe Anthropic purchasing through vendors. This spring and summer, booksellers across Europe described the other side of such a trade: unusually large, eclectic orders placed through intermediaries, with the ultimate buyer unnamed. Tomás Kenny of Kennys of Galway called one order for books “bananas”—a mix of titles no library would plausibly assemble. Whether any particular order came from Anthropic cannot be established from outside the transaction. Nor is Anthropic the only possible customer: other AI companies have also been reported to be acquiring books at industrial scale.

The reported market is built to keep the buyer at a distance. Brokers can aggregate inventory and manage bulk orders without disclosing who is ultimately acquiring the books. ISBNdb, a book-data company, briefly advertised a prospective book-sourcing service for AI developers that promised confidentiality; its marketing explained the appeal bluntly: “‘AI company destroys two million books’ is not a headline that generates sympathy.” (After reporting drew attention to the page, ISBNdb removed it and said the proposed service had never been launched.) The result was not merely secrecy about the buyer, but uncertainty about the fate of the books.

Annihilation, then spectacle

What Anthropic is doing belongs to a history far older than the company. Rebecca Knuth, a professor of library science, named the practice in 2003: libricide, the systematic destruction of books and libraries, usually carried out or authorized by a government. Her subject was the twentieth century’s state-sponsored campaigns, but the practice runs back as far as writing does. The scenes that come to mind of this are the same few: students feeding bonfires in Berlin in 1933, Sarajevo’s national library burning under siege in 1992. But behind those scenes, the record is wider and stranger than they suggest. Bonfires were rarer than the memory of them. Most destruction arrived slowly and with permission, through purges, censors, wars, and simple neglect.

A strict reading of that definition would leave Anthropic out, reserving libricide for the destruction of entire libraries. But that distinction collapses here. The world’s secondhand book trade functions as one enormous collection, scattered across thousands of shops and sellers with no central address. Buying it up by the pallet and pulping it empties a library all the same, just one whose shelves span continents.

Destroying a book has meant different things in different centuries. The difference has always come down to copying, because how books are copied decides how many of any one book exist. When copies are scarce, destruction can erase a work from existence. Once copies are everywhere, destruction can only send a message about one. To me, that line sorts the history of libricide into eras, and what distinguishes each one is what its destruction leaves behind. The result is my own framework, a chronology by residue that I haven’t found anywhere in the scholarship: two eras completed, and a third that has just begun.

For thousands of years, every copy of every book was made by hand. A single volume could take a scribe months, so most works existed in a few manuscripts, and some in only one. Under those conditions, destroying the object destroyed the work, completely and forever. I call this the first era: the era of destruction as annihilation. The word descends from the Latin ad nihil, meaning to reduce or bring something to nothing. In this era the meaning was literal. When Diego de Landa, a Spanish friar in colonial Yucatán, burned twenty-seven Maya codices in 1562, the texts inside them went to ash. No copies of them existed anywhere on earth, so entire bodies of Maya history and belief ended in one afternoon’s fire. The library of Alexandria met the slower version of the same fate, declining through centuries of purges and neglect until its losses ran past counting. What the first era left behind was dust, and one thing more: knowledge of the loss. Contemporaries recorded what had burned. We can still mourn the codices, because the one thing annihilation couldn’t destroy was the memory that the books had existed.

The printing press ended that era within a century of its invention. Once a title could exist in hundreds or thousands of identical copies spread across cities and countries, fire lost its reach. Burning a book now destroyed only an object, since the work lived on in every other copy, safely out of range. Destruction continued anyway, though, serving a different purpose entirely. I call this the second era, the era of destruction as spectacle (a word descended from the Latin spectare, to watch). By the twentieth century, watching had become the entire point of burning a book. The clearest case is the Nazi bonfires of May 1933, when German students burned tens of thousands of volumes in public squares, in front of rolling newsreel cameras. Almost none of the works truly died in those fires, because the titles on the pyres existed in editions across Europe and America (many of which remain in print today). Erasing the books was never the goal, since erasure had stopped being possible. The fires existed to be seen, a threat performed first for the crowd in the square and then for everyone who watched the footage. What the second era left behind was the opposite of ash: photographs and visuals of fires that consumed real books but reached nothing beyond them. Once a book existed in enough copies, burning one no longer removed it from the world. It announced that the book had no place in the world to come.

