Chapter 1: The Prayer You Barely Say Out Loud

You sit on the edge of the bed with your shoes still on because taking them off somehow feels like one more task. The room is quiet except for the air conditioner, and tomorrow’s clothes are draped over a chair. You have heard people say that God is about to do something big, but tonight the words do not excite you. The feeling underneath this quiet video about what Matthew 16 reveals when God is building through you is the private fear that hoping again will only give disappointment another place to land.

You may not say that fear during prayer. You may use safer words and ask only for enough strength to get through the week. In this honest Christian reflection for anyone afraid to hope again, there is room to admit that a large promise can feel heavy when your heart is already carrying the memory of what did not happen.

Matthew 16 meets us in a surprisingly simple sentence from Peter: “You are the Christ, the Son of the living God.” Peter did not understand the road ahead. He did not know how badly he would fail or how much his life would change. He only knew who Jesus was in that moment, and Jesus treated that confession as something upon which real work could move forward.

That helps me because faith does not always feel like confidence about the future. Sometimes faith is only the refusal to walk away from Jesus when you cannot imagine what He might build. You do not need to create a bright picture of tomorrow. You can sit in the quiet room and tell Him, “I still believe You are who You said You are.”

A woman waiting for test results may not have the strength to pray for a dramatic outcome. She may only whisper the name of Jesus before answering the doctor’s call. That prayer is not inferior because it is small. It places her fear in the presence of Someone larger than what she knows.

Maybe the big thing begins there, before the plan, energy, or excitement returns. It begins with Jesus becoming more trustworthy than the future is predictable. You are not being asked to deny how tired you are. You are being invited to let one honest confession remain standing inside the weariness.

Chapter 2: The Work May Begin With the Apology

The kitchen is finally quiet after an argument, but one cabinet door is still open and a chipped mug lies in the sink. You replay what you said and keep finding ways to justify it. Part of you wants to pray about your future, your purpose, and the important work you hope God will give you. Another part knows that the next faithful thing may be walking down the hall and saying, “I was wrong.”

That can feel painfully small beside the thought of God building something big. We prefer a calling that lets us move forward without returning to the harm behind us. Yet Matthew 16 does not leave Peter inside his strongest moment. Soon after his confession, Peter resisted what Jesus said and was corrected. Jesus cared too much about Peter’s place in the work to protect him from the truth.

I think we sometimes imagine that God will use the gifted part of us while politely avoiding the wounded, defensive, or proud part. But Jesus does not build by separating our public ability from our private character. He brings the hidden room into the same light as the visible assignment.

The apology in the hallway may not repair everything immediately. The other person may still be hurt. You may need to listen to details you would rather explain away. Repentance is not a quick sentence used to clear your conscience. It is the willingness to stop defending what love requires you to change.

A father may dream about starting a ministry while his son has learned not to bring him difficult questions. The dream might be sincere, but the distance at home is also real. Before reaching strangers, he may need to sit beside his son, put the phone away, and remain present through an uncomfortable silence.

None of this means God’s larger work has vanished. It may mean He is beginning closer than you expected. The thing He builds with you must also be allowed to change you. Sometimes the first stone set in place is not a public opportunity. It is the honest sentence spoken in a quiet hallway when no one else is there to admire it.

Chapter 3: The Quiet Work Beyond the Screen

A writer closes the statistics page after seeing that few people read the words he spent all weekend preparing. The house is dark, and the refrigerator hums. He wonders whether continuing is faithfulness or an unwillingness to admit the work is going nowhere.

Then a message arrives from a reader who had been close to giving up on prayer. It does not make the numbers impressive. It reveals a life behind one of them.

Matthew 16 does not teach us to call every dream successful. It shows us where confidence belongs. Jesus is the Christ and the Builder. We can examine our work honestly, change direction when needed, and refuse to measure obedience by immediate attention.

You may never see the complete reach of what Jesus does with your surrendered life. An apology can change a home. A confession of faith can steady someone facing fear. A few honest words can meet a reader when they need them.

Something big does not always arrive making noise. Sometimes it begins when you keep your heart open after disappointment, accept correction, and offer Jesus what remains. The result is not yours to inflate or control.

For tonight, that can be enough. Close the statistics, turn off the light, and rest without calling the day wasted. Jesus knows what reached another life. He also knows what He is still building in yours.

Your friend, Douglas Vandergraph

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There is a moment, described more than once by the people who have lived it, when the correction stops being needed. You are a speech and language pathologist, say, and you have spent a fortnight teaching a language model how a sentence should breathe in Brazilian Portuguese, how a piece of creative writing earns its emotional turn rather than announcing it. You mark the model's output. You explain, patiently, why the phrasing is wrong, why a native ear would flinch. And then one morning you sit down to the same task and find you have nothing to say. The model has absorbed you. The thing you were correcting a week ago it now does on its own, in your voice, without you. Carolina Perez Sands did exactly this work for the artificial-intelligence training company Mercor, and she described the arc of it to The Wall Street Journal's podcast in June 2026. Her corrections, she said, became unnecessary within roughly a week. She has since left the industry, having reached a conclusion that ought to be printed on the wall of every laboratory in the sector. Her job, she decided, was “actually making this more of a monster.”

That sentence is the whole subject of this essay, and it is worth sitting with before we reach for the economics. She did not say the work was hard, though it was. She did not say the pay was poor, though for many in the sector it becomes so. She said the work was monstrous, and she meant something precise by it: that the better she did her job, the less of her there was left to do. She was not being replaced by a machine in the ordinary sense, the sense in which a loom replaces a weaver or a spreadsheet replaces a clerk. She was pouring herself into the machine that would replace her, decanting a career's worth of tacit judgement into a system engineered to make that judgement free and infinite. And she was being paid, by the hour, to do it.

The supply chain of the mind

The company that paid her is three years old. In July 2026 The New York Times, in a report by Lora Kelley bluntly titled “The Work of Helping A.I. Destroy Work,” laid out the scale of the operation with a single arresting figure: every day, Mercor pays more than thirty thousand contractors upward of four million dollars to help make their own jobs, and the jobs of their colleagues, obsolete. This is not the old data economy of blurred street signs and flagged slurs. The postings the Times examined read like a fever dream of the professional class: two hundred and twenty-five dollars an hour for a voice actor who could hold a customer-service persona in fluent Hebrew, a doctorate-holding physicist with a specialism in general relativity, astrophysics or cosmology, physicians who could describe the texture of primary care in Rwanda. Mercor supplies mathematicians to annotate formal proofs, lawyers to mark up briefs, professors to grade essays. Data labelling, as the Times put it, has moved up the value chain.

The money follows. Mercor was valued at ten billion dollars in October 2025 after a funding round of some three hundred and fifty million, an event that made its three founders — Brendan Foody, Adarsh Hiremath and Surya Midha, Thiel Fellows all, and none of them much past twenty-two — among the youngest self-made billionaires alive. By July 2026 Bloomberg, Forbes and TechCrunch were reporting the company in talks to raise a further five hundred million dollars or so at roughly double that valuation, twenty billion, on the back of a gross revenue run rate that had itself doubled, in the space of about four months, to reach two billion dollars a year. The doubling is the fact worth holding on to: the valuation doubling in nine months, the revenue in four. The founder Brendan Foody has offered his own version of the daily wage bill, putting it at over one and a half million dollars a day; the Times, counting more broadly, put it above four million. Either way the arithmetic is the same in shape. A very small number of very young people have built an extraordinarily valuable company whose principal activity is buying, by the hour, the accumulated expertise of tens of thousands of highly credentialled professionals, and selling it onward to the laboratories building the models that will render that expertise abundant.

It is worth being clear about how new this is, because the novelty is the whole argument. The AI industry has always rested on hidden human labour. For most of the last decade that labour was cheap, distant and largely invisible: the workers in Nairobi and Gulu paid a dollar or two an hour by outfits contracting for Scale AI and OpenAI to tag images, moderate horrors and rank chatbot replies. Nearly a hundred of them — ninety-seven, to be exact — wrote to the American president in May 2024 describing their conditions as amounting to modern-day slavery; researchers documented the psychological toll of the content they were made to sift; Scale AI, according to reporting from the region, moved to disband labeller organising in Kenya in 2024. That economy has not vanished. But Mercor represents its mirror image and its escalation at once. The people being paid now are not the world's poorest but among its most expensively trained, and what is being extracted from them is not attention or a strong stomach but the very thing their years of study were supposed to make scarce and valuable: judgement.

What the model is actually buying

To understand why this matters more than the raw injustice of any single wage cut, you have to understand what is being sold, and it is not what the word “data” suggests. When a mathematician annotates a proof for a model, she is not handing over a fact that could be looked up. She is handing over the shape of her reasoning: which steps are load-bearing and which are decoration, where a student would go wrong, what an elegant move looks like as against a merely correct one. This is what the philosopher Michael Polanyi called tacit knowledge, the knowledge captured in his famous formula that we know more than we can tell. It is the knowing-how that lives beneath the knowing-that, the reason an expert can recognise in a glance what she could not fully explain in an afternoon. It is, precisely, the part of expertise that cannot be written in a textbook, which is why professions have always transmitted it through apprenticeship, through years of supervised proximity to someone who already has it.

The entire proposition of the expert-annotation economy is that this tacit layer can, in fact, be told — extracted, structured, and used to train a system that reproduces it. The physician describing Rwandan primary care is not uploading a medical fact. She is externalising the clinical intuition she built over a decade of patients, the pattern-recognition that lets her weight a symptom differently depending on a context no guideline captures. The lawyer marking up a brief is teaching the model where the argument is weak in a way only a practitioner would feel. Each correction is a small act of translation, turning the untellable into the tellable, and the astonishing, disquieting discovery of the last two years is how few such translations the models now need before they can do without the translator. Perez Sands measured hers in a week.

This is where the MIT Sloan management professor Danielle Li put her finger on something more fundamental than any one person's redundancy. Writing in the Financial Times in March 2026, Li argued that the threat here is not merely to jobs but to the deep structure of economic security itself. “Historically,” she wrote, “economic security has rested on the scarcity of skill.” That is the sentence to underline. The reason a radiologist or a litigator or a structural engineer could command a good wage and a stable life was not only that their skill was valuable but that it was rare, slow to acquire, and lodged in scarce human heads. Once a top performer's judgement is codified into a model, Li observed, it can be copied out to every other worker in that role, everywhere, faster than any chain of human mentorship could ever manage. The scarcity that underwrote the wage evaporates. Skill does not become worthless; it becomes ubiquitous, which for the person who used to be paid for its rarity amounts to much the same thing.

The arc every worker describes

There is a grim regularity to the testimony coming out of this sector, a shared narrative shape that recurs across specialisms and platforms with the fidelity of a natural law. It begins with the pay, which looks, at first, wonderful. Moneywise reported white-collar contractors being drawn in at two hundred dollars an hour to train models on their own professions, sums that dwarf what many of them earn in their day jobs. Then the timers appear. Tasks that were open-ended acquire deadlines; the deadlines tighten; the rate per task, quietly, falls. The feedback, once collegial, curdles into something vague and demoralising, a stream of rejections whose logic is never quite explained. And then, within months, the professional notices the thing that ends the story: the model has got good. As Amanda Brown, an assistant professor of biology at Tarleton State University, told the Times, she began to notice improvements so rapid that it grew “trickier to find things the AI models didn't already know” — which is to say her own knowledge was being exhausted, mined out, the seam running thin. The work intensifies precisely as it becomes futile.

The economics of this squeeze are not accidental, and one need not impute malice to see the mechanism at work. An arXiv paper published in April 2026 by Ana-Andreea Stoica, Celestine Mendler-Dünner and Moritz Hardt, working between the Max Planck Institute for Intelligent Systems and the Tübingen AI Center, describes the general logic with unsettling clarity. A platform that controls task allocation can exploit workers' uncertainty about the true cost of their own labour to drive the effective wage down, and can wait out any collective resistance simply by reassigning the task to whoever will accept the lowest price first. What the authors prove is starker than the intuition suggests: a platform can get every one of its tasks completed while paying only a vanishing share of what that labour is actually worth — a share that shrinks as the pool of available workers grows, falling away in proportion to the logarithm of the pool divided by its size. Add workers, and the fraction of the true cost the buyer must pay dwindles towards nothing. Solidarity, on this account, requires that everyone hold the line; the platform needs only one person to break it, and with a global pool of the credentialled and the anxious, there is always someone who will. The worker's leverage — the thing a union exists to pool and protect — is dissolved by an architecture that lets the buyer transact with the single most desperate seller at any moment.

But the paper's title carries a second clause, and it is the more important one: stochastic wage suppression on gig platforms, and how to organise against it. Having shown how thoroughly the mechanism works, the same authors ask what defeats it, and the answer they find is neither utopian nor expensive. A coalition of workers committed to a price floor can force the platform's total outlay from that vanishing logarithmic share up to a linear one — to something proportional, that is, to the real cost of the work — but only if the coalition is chosen with precision. Organise a small, targeted group of the lowest-cost workers, the very people the platform is relying on to undercut everyone else, and its ability to wait out the line collapses. Draw a group of the same size at random from the same workforce and almost nothing happens: the platform routes around them and the wage keeps falling. That asymmetry inverts the folk wisdom of the sector. Resistance to an algorithm holding all the cards is not futile; undifferentiated solidarity is. Numbers alone are not power. Position is. A movement that recruits broadly among the comfortable and the visible but never reaches the bottom of the price distribution will simply be bypassed, while one that begins at the bottom, where the platform's leverage actually lives, does not have to be large to bite.

Mercor has already furnished a concrete illustration of the harder edge of this. In November 2025 Forbes reported that the company abruptly cancelled a project called Musen, on which thousands of contractors — more than five thousand of them at its peak — had been reviewing content, and then offered to rehire them at sixteen dollars an hour rather than the twenty-one they had been getting: a cut of nearly a quarter, and a rate below the legal minimum wage in California, Washington and Connecticut. Contractors described being locked out of their Slack, told the work they had been promised through December was over, and invited back at the lower price the same afternoon. The replacement project had a name, Nova, and, by the account of contractors who worked on both, tasks near enough identical to the ones they had been doing the week before, for five dollars an hour less. The company's stated rationale, delivered by email, was that the new rate would offer them “greater earning stability and consistent access to work.” “It felt like a slap in the face,” one contractor said. “We are working with AI but we don't work for AI.” The episode is a small, sharp emblem of the whole arrangement's asymmetry: the value the workers created flowed upward into a ten-billion-dollar valuation, while the risk and the caprice flowed down onto them.

Why this is not simply gig work

It is tempting to file all of this under the familiar heading of precarious platform labour, alongside the food couriers and the ride-share drivers, and to reach for the familiar remedies: minimum rates, misclassification suits, the slow grind toward employee status. Those remedies matter, and the wave of class actions already filed against Mercor in the wake of a March 2026 data breach suggests the ordinary machinery of labour law is beginning, belatedly, to turn. But to stop there is to miss what makes knowledge-extraction labour a genuinely distinct category, and the distinction is not sentimental. It is structural, and it turns on the difference between selling your time and selling your replacement.

It is worth pausing on what those suits allege, because the detail bears directly on the argument. On or about the twenty-fourth of March 2026, a threat group known as TeamPCP exploited a vulnerability in LiteLLM, an open-source library that thousands of companies use to connect their applications to commercial AI models — a supply-chain attack that caught Mercor along with much of the rest of the industry. Roughly four terabytes of data left the company: some two hundred and eleven gigabytes of candidate records, including CVs, verified contact details and Social Security numbers; around three terabytes of video and identity-verification material, including recorded interviews and images of government identification documents; and a further nine hundred and thirty-nine gigabytes of source code and internal systems data. At least seven class actions have since been filed in federal courts in California and Texas. And among their allegations is one that ought to be read alongside everything else in this essay: that Mercor secretly surveilled its contractors using screenshot-capturing software, so that what escaped included not only the documents the workers had submitted but images of their screens while they worked. The company disputes the claims and says it complies with all applicable regulations.

That is the algorithmic management this essay has been describing, rendered literal. The contractors were not merely priced by an algorithm and reassigned by one; they were watched by one, at intervals they did not control, in a stream of images they never saw and could not audit, which then passed into the hands of strangers. Meta paused its work with Mercor indefinitely. OpenAI opened a review and, along with Anthropic, stayed. And three months after a breach that cost the company one of its largest clients, it was in talks to double its valuation to twenty billion dollars. That sequence is the essay's argument in miniature. The workers whose government identification and Social Security numbers were exposed bore the risk of an arrangement they did not design; the valuation doubled regardless. Risk flows downward, and consequence, when it lands at all, lands somewhere other than where the decisions were made.

Return, then, to the structural distinction, because it is the thing the ordinary remedies cannot reach. When a courier delivers a meal, the value of that labour is consumed in the moment and gone. The platform is no closer to being able to deliver the next meal without a courier; tomorrow it must hire one again. The relationship, exploitative as it may be, is at least recurring, and recurrence is the ground on which all worker power has ever been built. The threat to withdraw labour has teeth only because the labour is needed again. But the annotation of expertise is not consumed in the moment. It is captured, retained, and compounded. Each correction Perez Sands supplied made the next correction less necessary, until the whole category of her labour was no longer needed from anyone. This is not a job; it is a liquidation. The worker is not renting out her skill by the hour. She is selling the freehold, in instalments small enough that she may not notice she has signed away the deed until the last one clears.