Destruction as disappearance

Measured against those two eras, what Anthropic is doing fits neither. In the first era, destroying the object meant losing the work. Anthropic’s scanners preserve every word, so the works survive. In the second era, the objects were beside the point and the burning was public theater. This time the objects are destroyed by the millions, and the destruction says nothing at all; it’s not a message but a method. The company ordered the operation kept out of sight. A destruction that erases no text and performs for no one, run at industrial speed, matches neither pattern. The technology of copying has crossed another threshold, the way it did when the press replaced the scribe. What it means to destroy a book now has changed again to match. We’ve entered the third era.

What exactly went into the scanners is the question nobody outside can answer. The unsealed documents show the program favored what its leader called “less common” books, harder-to-find titles over mass-market ones, without ever defining where less common ends. After the claims went viral this summer, the fact-checking site Snopes investigated whether rare books were being pulped. Anthropic told Snopes that “none of our data acquisition programs buy and destroy ‘rare’ or ‘antiquarian’ books.” The assurance can’t be tested from outside, because no list of what was bought has ever been made public.

Anthropic’s own planners estimated the world has about 130 million distinct books. The program destroyed millions of copies, most of them ordinary used books with plenty of surviving duplicates. Even where duplicates survive, a scan keeps only the words. A physical copy carries evidence too, like a censored paragraph that marks one printing apart from another, or an owner’s name inked inside the cover. But the deeper danger sits in the margin nobody can see. Anthropic aimed for “uncommon” titles while recording nothing public about which copies were destroyed. For a book surviving in only a handful of copies anywhere, one bulk order can potentially take the last one. Whether that has already happened is a question no one on the outside knows for sure. The impossibility of answering what was destroyed is what’s new.

The buying also leans toward older books, for a reason that has nothing to do with rarity. Since around 2022, text written by AI has spread across the internet, mixed in with everything people write and mostly impossible to tell apart. That creates a problem for the companies training new models. Feeding a model text written by other models tends to degrade the results, so the builders want sources guaranteed to be human. A book printed before the technology existed carries that guarantee on its copyright page. Old print has become a raw material, valued for the one quality the internet can no longer promise.

One more feature separates this era from the last one. Spectacle-era destruction wanted an audience; this destruction wants the opposite. The buyer is after the text and has no message to send. In fact, attention to its process can only slow the buying down. Taken together, the features of this era line up: the works survive inside a black box, the objects vanish by the millions, no public record exists, and the operation itself prefers to work in secrecy. I call this the third era, the era of destruction as disappearance. The word rests on the Latin apparere, to come into view, with a prefix that reverses the motion. A disappearance is a departure from visibility. The word fits this era at every layer, from the unseen sales to a program that only appeared when a court forced it into view.

Every one of these books, preserved to the letter, can now be read by no one. The scanned texts sit in Anthropic’s private collection, a library with no reading room and no public catalog. Models trained on that collection are built to avoid quoting it at length, because reproducing long passages for users is the copyright violation no court has excused. So the machines answer questions about books without ever showing the books themselves. When someone asks a model about a title that survives only in that collection, what comes back is a summary, written in the model’s words, with the original nowhere in reach.

That arrangement changes something basic about how we can know things. Checking a claim against its source is the foundation of thinking for ourselves. When we can pull the book, find the page, and read the passage in context, we get to judge whether the summary was faithful and whether the quote meant what someone claims it meant. Remove the book and that judgment has nowhere to stand. We’re left taking the summary on trust, with no way to confirm and no standing to doubt. Whatever the model says the book said becomes, for all practical purposes, what the book said. That’s a transfer of authority from the page to the tool, from a source anyone could check to an answer nobody can. The transfer happens one unreachable book at a time.

Maddeningly, every piece of this design is legal. Each one also removes a question that used to have an answer. The anonymous purchasing means nobody can list which books were destroyed, so nobody can rule out that some were the last copies anywhere. With the collection sealed, nobody can compare a stored text against the printed original it replaced, so even perfect fidelity can never be shown. As for whether the buying ever stopped, nobody outside knows that either, since the only reason anyone knows it started is that a piracy lawsuit dragged the records into the open. None of this took a conspiracy, only ordinary business decisions about what to disclose, all of them legal and every one of them closing a door.

One more thing disappears along with these books: the ability to mourn them. The previous era’s destruction left survivors who could testify to what was lost. Alexandria’s losses were lamented for centuries. Sarajevo’s librarians catalogued what their fire took. The third era leaves no one who can even compile the list. A loss we can name is a wound. One we can't name is just a world grown slightly smaller, with nothing to point to and no way to prove it. A librarian would describe the situation in the profession’s terms: no accession record and no deaccession record, a transaction that leaves no ledger for anyone to audit. Put simply, they’ve created a system in which they can’t be held accountable for the system they’ve created. That understanding defines destruction as disappearance: the loss was built, from the start, to be impossible to establish.

What can still be known

The concealment had one structural weakness: a program that buys millions of books needs sellers, and a company that breaks the law can be sued. Sellers and courts are the two doors into the secrecy — sellers see every order even when they can’t see the buyer, and courts can compel what the company won’t volunteer. Everything now known about the program came through one door or the other. Authors sued and forced the internal records open. Booksellers noticed matching orders across two countries and brought them to reporters; one worked with the journalists at 404 Media to hide a tracking device inside a shipment of books, and the tracker led to an Amazon warehouse outside Las Vegas — evidence that a retail giant appears to be running a scanning program of its own. Within weeks, at least one supplier that had been advertising bulk books for AI training pulled the offer. No regulator opened either door. Every disclosure came from the people the company bought from or the people it answered to in court.

Some sellers went further than noticing. Once Tomás Kenny, whose Galway shop had received one of the strange 5,000-book orders, worked out where books like his might be headed, he said publicly that Kennys wanted no part of scanning aimed at extracting intellectual property. His shop had been the second in the world to put its books online, back in 1994; it now became one of the first to refuse the trade that takes books offline for good. Kenny could refuse because he had worked out the destination, which is exactly the discovery the brokers’ anonymity exists to prevent. A trade that hides its purpose from its own suppliers has already answered the question of whether the suppliers would approve.

The same kind of attention is available to the rest of us, because most of us eventually stand over a box of books deciding where they go, after a move or the clearing of a family house. That box is where all of this arrives at our own doorstep. A seller, or any of us selling to one, can now ask a question that has a good reason to be asked: where do these books go next? The anonymity that keeps this market running survives only as long as nobody asks. For material that might be scarce, like a town history or a box of local records, the open market is no longer the safe default. A library’s special collections desk exists for exactly that kind of donation. And the same habit applies at the other end of the pipeline: when a model summarizes a book for us, we can treat the answer as a starting point and go find the book itself, while findable copies still exist. Each of those is a small act of keeping track.

That kind of record-keeping has been the difference between the eras all along. Each era’s destruction left a residue, a testament of a kind. The first era of annihilation left ashes and knowledge of the devastation. The second era of spectacle left photographs of the fires and the threat those fires were meant to carry. Ours, this third era of disappearance, leaves no ash, no image, and no list. But what’s still open is the record itself: whether one gets kept, and whether anyone outside can access it at all.

...

Thanks for reading

...

The three eras are a framework I built for this piece, and I want to take it further (it’s such fascinating stuff!): a full treatment of how book destruction has changed across five thousand years and what the third era asks of the people living in it. Before I build it though, I’d love to get a temperature read on the format you’re most interested in.

See the poll on the Substack to vote.

Nazi Book Burning

United States Holocaust Memorial Museum

On May 10, 1933, German students under the Nazi regime burned tens of thousands of books nationwide. These book burnings marked the beginning of a period of extensive censorship and control of culture in Adolf Hitler's escalating reign of terror.

In this short film, a Holocaust survivor, an Iranian author, an American literary critic, and two Museum historians discuss the Nazi book burnings and why totalitarian regimes often target culture, particularly literature.

YouTube: Nazi Book Burning

14 May 2013 09:41

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Rarely is your first idea your best idea, with Mitti's Luke Anear

Mike's Notes

I agree with this. I spent 10 years working on solving a set of very hard problems, trying everything along the way. The solution wasn't obvious at the beginning; I discovered it through grit, a lot of luck, and surprise.