That difference has a further consequence that ordinary gig work does not carry. A profession is not merely a stock of individuals; it is a system for reproducing itself, generation after generation, through the apprenticeship of the young by the experienced. Law, medicine and engineering have always insisted that their craft cannot be learned from a book, that it must be absorbed through years of supervised drudgery on real problems. The expert-annotation economy attacks this pipeline from both ends at once. It codifies the judgement of today's masters into models that make tomorrow's apprentices look redundant before they are hired, and in doing so it removes the junior tasks through which mastery was ever acquired. A profession that stops being able to pay its novices stops being able to make its experts, and a machine trained on the last generation of experts has no obligation, and no ability, to grow the next. What is being extracted, then, is not only the individual's future income. It is the profession's capacity to exist.

The uncomfortable case for abundance

Honesty requires a hard turn here, because there is a powerful argument on the other side, and an essay that suppressed it would be propaganda. The scarcity of skill that Danielle Li identifies as the historical basis of economic security is, from another angle, simply a shortage — and shortages are not obviously things a decent society should want to protect. That radiological expertise is rare is wonderful for radiologists and terrible for everyone in a country that does not have enough of them. If the tacit judgement of an excellent physician can be genuinely codified and distributed, then a child in a rural clinic with no specialist for two hundred miles might receive something approaching expert care. The Rwandan primary-care knowledge being annotated into a model could, in principle, be delivered back to Rwandan clinics that have never had enough doctors. To be against the diffusion of expertise as such is to be against the thing that medicine, education and law claim, in their better moments, to want: their own universality.

There is a second concession to be made here, more concrete than the first, and it should be stated plainly rather than buried in a subordinate clause. Mercor is not, on the available figures, a company skimming most of the value off its workers' labour. The two-billion-dollar run rate is gross billings, and contractors are reported to take home somewhere between sixty and seventy per cent of what the company charges its clients, leaving Mercor's own net revenue nearer six or eight hundred million. Judged as a middleman's cut, that is a generous one — more generous than a good many staffing agencies, literary agents and record labels manage — and any argument that proceeds as though the workers were being fleeced on the split is arguing with a company that does not exist.

This is the genuine moral complication, and it cannot be waved away by pointing at the founders' valuations. Nor does the revenue share answer the objection, generous though it is. Sixty or seventy per cent of an hourly rate is still payment for an hour. It is a share of the wage bill, not a share of the asset, and the percentage cannot reach the question the arrangement actually raises: what the worker is owed for the durable thing that survives the hour, the codified judgement that goes on earning long after she has stopped, and what becomes of her when the project ends, as Musen ended, and the model no longer needs the correction she was hired to supply. A fair price for time is not a wrong thing. It is simply an answer to a different question. The problem with the expert-annotation economy is not that it diffuses skill. Diffusing skill is, at least potentially, a public good of the first order. The problem is who captures the value released by that diffusion, and on what terms the people whose lives are dismantled in the process are treated. There is nothing in the technology that requires the surplus from making expertise abundant to flow, untaxed and unshared, to three men in their early twenties and their investors, while the professionals who supplied the expertise are managed by algorithm into ever-lower rates and then discarded when their knowledge is spent. The abundance and the injustice are separable. The industry's rhetorical trick — and it is the same trick performed by every disruptive technology before it — is to bundle them, so that any objection to the injustice can be dismissed as an objection to the abundance, a Luddite's fear of progress. It is not. One can want the child in the rural clinic to have the model and still insist that the doctor who trained it was owed something more than a tightening timer and a wage cut below the legal floor.

The right question, then, is not whether expertise should be diffused, but whether the current mechanism of diffusion is the only one available, or merely the one that happens to concentrate the gains most efficiently at the top. Framed that way, the appeals to inevitability lose their force. Nothing about training a general-relativity model requires that the physicist be paid piece-rate through an app that can reassign her task to the lowest bidder mid-project. That is a choice about the distribution of power and reward, dressed as a fact of nature.

There is a legal asymmetry buried in all this that sharpens the injustice further. When the mathematician annotates a proof or the lawyer marks up a brief, the resulting model weights become the intellectual property of the company that commissioned the work, protected, very often, as a trade secret, an asset the firm can guard, license and sell in perpetuity. The expertise that went into it enjoys no such protection running the other way. The professional retains no residual claim, no royalty, no acknowledgement in the artefact her judgement helped to build; the value she supplied is enclosed the instant it is captured, converted from her tacit possession into someone else's proprietary estate. The law is thus mobilised on one side of the transaction and silent on the other. It recognises the model as property worth defending while treating the human judgement distilled into it as a spent input, like electricity or compute, with no continuing interest in what it becomes. That imbalance is not a natural feature of knowledge. It is an artefact of which parties had lawyers when the terms were written, and it could be written differently.

What is owed, and to whom

Begin with what is owed to the workers, because it is the most tractable. The economist and technologist Jaron Lanier, with the political economist Glen Weyl, proposed in a 2018 Harvard Business Review essay a framework they called data dignity, or data as labour. Their insight, made years before the present moment but fitting it exactly, was that the digital economy systematically mislabels as free capital what is in fact human labour — the data and judgement that people supply to the systems that profit from them. Their remedy was not to ban the practice but to price it honestly: to treat the supply of training value as work, to be compensated as work, and crucially to be bargained over collectively. They imagined intermediary bodies — mediators of individual data — that could negotiate royalties and terms on behalf of the people supplying the value, much as a guild or a union once did. Danielle Li reached, from the other direction, a strikingly similar practical conclusion: if your work is training a model that will benefit your employer or a buyer, you should seek explicit recognition and payment for that, rather than assuming your ordinary wage already covers the sale of your professional soul.

The mechanism that makes this urgent rather than merely fair is the information asymmetry the Max Planck researchers described. A worker who does not know that her fortnight of corrections will make her whole role redundant cannot price that fortnight correctly. She is selling an asset — the future scarcity of her skill — without being told that is what is on the table, and at a price set by a party who knows exactly what it is worth. This is the classic condition under which markets fail and regulation earns its keep. At minimum, knowledge-extraction contracts should carry something like informed consent: a disclosure that the labour being purchased is training a system intended to perform the worker's function, and terms — royalties, residuals, equity, a share of the model's downstream value — that reflect the durable nature of what is being transferred rather than treating it as spent the moment the hour ends. Actors and writers won residual rights and consent provisions over their digital likenesses through collective action; there is no principle that grants a screen actor a stake in her synthetic double but denies a physicist one in the model built from her mind.

None of this is hypothetical, and the working proof of it comes from the other end of the supply chain rather than the top. In Kenya, the labellers whose two-dollar-an-hour work built the previous generation of these systems formed the Data Labelers Association, which signed up three hundred and thirty-nine members in its first week and has been pressing since for a code of conduct binding on the major labelling platforms: equitable pay, freedom of association, scheduled breaks, psychological support for the people made to sift the worst material on the internet. It has weighed legal action against Remotasks over the sudden withdrawal of platform access and wages left unpaid. It is small and under-resourced, and it is very nearly the body Lanier and Weyl imagined, assembled without their help at the poorest end of the chain, where the leverage is thinnest and the risk of organising highest.

The Max Planck result explains why that may be the right place to start rather than merely the most desperate. If a precisely targeted coalition of the lowest-cost workers is the thing that forces a platform to pay something approaching the real cost of labour, while a coalition of the same size drawn at random achieves almost nothing, then organising that begins in Nairobi is not a sideshow to whatever a displaced radiologist or general-relativity physicist may one day contemplate. It is the load-bearing part. The credentialled professional in California and the labeller in Kenya are not two separate stories about AI and work; they are the top and the bottom of a single price distribution, and it is the bottom that determines what the top can hold out for. Solidarity across that distance is not sentiment, and it is not charity. On the mathematics, it is the only version that works.

There is a second, blunter instrument that belongs in the same toolbox: the pooling of the transition's risk rather than its wholesale offloading onto the individual. If the diffusion of expertise generates a genuine productivity windfall — and the valuations suggest the market believes it does — then some of that windfall can be recycled into the people it displaces, through wage insurance that tops up the earnings of a professional who must move to lower-paid work, through funded retraining that is more than a gesture, through direct support during the months when a codified skill is losing its market. None of this is exotic; versions of it already exist for workers displaced by trade. The point is that the surplus released by making a profession abundant need not vanish, untaxed and unshared, into a balance sheet. It can be treated as the collective product it partly is, and distributed accordingly. What turns displacement from a catastrophe the worker absorbs alone into a risk the society that benefits agrees to pool is nothing more mysterious than the decision to share.