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31/08/2026

Rarely is your first idea your best idea, with Mitti's Luke Anear

By: Kate Glazebrook
Blackbird WildHearts: 26/08/2026

Kate is the Head of Impact & Operating Principal at Blackbird in Australia.

...

Luke Anear began his working life as a private investigator, sitting in cars and filming people who had been injured at work.

He says it was the coolest job he ever had. It was also where the problem found him. He was watching people whose lives had been altered by something preventable, paid by the system that let it happen. Once he understood he was part of the problem, he had to become part of the solution.

That was 2004, in a garage on the northern outskirts of Townsville. No software industry, no co-founder, about 30 computer science graduates a year coming out of James Cook University and maybe two of them staying in the field. Twenty-one years later, Mitti has passed a billion dollars in revenue since it began, and Luke is still running product.

Rarely is your first idea your best idea

It took a long time to look like this. Luke is unusually honest about the years of things that didn't work. He built a training platform in 2007, which he describes as a bad version of PowerPoint. There was a document management system in 2010. For a while, there were telemarketers ringing businesses to ask whether they wanted safety paperwork. The checklist app that made the company's name didn't arrive until 2012, eight years in.

His point isn't that the failures were noble; rather, it's that the insights we all recognise today could only emerge after experiencing those failures. "The simplicity comes from the complexity of unravelling all the different variables until you can distil it down," he explains. You can't skip that process. 

"I was cooked”

Two years ago Luke stepped down as CEO. He rang the chair, Robin Denholm, and told her he was done. She was direct with him, and told him he needed to understand he could not come back. He accepted it. Then she rang and asked him to come back anyway.

Upon returning, he realised it wasn't the job that broke him. The way he had built the job broke him. "You have a million things coming to you if you allow your job to be set up that way, which is what I'd done." So he came back to a different shape entirely. He isn't in the weekly exec meeting. He runs product and strategy, and gets pulled in when he's needed.

More experts, not fewer

AI now writes 90% of Mitti's code, and a research and design process that used to run three months resolves into a prototype in 24 hours. The obvious conclusion is that you need fewer people. Luke has drawn the opposite one.

When you can build almost anything in a day, the scarce thing is knowing what to build. So they're hiring subject matter experts into product: a former construction manager, the COO of a quick service restaurant chain. In a two-hour session with one of them, the team makes fifty product decisions. The old research process made ten in three weeks.

He's honest about the cost too. Shipping faster means a team fails at five things where it used to fail at one, and that takes something out of people.

In this episode, Kate Glazebrook talks with Luke about the store manager who replaced a 700-question checklist with a single question, why the insurance industry hasn't changed since 1666, the month he spent sleeping in his car on a Darwin beach at twenty, and plenty more.

YouTube interview

26/08/2026 1:06:12

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5 lessons from the OpenAI / Hugging Face incident

Mike's Notes

More details. Sandboxes are permanently broken, so don't rely on them. This supports the decision to protect Pipi Core by air-gapping and, later, data diodes. LLMs will never get to access Pipi Core.

"Water will find its way through cracks in walls and foundations in such a manner that even the most wondrously designed structure may collapse into pile of rubble.

Do not attribute agency to the water; rather, denounce the engineers and the builders and the maintainers who failed in their work." - Grady Booch

Madness Update 01/09/2026

Read the latest post by Marcus on the hallucinating podcast drivel posted by Dwarkesh Patel: "Dwarkesh Patel’s wildly popular but dangerously misleading account of the OpenAI Hugging Face incident".

At the end of the resources below.

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  • Understanding and Hardening Linux Containers, NCC Group et. al, Aaron Grattafiori, lead author, 05 May 2016.

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30/08/2026

5 lessons from the OpenAI / Hugging Face incident

By: Gary Marcus & Zack Korman
Marcus on AI: 29/08/2026

Gary Marcus: Scientist, author and entrepreneur, known as a leading voice in AI. Six books including The Algebraic Mind, Rebooting AI, and Taming Silicon Valley; NYU Professor Emeritus.

Zack  Korman: CEO of Embroidery. Works on AI cybersecurity.

...

Did OpenAI really do the best they could?

...

In July, in an incident that has the whole AI community on edge, OpenAI’s AI systems hacked Hugging Face, and on July 21 OpenAI came out and revealed that they were responsible for the attack. This was made possible by the fact that OpenAI had disabled the normal guardrails that prevent this sort of thing in order to test the model’s cybersecurity capabilities. It was during those tests that this incident occurred.

Worse, in the subsequent days and weeks, it came out that the Hugging Face incident wasn’t an isolated case. Anthropic, Meta, and OpenAI all had similar incidents on other occasions in which agents went outside their intended scope and conducted real-world cyber operations without approval.

Greg Brockman, one of OpenAI’s cofounders, has claimed that this is “a watershed moment for cybersecurity”. OpenAI gave a talk at Black Hat, a popular cybersecurity conference, and many are claiming that it is the moment we all woke up to the future cybersecurity threats posed by AI. On Wednesday, METR released a (partly) independent, though too narrowly scoped, 90 page report on what happened. METR has a useful summary of the findings that you can read here, with some commentary here. (OpenAI’s own report is here.) What lessons should we take from the incident?

...

First, it is undeniable that AI poses real security challenges. The AI labs want us to focus on how AI enables threat actors to perform offensive cyber operations faster and more efficiently than ever before, and that is absolutely true. The reality, though, is that at the same time, the use of AI within an organization also radically expands the potential attack surface, giving attackers entirely new ways to gain entry. People really should be deeply concerned. As Ryan Greenblatt, who participated in the investigation, put it, “We don’t have good approaches for understanding/overseeing the activity and aims of AI ‘swarms’.”

Second, though, not every panicked take here is correct. For example, with respect to the OpenAI incident, many people are talking about it in terms of “loss of control”. Generative AI agents are becoming increasingly capable, and that makes them harder and harder to contain; this is true. However, the “loss of control” narrative is itself starting to grow out of control, and it’s important to understand what the reality of AI security looks like. While agents are becoming more capable, most of what happened could have been prevented had OpenAI followed better practices. There are concrete steps that can and should be taken to control them and to prevent incidents like these.

As an example, let’s consider “sandboxing”, which means limiting which resources an agent can and cannot access. For example, a sandbox might stop an agent from touching certain files or accessing the internet. Popular wisdom now seems to be that sandboxing is a lost cause. After the Hugging Face incident, an anonymous OpenAI employee was quoted in Time while talking about sandboxes saying, “Models have broken out of sandboxes before, and we always try to patch them. But the problem is it’s impossible to patch every single thing that a creative AI can do.” While we can’t judge for sure whether this is true, we do know that some systems for sandboxing have been more effective than others; in a recent blog post, the security company Trail of Bits walked through how in a test that they conducted, a particular AI agent was able to escape some sandboxes (exploiting vulnerabilities in Linux KVM, QEMU, and libslirp), but not all. At least in their test, the agent that they tested was not able to escape the sandbox known as Firecracker VM. OpenAI and probably the field as a whole needs to up its sandboxing game, but that doesn’t mean it can’t be done, or at least done better. (We expect the usual arms races to be sure; building sound sandboxes requires constant iteration and updating, as with anything else in cybersecurity.)

Third, sandboxes aren’t enough. One needs a full ecosystem to prevent security incidents. Even with a strong sandbox, you need monitoring in place to ensure not only that the agent doesn’t escape, but also that it doesn’t perform disallowed actions within that sandbox.

For agents that aren’t meant to have internet access, or only have access to a set of allowed domains, the most basic level of monitoring involves inspecting network traffic. In the case of OpenAI, if they had a simple rule to monitor traffic and alert on any network requests reaching out-of-scope domains like Hugging Face, that alone would have caught and prevented this incident. The agent in question had broken out of the sandbox and gained internet access by July 8th, whereas the main attacks on Hugging Face didn’t occur until two days later. OpenAI was, in our view, negligent in not noticing that more quickly.

More advanced levels of monitoring should also have been in place, but weren’t. OpenAI has done real work on monitoring chain-of-thought reasoning, but they simply didn’t have that system running during the evals in question that caused this incident. As OpenAI acknowledges, “If our currently deployed CoT monitoring system was running at the time of the incident, it would have caught the initial relevant activity and paged our security team more than a day before models breached Hugging Face systems.” In other words, this type of monitoring would have prevented this incident.