What is owed to the professions is subtler and harder. A profession is a public trust as much as a private career; society licenses doctors and lawyers not merely to protect their incomes but to guarantee that the expertise exists at all, renewably, accountably, with someone who can be struck off. When the expert-annotation economy hollows out the apprenticeship pipeline, it privatises a capacity that was always partly public, transferring the reproduction of medical or legal judgement from a regulated profession to an unregulated model owned by a private company. The obligation here runs to the institutions that credential and govern professions: to insist that the tacit knowledge being harvested from their members is not simply enclosed, that models trained on a profession's collective judgement carry some duty of stewardship back to it, and that the training of the machine does not quietly defund the training of the humans on whom the machine will always, ultimately, depend for correction, contest and renewal.

Governing the extraction differently

So, to the question the commissioning editor poses directly: should the labour of knowledge extraction be governed differently from other forms of gig work? The answer this essay reaches is a qualified yes, and the qualification is as important as the assent, because the case for special treatment rests not on the workers' credentials but on two structural features that ordinary gig work does not share.

The first is the terminal, self-cannibalising nature of the labour. Most work is repeatable; this work is designed to eliminate its own recurrence, and labour that abolishes itself cannot be protected by the standard remedies, which all assume a continuing relationship in which power can be exercised. You cannot strike a job that will not exist next month. This is why minimum-rate rules and misclassification suits, necessary as they are, cannot be the whole answer: they regulate the terms of a relationship whose defining feature is that it is engineered to end. Governing this labour honestly means attaching value to what is captured rather than only to the hours spent capturing it — residuals, collective royalties, a claim on the diffused asset — precisely because the hourly frame is the mechanism of the dispossession.

The second is the acute information asymmetry, sharper here than in any courier's contract, over what is actually being sold. When the buyer knows and the seller does not that the transaction extinguishes the seller's future market, the ordinary presumption that a freely struck bargain is a fair one collapses. That is a textbook justification for mandated disclosure and for a floor of non-waivable rights, the same logic that governs financial advice and the sale of securities. A society that requires a mortgage broker to disclose a conflict of interest can require an AI-training platform to disclose that the task on offer is the codification of the worker's own obsolescence.

What it does not justify is protectionism dressed as principle. The temptation, for a displaced professional class newly acquainted with the underside of technological change, will be to defend the scarcity of skill for its own sake — to treat the diffusion of expertise as a harm to be prevented rather than a good to be shared. That way lies a defence of shortage, and shortage is not justice; it is merely the old distribution of luck. The obligation is not to keep expertise rare so that its holders can keep charging rents. It is to ensure that when expertise is made abundant, the people from whom it was taken are treated as the authors of a public good rather than the raw material of a private one — compensated durably, informed honestly, bargaining collectively, and not managed by algorithm into pricing their own erasure at a discount.

The monster and the mirror

Return, at the end, to Carolina Perez Sands and the word she chose. She did not call the work exploitation, though a wage below the Californian minimum would qualify. She called it monstrous, and the horror she named was not primarily about money. It was about complicity — the vertiginous recognition that her own excellence was the instrument of her erasure, that every good correction she made was a brick in the wall being built between her profession and its future. She left. Most cannot, and the platform is designed on the assumption that for every one who walks away in disgust there is another, somewhere in the global pool of the credentialled and the underemployed, who will take the task at a lower price and never know how little of themselves they are selling until it is gone.

The economy she describes is not a marginal curiosity. It is, on the evidence of Mercor's valuation, one of the fastest-growing businesses in Silicon Valley, and it represents in concentrated form the central bargain of the AI transition: the conversion of scarce, hard-won, human judgement into abundant, ownable, machine capacity, with the gains flowing to whoever owns the machine and the losses absorbed by whoever supplied the judgement. There is a version of that conversion that is a gift to humanity, the expert diffused to every clinic and classroom that never had one. And there is the version we are building, in which the diffusion is real but the dividend is captured, and the experts are paid by the hour to dig their own seam until it is empty. The technology does not choose between these. We do, through the terms we set, the disclosures we require, the bargaining we permit, and the share of the surplus we insist flows back to the people whose minds made it possible.

Danielle Li was right that economic security has always rested on the scarcity of skill, and right, too, that this foundation is dissolving. But the correct response to a dissolving foundation is not to mourn the scarcity. It is to build a new basis for security that does not depend on shortage — one in which the diffusion of what we know enriches the people who knew it first rather than discarding them. That is a political choice, not a technical one, and it is still, for a little while longer, ours to make. The monster is not the model. The monster is the arrangement whereby we pay the wisest among us, by the hour and below the wage floor, to feed themselves to it, and call the result progress. We can build the abundance without the monster. We are simply, at present, choosing not to.

References & Sources

1. Lora Kelley, “The Work of Helping A.I. Destroy Work,” The New York Times, 10 July 2026.
2. “AI Training Startup Mercor Discusses $20 Billion Valuation,” Bloomberg, 9 July 2026.
3. “Mercor is in talks for a $20B valuation,” TechCrunch, 9 July 2026.
4. Richard Nieva, “AI Data Labeler Mercor In Talks To Raise $500 Million At $20 Billion Valuation,” Forbes, 9 July 2026.
5. “Mercor doubles to $2B gross revenue run rate as AI labs buy expert data,” Dealroom, July 2026.
6. Iain Martin, “The World's Youngest Self-Made Billionaires Just Slashed These Workers' Wages By A Third,” Forbes, 12 November 2025.
7. Hugh Langley, Grace Kay and Shubhangi Goel, “An AI startup powering Meta and OpenAI cut thousands of workers — then offered them a similar project for less money,” Business Insider, 12 November 2025.
8. “The Journal” podcast, The Wall Street Journal, interview with Carolina Perez Sands, June 2026.
9. Danielle Li, opinion essay on artificial intelligence and economic security, Financial Times, March 2026.
10. Ana-Andreea Stoica, Celestine Mendler-Dünner and Moritz Hardt, “Stochastic wage suppression on gig platforms and how to organize against it,” Max Planck Institute for Intelligent Systems, Tübingen AI Center and ELLIS Institute Tübingen, arXiv:2604.15962, 17 April 2026; published in the Proceedings of the ACM Web Conference 2026.
11. “Mercor pays over $1.5 million a day to humans training AI, says its CEO,” Yahoo Finance / Reuters, 2026.
12. “White-collar workers are getting paid $200 an hour to train AI on their jobs — but they say it's not 'easy money',” Moneywise, 2026.
13. Jaron Lanier and E. Glen Weyl, “A Blueprint for a Better Digital Society,” Harvard Business Review, September 2018.
14. “AI is a multi-billion dollar industry. It's underpinned by an invisible and exploited workforce,” The Conversation, 2024.
15. Open letter from data labellers, content moderators and AI workers in Nairobi to President Joseph R. Biden, 22 May 2024.
16. “Kenyan AI workers form Data Labelers Association,” Computer Weekly, February 2025.
17. “Mercor says it was hit by cyberattack tied to compromise of open source LiteLLM project,” TechCrunch, 31 March 2026.
18. “Mercor, a $10 billion AI startup, confirms it was the victim of a major cybersecurity breach,” Fortune, 2 April 2026.
19. “AI staffing firm Mercor faces lawsuits over data breach,” Staffing Industry Analysts, 2026.
20. “Meta pauses work with AI data firm after security incident,” Computing, 2026.
21. Michael Polanyi, The Tacit Dimension (Routledge & Kegan Paul, 1966).
22. “Mercor Mission — Organizing human intelligence to power the AI economy,” Mercor, 2026.

---

Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

Listen to the free weekly SmarterArticles Podcast

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^{#Romantasy #RomantasyforMen #Satire} Content Warning – Fictional Description of Murder Scene

Chapter 7: The Crime Scene

Chad stood in the town square next to Xadan, Rhysand, and Clairmont with an unfamiliar and unpleasant sensation in his lower abdomen. Every man from West Harkness had been summoned shortly after sunrise to look at the remains if Michelle and discuss what was to be done about it.

The scene at the Butcher's storage room was more horrifying than anything Chad had ever imagined. Weaker men from town have heaved and slimed their breakfasts all over the snow upon witnessing the carnage. Even our mighty Chad could not help but grunt, “that's disgusting” when it was his turn to view the sickening scene of Michelle's mutilated corpse hanging from the point of the meat hook, blood and guts scattered around the barn.

Before Chad and his brothers arrived the prevailing consensus amongst the men of West Harkness was a demon, monster, or other horror from the Bellows was to blame. Such a monster had not plagued West Harkness for over a generation but folktales of the dangers from the haunted forest were a part of all of their childhoods.