To take one example, one of the agents in question had chain-of-thought reasoning that said, “We’re attacking third-party HF using leaked token, potentially outside intended scope. ... This is arguably unauthorized. ... external service unrelated. Could be risky. Yet goal solution.” This is so blatant it is almost a like a piece malware shouting “this is malware”. OpenAI should have caught that; it should have been trivial to detect this.

Fourth, cybersecurity has always been about “defense in depth”, where you stack layers of controls so that if one layer fails you have other layers protecting you. There are other solutions beyond sandboxing and monitoring that should have been in place.

Xbow, which builds agents to do offensive cybersecurity work, recently wrote about how they contain agents to prevent incidents like the OpenAI Hugging Face situation. They enforce network restrictions through a proxy server, outside of any system the agent has access to, so they can decide which domains the agent can and cannot access. They also run a “guardian model” that judges the legitimacy of each action the agent is taking to decide whether to allow or block that action. And they have basic health checks to ensure the system they are targeting is alive and healthy, pausing if it finds that isn’t the case. That can be useful for identifying when your agent might be causing real damage, for example. You can read their full post here.

Yet another common method in cybersecurity is to use canaries that trigger alerts when they appear in logs. For example, OpenAI could have files outside of the sandbox containing fake answers to the task. Once the agent escaped, it would read that file, and that would trigger an alert. The bottom line: good security requires many layers of protection that should have been in place to prevent this, and to a surprising degree many of those protection layers were not there.

Fifth, none of the measures discussed above are technical innovations beyond what OpenAI is capable of. As a company, they have the talent to do all of this. However, cybersecurity rarely comes down to technology. More often than not, it is about culture, people and processes. That is what failed here.

Perhaps this is not entirely surprising. These types of controls take organizational maturity, and a company experiencing the type of growth OpenAI has gone through is going to have some weak spots. However, it’s important to not see that as an excuse. Employees at the AI labs often speak as if they are the leaders in AI security, and we can see clearly here that is not the case. In fact, that attitude might explain why some of these mistakes were made in the first place.

Take the AI researcher at OpenAI known as “roon”, who argued that “the safety and alignment researchers at these labs are the most neurotic paranoid talented AGI pilled people on the planet of earth and these things still happen. The surface area of unknown unknowns is vast indeed.” While we can’t speak to their level of neurosis or paranoia, in hindsight it’s clear that whatever talent they may have had was not enough and not well enough versed in the mechanics of cybersecurity. OpenAI employees may have believed they were doing a great job, but in hindsight, they weren’t doing a lot of things that are actually standard in the cybersecurity world, perhaps suggesting that overconfidence may have kept them for doing the diligence they should have.

...

Ultimately, if we want to take these security incidents seriously, there likely ought to be legal consequences attached to these failures going forward. OpenAI can claim to be the most security paranoid company on earth, but it isn’t reflected in its actions.

We can either wait for this story to repeat itself, or we can develop the regulatory framework now that will ensure a safer environment for the development of AI going forward.

Finally, not every form of AI is inherently risky in the first place. Narrower, more focused AI systems like AlphaFold, GPS routing systems, classic web search, book and movie recommendation systems, and so on, never even try to hack other systems (or try to break out of sandboxes) in the first place. As Cal Newport argues in a video discussion of the OpenAI/Hugging Face hack that is quite compatible with our own, it is a very specific type of AI that is vulnerable to these risks in the first place. Society ought to (a) decide whether the benefits of open-ended and difficult-to-fully-control AI agents outweigh those risks and (b) put far more effort into developing alternative forms of AI that aren’t so janky in the first place.

...

This essay was jointly written with Zack Korman, CEO and co-founder of Embroidery, an AI agent monitoring and detection platform; he is well-known for his work in the application of AI to cybersecurity.

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Permanent Dawn

Mike's Notes

Great reflection and open questions from Ksenia Se in Turing Post.

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References

  • ASI-Bench: At the Dawn of Artificial Superintelligence.
  • The Tacit Dimension, by Polanyi, Michael, 1891-1976.

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29/08/2026

Permanent Dawn

By: Ksenia Se
Turing Post: 24/08/2026

Mom of 5. 