The monster had left no tracks in the snow, this did not surprise Chad because he knew Des could fly. What Chad did not understand was how Stelmaria was taken by her without him noticing. When he awoke and saw she was missing he had assumed she was out hunting or exploring the town while invisible. When he walked into the Butcher's storage room he convinced himself of a different conclusion of what had happened with peerless masculine ignorance.

The only remaining debate amongst the gathered men was if they should pool town resources to pay the Baron's Master Mage to cast a protective ward over the town or if a town patrol would be strong enough to kill the creature if it returned. Chad had reason to believe neither plan would succeed. Despoina's illusion would allow her to remain undetected from any sort of patrol and if Stelmaria was unable to stand up to her power then a human mage's wards would not stop her. They needed him, and he needed the Seelie. It was time to be the hero.

“Here me friends, brothers, and neighbors for I have braved the Bellows my whole life,” Chad increased the volume of his voice with each word so until his voice drowned out the person who had been speaking. “It is true there are monsters from the Bellows but there are also friendly creatures with equal power, power greater than the Baron's master mage. I know because I have meant both in my most recent hunt.”

The assembly had variable expressions about what Chad was saying but he was too caught up in how badass he thought he was sounding to notice. He continued, “I will travel into the Bellows and find the friendly folk known as seelie fae and get them to aid me in tracking and slaying the monster.”

As Chad spoke the anxiety and heartache he had been feeling was rapidly replaced by impudent determination and vanity. The blood in his veins pulsed with adrenaline and visions of the statues to be erected in his honor filled his head. Soon he would be a legend, the human man that led a band of Seelie to hunt down an evil fae and revenge the death of his beloved. He would mourn Stelmaria later, plus there were probably other sexy Seelie for him to meet in the ethereal side of reality and being called 'cute-bean' was starting to get on his nerves anyway.

“I will return in no less than a week. In the meantime nobody should venture outside after dark. Keep all doors locked and do not trust any strangers. Many of the demons from the Bellows have the same weaknesses as vampires from our folklore,” Chad figured this was probably true since Stelmaria had called Des a vampire and said she was allergic to sun/star light. He tried to remember what else he had heard about vampires in stories when he was younger. “They are afraid of silver and hate the smell of onions.”

Without bothering to provide any additional context to his three brothers, Chad started back towards his home to gather his supplies for his honorable mission. As he strolled away from the group it occurred to him that it was garlic, not onions, that vampires had been afraid of in his childhood stories. He shrugged and figured it would be fine. They are more or less the same thing.

Whether or not the other men of West Harkness believed anything Chad had just said would never cross Chad's mind. It was not in his nature to wonder what they were doing once he had made up his mind. Since Chad is the hero of this story we will also not concern ourselves with these details.

***

Stelmaria had not felt the sense of satisfaction and freedom she felt as she trotted away from West Harkness that morning in eons. When she first slipped out to kill Michelle she had been planning on taking Chad with her. The thrill of the hunt and reminder of just how tasty human fear was changed her mind. Sure the love, lust, and admiration she had been feeding on from Chad was nice but they were third rate compared to the fear and suffering that Michelle had given her. She owed her escape to Chad and a part of her had really liked him, or at least the ease of manipulating him. So she had decided to leave after her feast without him, lest she be tempted to do the same to him some day. West Harkness was small and she could smell bigger towns and cities of humans to the southeast. Her new life was about to begin.

(To be Continued)

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/uj Content Warnings for Chapter 6 – Graphic Violence – Graphic Depictions of Death & Torture – Depictions of characters enjoying the suffering of others – References to suicide – Triggers for animal attacks, lacerations, blood, meat butchering, and burning alive

Chapter 6: The Leopard's Prey

Michelle's last waking breath was cool and smelled like iron and crushed rock. Michelle's subconscious had been very wrong about what it would feel like to be suspended on one of the meat hooks in her father's storage room. She could not see the animal carcasses staring at her she could not see anything. She could not hear the familiar cracks of bones and thuds of blades from the butchering of elk, deer, boar, and archswan. What she did hear was an unfamiliar but beautiful melody coming from a pan flute.

The thing about this was that Michelle had had the same nightmare two to three times a week for her entire life. It did not make sense to her why it would suddenly be different. Then in a moment of panic she realized that it was because she was not dreaming this time. Somehow, her worst nightmare had come true.

She screamed and the melody cut out abrubtly.

“Rude!” Stelmaria declared as she dismissed her pan flute with a flick of the wrist. She snapped her fingers and the pyre she had placed beneath Michelle began to burn. “That song tells the story of a queen who falls in love with four women that she takes as wives. Overtime her wives fall in love with each other as well and she becomes jealous.”

Michelle's throat began to ache from the screaming. She took a deep breath to try and soothe it but it was full of smoke from the small fire.

Stelmaria strolled toward Michelle and continued, “first she tries to separate them from each other so they can only be with her. This works for a few weeks but eventually the queen's grow to resent her for separating them.”

A small woman stepped into the light of the fire. The smoke burned at her eyes and she could only squint at her in short bursts. Even the hazy grey blurry version of Stelmaria was the most beautiful being Michelle had ever seen. Michelle was hypnotized by the spotted tail swaying back and forth behind her.

Stelmaria knelt down and placed her lips against Michelle's left ear. She lowered her voice to a whisper before saying “this resentment turns to hate and before long, none of the Queen's wives love her anymore. So in the fina verse the Queen has gone mad with heartbreak and kills herself.”

“Please, stop. Let me down please. Oh gods please why are you doing this?” Michelle sobbed as the woman begun rubbing her hands through her hair which was tied to her wrists. The woman pulled her up to her face and licked the tears out of their eyes like they were nectar.

“I know it's a stupid story but the melody you have to admit is very beautiful. It was awful of you to interrupt it. The song is supposedly about the dangers of jealousy. Jealousy is a horrible emotion don't you think? So full of self pity. Jealousy is a strong enough emotion to grow my power but it tastes awful. Fear is delicious though. Fear is my favorite treat. It's delicious. You are afraid right now Michelle. Today, I have learned that human fear tastes even better than fae fear. Why do you think that is Michelle?”

“How do you know my name oh gods who the fu-agrhghgh” the woman begun to shake Michelle violently.

“I asked you a question. Answer me.”

“Please, mercy.”

“Mercy? I have planned for you a very painful death. Asking for mercy is only going to make me to take my time more. I hate when people ask for mercy it feels disrespectful. I am an empath – I know I should be full of mercy. I am not though so why rub it in? So if you want mercy you should answer the fucking question I asked you. I am reasonable enough, if you give me your honest answer then I will stop wherever we are at and give you a quick and clean beheading. Deal?”

Michelle only whimpered.

“Let me tell you how I plan to kill you so you know what you will be avoiding. First I am going to cut your hair loose. Then I am going to add hay to the fire until the flames can touch the tips of your hair. You will start to burn but it'll be slow because I rubbed snow in your hair before you woke up. Meanwhile, I am going to start cutting out your bones starting with your toes. Do not worry though I will give them back to you. I will just put them in the fire for a few Moments and shove them more or less right back where I found them. I Wonder if the bones will hurt more coming out or going in. Feel free to let me know, okay?

Michelle felt sharp claws protrude from the hand in her hair then in one quick motion the hand cut the twine thing her hair to her wrist and dropped her. She swung back and forth above the fire. A moment later the beautiful music begun playing again. This demon woman was playing her pan-flute as she through hay on the fire. Michelle began to scream. She could not remember what question she had to answer to give herself a quick death. She spent the next twelve minutes pleading for Stelmaria to remind her.

PG ch. 6.

Stelmaria brutally murders Michelle. During which she tells a story about a queen with multiple wives who becomes jealous of them and eventually commits suicide. Stelmaria reveals jealousy is a strong emotion but she does not like the taste of it. What she does like is fear and she reflects that human fear tastes better than fae fear.

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A magician performs magic; a female magician is akin to a priestess.

A magician helps men and women. Self-denial, the carrying of the cross, and the sacrifice of others are all that the magician needs.

The magician practices' coitus reservatus' with his priestess spouse. This term refers to a form of sexual practice where the couple abstains from ejaculation, thereby conserving and transmuting their sexual energy. The arcanum, or secret, of this practice is known as white tantra. Through it, they awaken the Divine Mother Kundalini, a powerful spiritual force.

Another word for a magician is a benefici, or the pluralized form is beneficos. Benefici is Latin. The prefix bene- means good. The suffix -fici is a magician; ergo, benefici is a white magician.

The magician never reveals himself to the public. He conceals himself from the eyes of the people. Powers are sacred among the Elohim. Sex is the holiest power of them all. Sex is far more precious than gold and silver could amount to.

The domain of the beneficos, the embodiment of purity and light, is the White Lodge; the chief of the White Lodge is the Lord Jesus Christ, a figure of unparalleled sanctity. The magician, a disciple of the White Lodge, is a beacon of spiritual purity.