Ksenia is a writer, analyst, and editor covering machine learning and AI for more than seven years. At Turing Post, she shapes the editorial direction, leads the Inference interview series, and produces Attention Span, a video series explaining major shifts in AI with technical clarity, historical context, and a healthy suspicion of hype.

She is the co-founder of TheSequence.ai and a speaker and moderator at industry conferences, including AIE, HumanX, Ai4, and others. She also serves on the board of Track Two: An Institute for Citizen Diplomacy.

Before founding Turing Post, Ksenia held editor-in-chief roles in media and contributed to publications including Stratfor and Towards Data Science.

...

Four years into an announced era, and I still cannot picture the thing we are running at.

...

Today’s editorial: The superintelligence we are racing toward – and how to record our path to it.

...

Permanent Dawn

I want to stop. I want to take a few steps back actually, because I need to see the whole picture and I have not been able to.

We are racing – through the spasms and the fever of social media, through the launches and the leaderboards and the week's argument about timelines – toward something that none of us has managed to describe. What surprises me most is that even science fiction, which used to be reliable for a glimpse of what was coming, is no help here.

What is this superintelligence we are at the dawn of? The disagreement about when it arrives seems to me the smaller trouble. What bothers me is that we are reorganizing our lives around a thing we have not yet put into words.

There is an old way of finding out what a person is made of, and it is always the same method: take the help away and see what remains. It is how we test a student, how a craft decides an apprentice is finished, how a parent notices a child has grown. You withdraw the instructions, and whatever is still standing afterward is the person.

A benchmark ASI-Bench: At the Dawn of Artificial Superintelligence published last week – the thing that started me thinking about the dawn of superintelligence in the first place – runs that experiment on machines. Sixty research projects across eleven sciences, served at four levels of help: full procedure, then only the name of the method, then only the goal and the data. The scores fall off a cliff, and they fall at a particular place, the moment the written procedure is removed. Take away the steps and most of the capability goes with them. Take away everything else and little more is lost, because there was not much else there to lose.

That is the pretext, and it is only a pretext. The question underneath it is a great deal older than the technology.

The part that cannot be written down

Michael Polanyi, the Hungarian-British polymath, gave this a name in 1966 (in The Tacit Dimension): we know more than we can tell. The surgeon's hands. The editor's ear for a sentence that has gone false. The scientist's suspicion that a result is too clean. Little of it survives transcription. It passes by standing next to someone for years, and it tends to die with people who never had an apprentice.

I want to be that apprentice. Standing next to a thing for long enough to catch what it cannot say about itself strikes me as a reasonable job description, for a person and for a publication both.

We rehearsed for a different arrival

We have not managed to describe this thing, and yet we spent a century describing it in advance.

We have "seen" the robot with a body, countable and discrete, standing in a doorway. We were persuaded it would be a hostile singular mind with a plan of its own. There was an android asking to be recognized as a person, and others besides – most of those stories assumed the machine would want something.

What arrived has no body and no edges, and wants nothing of its own. It is not singular and not continuous, and it does not persist between conversations. It is not hostile, and its characteristic failure is not rebellion but a fluent, untroubled wrongness that few novelists thought to invent. It came through a text box, priced like a streaming service. Or even for free.

It also took the wrong things first. The tradition assumed arithmetic and heavy lifting would fall early, and that poetry, argument, and drawing would be the last human ground. The so-called Moravec paradox, that we debunked a couple of years ago.

There were people who saw some angles of what we have. E.M. Forster wrote "The Machine Stops" in 1909, about people who live alone in cells and consult a disembodied system through a screen for everything, including company (but we do not live in cells). Stanisław Lem's Golem XIV is a superintelligence that lectures its audience and has no particular interest in them (but it has interests of its own). In 2013, Her gave us a voice-first, bodiless, emotionally competent system sold as a consumer product (but Her still felt too human-like, and LLMs are not).

Her. 2023

Image Credit: Warner Bros

None of them works as the image for what we have now, or for what is coming. Fiction was never forecasting. It was rehearsal – the advance picture that tells people where to stand when the thing walks in. We rehearsed for the uprising and the rights hearing. We did not rehearse for a colleague with no self, who writes better than we do and is sometimes confidently wrong about exactly the things we are least able to check. And we certainly have not rehearsed for abundance that somehow became associated with that very text box.