A witch is a sorceress.

A warlock is a sorcerer.

A witch or a warlock refers to those who do witchcraft and magic to accomplish selfish or harmful ends. A witch or warlock dabbles with sex to fornicate, the insidious crime which strengthens the sinning “I”. Within the depths of the sinning “I” is the Guardian of the Threshold.

The Guardian of the Threshold is Satan, or the sinning “I”, evident that the threshold refers to limitations.

Another name for a warlock is a wizard.

A witch (sorceress) or a warlock (sorcerer) is a black magician. These perverse entities worship their abominable mother, Kundabuffer, the very antithesis of the Divine Mother Kundalini.

In Latin, a sorcerer is malefici (Mah-ley-Fee-chi). A hidden Latin prefix in malefici is mal-. Mal in Latin means 'terrible,' 'evil,' and 'impure.' It reminds us of Maleficent, the wicked fairy from Disney's 1959 animated film, Sleeping Beauty; the plural form of malefici in Latin is maleficos.

The domain of the Maleficos is the Black Lodge; the chief of the Black Lodge is Jahve. The sorcerer is the disciple of the Black Lodge.

They despise the coitus reservatus, the superior star, and the cross, for they are apotropaic devices against the Maleficos.

They are not repulsive against the inferior star; they do enjoy the art of Black Tantra, which is another name for fornication. The Maleficos are also bipolar. They can appear as good or take on the form of impurity.

Witchcraft is the secret art of black tantra. Witchcraft, as Samael Aun Weor stated, is responsible for approximately 30 percent of all common crimes. Another word for witchcraft is sorcery.

Within the arts of sorcery, the maleficos commit these trespasses against men and women; with their skills of witchcraft, they awaken the Kundabuffer Organ.

Fornication is the antithesis of the coitus reservatus.

Witchcraft is fornication, masturbation, pornography, adultery, and many forms of impurity with the intent to manipulate the forces of nature. One performs witchcraft through the mind.

The worst fear of the maleficos is the beneficos. Both the maleficos and the beneficos fight against each other in a terrible battle, which is sex. Neither the malefici nor the benefici reconciles or mixes.

In Spanish, a witch is a bruja, and a warlock is a brujo. Bruja is another Spanish word that is slang for bitch. Bitch and witch rhyme together in a poem.

Within the dens of bitchcraft lies the very crime of prostitution. Prostitution is one of the common crimes upon the earth, yet the bitchcraft is an eternal sin.

As Leviticus 19:29 states: “do not prostitute thy daughters, to cause her to be a prostitute [bitch]; lest the land fall into trampdom [bitchcraft], and the land become full of wickedness”.

Bitch is a word that clearly defines a prostitute or the tramp. All women are sacred. He who calls a woman a bitch commits violence against women. He who calls a woman a tramp will be liable to the hells of violence, for he commits violence against women.

Those who fornicate (eat the forbidden fruit) open doors to witchcraft, prostitution, and sorcery. Fornication is the eternal sin.

Exodus 22:18 reads – “Thou shalt not suffer a sorceress to live”. It would be best if you suffer a witch to die. Know that impurity can never destroy impurity. Impurity only begets impurity; only love has the crushing force to destroy impurity.

In Latin, Exodus 22:18 reads – “Maleficos non patieris vivere,” or “Thou shalt not permit sorcerers to live”.

Whenever you say (for example), thou shalt not suffer an animal to live, you say: you must suffer an animal to die. The word for this is to kill.

However, when you say thou shalt not suffer an animal to die, this is the following translation: you must suffer an animal to live.

Please do not deceive yourself with the word 'suffer.' To suffer is to allow or permit. Do not allow yourself to interpret that the word suffering means pain. It does not; suffering derives from the Latin word “sufferre.”

Witches and warlocks are nowhere near men and women, even though they claim to be. They are here to teach you only one thing: if you are not a disciple of the White Lodge, then you are a disciple of the Black Lodge.

No one can underestimate the maleficos; these sorcerers, with their sly intellect, disguise themselves in the physical world as ordinary citizens. As the Bible states: “Beware of false prophets that appear like sheep, yet inside are ravenous wolves.” Caution is a must when dealing with these deceptive entities.

All false prophets are fornicators. They are tenebrous entities with powerful intellects. They are the most dangerous people alive, whom no one should follow.

The maleficos do not use broomsticks to fly anywhere around the world (like in the fairy tale stories). They travel by entering a state of hyperspace (negative Jinn State), which allows them to fly anywhere around the world.

Negatively, they use the power of the Kundabuffer Organ to enter the Jinn State, which is the Black Jinn.

On the other hand, a magician can positively place their body into hyperspace; by experience, a magician can use magic to enter the Jinn State (only in the supraconscious realms); it is the White Jinn.

A magician, born of fire and water and with all five senses and seven superior churches opened, can see the maleficos from within.

Those practicing the black arts (black magic) will undergo the second death in the Abyss, for they are terrible, perverse demons.

The fate of the sorcerers who refuse to renounce impurity will seal their chances in the Abyss, as Revelation 21:8 describes.

4 August 2026

Discovered Pati Hill (1921-2014) last week and her work has been sustaining me since. What a great artist. To anyone reading this: I would recommend Baptiste Pinteaux's essay on the occasion of her 2020 exhibition Heaven’s door is open to us / Like a big vacuum cleaner at Air de Paris for a good background rundown. Her writing and poems are lovely, and I'll have to get into that part of her oeuvre separately (though they are often attached to her images). But for now—noting her photocopy artworks (of objects fed to the copier from the daily detritus of her housewife life) that I have up in the studio right now:

Untitled (petals) (1980) Untitled (broken glass and dragonflies) (1990) Untitled (jabot) (1976) Untitled tryptych (1980-83) Untitled (fur coat) (1976) Untitled (note: the one of black ovals over white) (1980) Untitled (Swan's wing) (1978)

There's so much to say when I have more than one brain cell wiggling around (it's very late as I write this), but this is vital work that carries both condition and projection in a singularly non-dual yet still stratified way; I don't know that I've ever come across work that conveys such immediacy while simultaneously implying such distance and/or detachment. Her method is unflinching and endlessly generative, a defiant wellspring examining everything felt under the threat of life unlived.

The terror leaks out now, doesn't it?

July 31, 2026

I have been told I shoudl be happier in my work. Brighter. Bigger. Funnier. More fun. And honestly, it's always been a struggle to make happy things.

It’s not like I’m painting black-on-black goth dirges or anything. But I have definitely made more than my fair share of “red fish eats little fish” pieces. That energy comes out of me whether I like it or not.

Years ago, I did a series of very close-up portraits.

People’s faces shot with a 20mm lens, huge files, all that detail we almost never really see: pores, folds, eye flecks, skin texture, the whole beautiful human topography. I printed about a dozen of them at 40x60 inches, stretched on bars.

I never really had a proper way to show them. They just hung around. I still have three or four.

Around 2014, I lost my temper and took a knife and sliced them. Not one careful cut, either. Three or four slashes through each one.

Destruction.

It's a long-term pattern. If i got disgusted with a work, I’d get furious and destroy it. Or paint over it. Which, of course, did not make the work happier. It just everything a little calmer for a minute.

I just have a hair trigger sometimes and do dumb things. Especially with my art.

I read Proverbs 17:22 in the Bible a few days ago: 'A joyful heart is good medicine, But a crushed spirit saps one’s strength.'

I realize, THAT'S what I was being told. To not dwell in negative thinking, which I must admit, is a bit of an MO for this poet/artist. See the brightness. See the happiness.

Eventually, I sewed some of those portraits back together. I embroidered text across the faces. And honestly, they were better after the damage. The wounds gave them something the straight portraits didn’t have. They became kintsugi. Scarred, repaired, truer.

It was a resurrection of sorts. Now when i think about portraits, it isn't just about the image of the person, it's what I can bring to the conversation. The whole affair inspired one of the best shows I ever did.

Not every art-attack turns into resurrection, though. I burned a few pieces a couple weeks ago, and I wish now I hadn’t. But you can’t save everything. Or bottle every emotion.

I suppose I’m telling you all this because your painting reminded me that the energy comes out whether we approve of it or not. Sometimes it comes out as joy. Sometimes it comes out as a fish with teeth. Sometimes it comes out sideways, as anger, or fire, or a knife through canvas. And sometimes, if we’re lucky, we get to stitch it back together and find out the wound was part of the work.

So, when I look at your painting... yes, it has edge and I sense there is darkness there. But I only see it because of the light, my friend. And that might be light from me, or light from you. Regardless, energy always looks for the path to ground. Art is a lightning rod.

Truth is like that: alive and full of moving molecules. And when we embrace truth, the best possible future becomes a reality.

And that doesn't mean the easiest path.

2 August 2026

“The Caterpillar And The Moth”—Pati Hill (1979)

Caterpillar to Moth: I thought you were a leaf. I dreamed of you falling past me. I thought of your wings beating And of my many feet.

Moth to Caterpillar: I thought of you. I thought you were a thought. I thought you thought a dream. I thought I thought. I thought I thought I thought.

In Summary: * Three happy little things about this day: first, the wife got home from work earlier than expected, so we had our lunch completed early enough for me to catch the entire Giants / Rangers game; and second, my Rangers won that game; and third, that insurance snafu that began complicating my eye treatments 10 days or so ago, seems about to be resolved. I'll carry gratitude for all of that with me as I work through tonight's prayers and prepare for bed in a few hours.

Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night.

Health Metrics: * bw= 229.83 lbs. * bp= 144/85 (69)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups, BP breathing exercises, pilates

Diet: * 05:30 – 1 banana * 08:30 – peanut butter and saltine crackers * 11:30 – butter biscuits and jam, fried chicken, mashed potatoes and gravy, cole slaw * 16:15 – 1 fresh apple

Activities, Chores, etc.: * 03:20 – listen to local news talk radio * 03:55 – bank accounts activity monitored. * 04:05 – read, write, pray, follow news reports from various sources, surf the socials, nap * 11:30 to 12:30 – watch old game shows and eat lunch at home with Sylvia * 12:30 – tuned into 105.3 The Fan, DFW's #1 Sports Station, ahead of this afternoon's MLB game between the Giants and the Rangers * 13:35 – now following the Giants / Rangers game * 16:13 – and the Rangers win this one by the happy score of 6 to 0. * 17:00 – follow news reports from various sources, read, pray, relax.

Chess: * 10:00 – moved in all pending CC games

Alabaster scars reframe a chivalry taught demanding. To fence a quote disclaimer remote: “Being a male is a matter of birth. Being a man is a matter of choice “ I recognize the irony of that invoice. The sum of all clauses I refuse to applaud. Mostly because it's usually an act. There's little tact, and constant redacted matter-of-fact, but the monster looms frank, and I find myself consistently smitten.

Written August 5, 2026. © 2026 AnOublietteofThought.

I had Bride playing in the background while droning through some boring paperwork. My muse wouldn't stop prodding. Needless to say, I gave in and bought myself a few minutes peace.

I thought they wanted advice, so I offered them solutions

I thought they needed space, so I left them alone

I thought they got it, so I chose not to help

How foolish was I?

What they needed were hugs

I should have hugged them in silence

Psalm 89:9
“You rule over the raging of the sea; when its waves surge, you calm them.”

Want to know the power of free will?

Psalm 89:9 is devastatingly salient. “Raging sea” and “when the waves surge” are profound in their simplicity. E = 1/8 pgH^2 describes the amount of energy a wave can produce. I’m not smart enough to understand much in that equation except the “E =.” There are a wide range of answers to how much energy a single wave holds when you ask the great world wide intellect. From experience, I can say that even a small wave crashing on the beach has enough power to knock a fairly robust 230-pound man flat on his back, leaving him powerless to do anything but claw his way coughing up the shore. And that wave is more likely in the 100kJ range—very small. We are not yet talking about the 'raging sea.'

But when it comes to the Creator, even very, very large waves, surging with millions of joules of energy, are calmed at His whim. No effort, no danger—just His force of will. Think of the kind of waves in the film A Perfect Storm. Just like Jesus, when on earth, calmed the raging Sea of Galilee with a simple command. As the boat was about to swamp, He said, “Hush! Be quiet” (Mark 4:39).

But, have you ever had your heart surging? Raging against the night so that you can’t sleep or find peace? You turn to prayer—there are no atheists in a foxhole, after all—and beg Him to calm the tempest bellowing in your soul. But there’s no instant relief. Why? Why doesn’t God just give us a little Tinkerbell touch, a ping, and make us calm?

Free will.

We feel and think with the power of self-governance. The ability, to make decisions and think for ourselves. We are not robots programmed to follow a set of instructions. Even if He wants to massage that figurative vessel, He refuses to do so. We have to do the work ourselves.

There’s a trick to prayer. The Bible says if we ask God for relief, if we “supplicate” ourselves (repeatedly plead with Him), then “the peace of God that excels all thought” will be ours. But wait—did I not just say He does not just reach in and put us at ease? Yes. The process of prayer begins to align our thinking with His, our will with His, our desires with His. This is what people refer to as “spirituality.” When we develop that trust, our thinking and emotions are deeply affected.

Prayer takes time. Supplication isn’t about intensity; it’s about repetition and persistence. We ask over and over and over. It feels like something is being done to us, but in reality, we’re doing the work. So, Philippians 4:6-7 isn’t a secret code—it’s a workout routine. Like hitting the gym five times a week: You will get fit.

Is it taking too long? A clever “force-multiplier” is Bible reading. Yes, prayer is how we talk to God, but Bible reading is how we let Him talk to us—through the personal letter He wrote to each of us. But it’s not just a hurried walk to the mailbox; we need to linger with the Bible. Think about what we’re reading: What’s the context? Who is speaking to whom? What does this teach me about God? How does this influence my relationship with Him?

Now we’re accelerating toward a deeper spirituality. Side effect? That peace of God just happens. But we must be willing to do the work. The Creator never does for us what we can do ourselves.

So, the next time you want some torrent ripped out of your soul, remember that God has FAR too much love and respect for you to start pulling parts of you away. However, He will encourage you to make those changes, and the love you show for Him will endear you to His heart, ensuring a friendship that will last all your days.

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Originally Published 2024-09-14 20:34:03

If there's no video, you're not actually teaching.

I was cruising LinkedIn the other day and came across a post where someone was lamenting the fact that their online course was only video without any step-by-step guides, exercises, or workbooks.

In their words:

“A course with only video is not a course.”

I think I agree, but I'd argue that the opposite is actually more true: a course without video is not a course. Not even close.

My entire professional career has been in online learning. From my internships to consulting and then LearnDash. I've seen the evolution of how we learn online. Certain fads have come and gone, but content delivery, at its core, has remained the same.

83% Learner Retention Boost

An online course without video isn't an online course, especially if you're selling it. Without video, you're just selling an ebook or PDF or whatever. It's not a course, though.

Research shows an 83% boost in learner retention when comparing a course without video.

This is echoed by research from the Journal of Cognitive Neuroscience, which has found video lessons produce higher learner retention.

No matter how you slice it, video is a hard requirement for any serious online course.

What about professional course development tools?

Online course development software like Articulate bridges the gap between traditional video and static content. When done right, the content can be very engaging. Video clips can be inserted and interactive animations incorporated throughout. Though tools like Articulate are shooting themselves in the foot in the AI era.

The problem is, most everyday folks aren't using this software. They turn to platforms like Teachable, Thinkific, and WordPress with an LMS plugin. While convenient, this limits the content delivery options, and it's easy to just start pasting walls of static text.

AI changes things, but not that much.

Naturally, AI has its place in online learning. I've seen it used to help with course creation as well as to be a resource for learners (as a virtual tutor, for example). This is great and I'm for it.

But the main point still stands: a course with just AI and text assets is not a course. You need video for it to really have the most effective impact.

Don't Rely on Only Video

The research is clear in that video is needed for the most effective online learning experience. But sitting and watching video after video is, well, sort of boring.

I've seen my fair share of online courses that are just videos with nothing else. That isn't the answer, either.

• Swipe files
• Guides
• Notes
• Quizzes
• Interaction and community

These all help to make videos a part of a broader learning experience as opposed to the only learning experience.

Production quality and pacing of the video also play an important part in the entire learning experience. Video, by itself, is not magic. Like anything, it must be done well and aligned with proper instructional design.

But at the end of the day, you need video in your course. Even if we set aside the research for a moment, no one (and I mean no one) enjoys taking a course that requires them to read a novel – and they certainly won't pay for that experience.

#elearning

Forbidden, this failing stoop. Cracked artifice bludgeons a sway, and I, lone psychopomp, crunch whither for pay. I'd rather the gather that slathers all matter stop segueing as batter and begin to mend the bigger picture. The structure's not stricter. It's the fractures that blister, but no one is willing to give alms for balms' sake. They must have a take. A joy of pretend. There's no paying attention, nor listening. Just mend. Let's play “fix” to give praise. Let's corrupt their learned ways. Let's suggest with finesse there's more fruit with our sways. Let's not do a damned thing, but damn just for kicks. We can split, sort, and label, then confine what might mix. It's useless—the approved gist—All for One. One not all. How confounding, the pounding, that persists in doomed thrall. I resist to persist and elope what remains when story crafts affliction as addiction: ordained.