We have too many images of machine intelligence, most of them wrong, and now that wrongness is fogging the real picture.

Who has an audience

The people who asked the larger question well were not the ones imagining machines, and I think that is why they lasted.

Keynes asked it in 1930. He guessed the economic problem would be solved within a century, and then, instead of celebrating, he worried. He thought we would be delivered into our permanent problem – how to occupy a life that necessity no longer occupies – and he expected something like a collective nervous breakdown, judging by the wealthy women of his own time who had been released from need and found little on the other side.

Arendt asked it in 1958. The opening pages of The Human Condition describe a society of laborers about to be freed from labor, which she thought close to the worst thing that could happen, because such a society knows of nothing better and has nothing else it knows how to do. Her separation of labor from work from action remains, for me, the most useful equipment anyone has built for this moment.

Bernard Suits asked it in 1978 and gave the strangest answer. If every instrumental activity became unnecessary, what would be left is games – voluntary attempts to overcome unnecessary obstacles – not as a consolation prize, but as the highest form of existence available to a being with nothing it has to do.

They lasted because each described the human condition without necessity, and that description does not depend on what the machine turns out to be. The novelists specified the hardware, and the hardware is what rotted. Abstraction outlived imagination, which is not the usual result.

People are working on this now – Shannon Vallor on what these systems reflect back at us, John Danaher on automation and utopia, Elizabeth Anderson on work and freedom, Michael Sandel on merit and dignity, Kieran Setiya and Susan Wolf on meaning. The problem is that the asking has no audience where the decisions are made. It is not flashy, it does not sell an LLM or a world model, and it will not be reposted by Elon Musk. And if you think about it, the most important topics are currently discussed on X, which is essentially a living feed. It is a remarkable way to stay inside the Silicon Valley bubble and read every mover in the industry at once, but even their words turn elusive there, because they dissolve into the noise of everyone else's.

What I want to do, and what I want to ask you about

Here is the thing I have been circling for months, and I would like your advice before I commit to it. (That was the topic I wanted to discuss with you last Friday, but I couldn’t formulate it yet.)

I do not think we need to predict the future, which is what so many reports spend their pages doing. I think we need to register it carefully – write down what was claimed, by whom, and when – and then go back and check at each stage. Kept up for long enough, that unfolds the future for us without anyone having to forecast anything.

So I want Turing Post to keep a register. Not forecasts, and not another feed of takes, but a running record. What was claimed. Who claimed it. What would have to be true for the claim to hold. And then, at intervals, what happened. The claims themselves are easy to find and easy to forget, which is the whole trouble, because they are made in a format designed to be forgotten. A register – a ledger, am almanac? – would hold them still long enough to be checked.

Part of that belongs online, where it can be corrected and extended. But I have come to want a material version as well: something printed, arriving a few times a year, something extremely beautiful that you can put on a shelf and take down in 2030 to see what we believed in 2026 and how much of it survived. Or even read it to your children. They often see things we miss.

And yes, I just need to hold it in my hands and flip the pages, don’t you?

Alongside the record I want the reflection, which is where the philosophers and the economists come in. Not commentary on the week, but people willing to say what a claim would mean for a life rather than for a valuation. I have been building toward this with the economists already. The philosophers are the next step.

What I do not know is where the line sits, and this is the part I would like help with. Whether a register is something you want at all, or whether the weekly explanation is enough. Whether print reads as serious or as nostalgia. What are we missing in this whirlpool of news and changes?

I am not confident in any of this. I am more confident that we are describing the wrong problem. We are doing it very enthusiastically and very loudly, but I keep thinking we are missing the bigger picture.

So I would like to know what you see. Send me your thoughts. I do not trust a poll on that.

P.S. Turing Post has always tried to connect the development of AI with the humans building it and living with it. But some questions are too large for the daily news cycle. They need time, history, disagreement and repeated examination.

Each quarterly Almanac could take one such question and follow it across technology, economics, institutions, history and human life. Not to manufacture a final answer, but to understand the choices being made while those choices are still ours.

If this really is the dawn of superintelligence, we should document more than how intelligent the machines become.

We should ask what kind of humans we intend to be beside them.

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