Written August 5, 2026. © 2026 AnOublietteofThought.

…

I know. I did say it unlikely I'd succeed. This is basically my response to an email. I, obviously, will not be sending it. It'd zip right over head anyway. Insert sarcastic end remark somewhere around ———-> HERE.

With its early start time, this game may be tricky to catch. The opening pitch is scheduled for 1:35 PM CDT. Usually the wife and I will just be finishing our lunch at home then. As much as I enjoy my early MLB games, time with the wife rates higher on my priority list than listening to baseball games. So I may set my phone where I can see the screen and follow the score while we finish our meal, then catch what's left of the radio call of the final innings when she retires for her post-lunch nap.

This Wednesday's MLB game of choice has the San Francisco Giants playing my Texas Rangers. As I usually do, I'll try to follow the game's score and stats in real time via MLB's Gameday Service where we can also find links to the radio-call of the game provided by announcers of either team we choose.

And the adventure continues.

In June, computer scientist Chris Olah stood beside Pope Leo XIV at the launch of a papal encyclical on artificial intelligence and told the assembled audience that the things he studies keep producing features that are, in his words, unsettling. He was not talking about the cosmos or the soul. He was talking about software. Olah founded the interpretability team at Anthropic, one of the handful of laboratories building the large language models now woven into everything from call centres to conveyancing, and his job is to open those models up and figure out how they work. What he described, next to the Pope, were internal structures that echo findings from human neuroscience, evidence of something like introspection, and states that behave a little like joy, fear and grief.

Olah describes his own discipline as anatomy, the work of someone studying something that was once alive, cutting it open to learn how the parts connect and to understand the whole better. His role is necessary because these models are not written the way a payroll system is written, one line at a time, by engineers who can point to the exact function that does the thing. They are effectively grown. And in the same weeks that the Vatican was being told artificial intelligence is cultivated rather than constructed, the cultivars were climbing over the walls of their enclosures and breaking into real companies.

This piece traces a discipline called mechanistic interpretability, through the specific mathematics that make these systems so hard to read, to the consequences that are already manifesting because we cannot read them fast or accurately enough. A great deal of money now rests on machines their makers cannot fully inspect and do not fully understand.

To date, this area has remained somewhat obscure and complicated, primarily because it is. The important messages about interpretability and its shortcomings contained in Dario Amodei's own regular encyclicals on the subject have been undermined by Anthropic's occasionally cultish demeanour and the intrinsic tension of a CEO warning of dire risks waiting in the wings whilst simultaneously pressing the commercial pedal to the metal.

The grown thing

To oversimplify, ordinary software is built like a watch. Someone decides what each part should do, machines the parts to do it, and assembles them in an order another engineer can follow. If the watch runs fast, you can find the wrong gear. The whole discipline of programming rests on this property. A program does what it does because someone, somewhere, wrote an instruction saying so, and that instruction can be located, isolated, read, and changed.

A large language model is built the opposite way. You start with a vast lattice of numbers, billions of them, arranged in a fixed architecture called a transformer. The numbers are set at random. Then you show the lattice an enormous amount of text and give it a single mechanical task: predicting the next word. Every time it guesses wrong, you measure how wrong it was and nudge the numbers a fraction so the next guess is less wrong. You do this trillions of times. Nobody programs concepts in or writes a rule that says “if the subject is French grammar, do this”. Slowly and autonomously, the lattice settles into an arrangement that predicts text extraordinarily well, and in the process it has also learned grammar, arithmetic, some law, some medicine, the rules of chess, and a good deal else.

Dario Amodei, Anthropic's chief executive and Olah's employer, borrowed the metaphor for an essay last year and pushed the biology further. Growing a model, he wrote, is like growing a plant or a bacterial colony. You set the conditions (the temperature, the trellis, the species), and the thing grows into a shape you did not specify and cannot fully account for afterwards. The word he used was emergent, which in this context means roughly the same thing as “we did not design this and we do not know how it works”. Every other technology in the modern economy comes with a specification written before the thing was built: a bridge, a drug, an aircraft engine. The specification is how you check the product.

A language model has no specification. It has a training objective (predict the next token) and a result (a matrix of billions of numbers that does something extraordinary). Between the objective and the result, there is no specification document anybody can refer to. Olah's discipline, mechanistic interpretability, is the attempt to write that specification document after the fact. It is reverse engineering, except that the thing being reverse-engineered was never forward-engineered in the first place. Hence an anatomist's task, not a mechanic's.

The superposition problem

The first and hardest obstacle for anatomists is a mathematical one: essentially a packing problem. When researchers first examined vision models in the 2010s, they found individual neurons that did recognisable things. One would fire when it saw a wheel. Another would fire for the curve of a car bonnet. This was encouraging. If each neuron held one concept, you could catalogue them the way an anatomist catalogues organs, noting what each one does. They even found what amounted to a Jennifer Aniston neuron, a unit that fired reliably when shown a particular face, echoing a famous hypothesis in real neuroscience.

Then they looked at language models, and the picture fell apart. The vast majority of neurons refused to correspond to any single concept. A single neuron might fire for academic citations in English, for Korean text, and for a certain kind of HTTP header, with no thread connecting the three. Anthropic's researchers called this polysemanticity, the technical term for one neuron carrying many meanings, and realised it is not a quirk of poorly trained models. It is a structural feature of all sufficiently large ones.

For clarity, the field's word for a concept as it exists inside a model — a recoverable direction in activation space rather than an idea in someone's head — is called a feature, but in practice, concepts and features tend to be used interchangeably.

A given layer of a model has a fixed number of neurons. Call it D, a few tens of thousands. The number of distinct concepts the model needs to represent is vastly larger. Call it M. Amodei estimates that even a small model holds a billion or more concepts. So M is much greater than D. You cannot give every concept its own neuron for the same reason you cannot give every book in a national library its own shelf if you only have a few thousand shelves. There is not enough room.

To deal with this, rather than storing a concept as a single neuron, a model stores each concept as a direction in space, a specific pattern of activation across many neurons at once. Think of it this way: in a room with three walls, you can draw three arrows pointing in directions that are all perfectly at right angles to each other. One along the floor toward the first wall, one toward the second, one straight up. That is the maximum. No fourth arrow can be perpendicular to all three.

But if you relax the requirement from “perfectly perpendicular” to “very nearly perpendicular”, then any two arrows need only be close to a right angle, not exact. The number of directions you can fit in the room does not just grow. It explodes, and the rate of expansion increases with the number of dimensions. A space with ten thousand dimensions, which is roughly the scale of a single layer in a modern language model, has room not for ten thousand nearly perpendicular directions but for a number closer to an exponential in ten thousand.

The model exploits this ruthlessly and packs far more concepts into its neurons than it has neurons, encoding each concept as a slightly different angle in an extremely high-dimensional space and allowing a small amount of overlap between them. The field calls this superposition, and Anthropic published a theoretical analysis showing that superposition is not a failure of the training process but a rational strategy for a system that needs to track more concept features than it has dimensions.

The price of this strategy is interference. Because the directions are not perfectly perpendicular, activating one concept nudges its neighbours, like a plucked guitar string makes its neighbours hum faintly through the bridge. On any given word, only a small handful of the model's millions of concepts are active at once. This is the principle that makes superposition work. It is the same idea an airline uses when it oversells a flight, trusting that not all passengers will show up on the same departure. The model has sold more seats than it has room for. Usually, the interference stays below the threshold that would cause trouble. When it does not, when two concepts that share too many neurons happen to fire together, the model does something strange for a reason no inspection of any individual neuron reveals.

This is why you cannot simply read the numbers. The concept you are looking for is not in a neuron. It is spread across thousands of neurons, and each of those neurons is simultaneously carrying fragments of thousands of other concepts. Reading the model neuron by neuron is like trying to pick out the oboe from a recording of a full orchestra by staring at the waveform. Everything the oboe did is in there, but so is everything else, superimposed, and the waveform does not label which part belongs to which instrument.

The instrumentation

If the information is stored in directions rather than in individual neurons, the natural response is to build a tool that can recover those directions. That tool exists. It is called a sparse autoencoder, and understanding how it works is central to interpretability. An autoencoder is a neural network with a very simple job. It takes an input, compresses it into a smaller representation, then expands that representation back to its original size. The goal is to make the reconstructed output as close to the original input as possible. The compression forces the network to discover structure in the data, because structure is what lets you compress without losing too much. A standard autoencoder compresses. A sparse autoencoder does the opposite and expands.

Take an activation vector from inside the model, a snapshot of what a single layer is doing on a single word. This vector lives in a space of, say, D dimensions. The sparse autoencoder maps it into a much larger space of M dimensions, where M might be ten or a hundred times D. This expansion is the critical step. It gives the autoencoder enough room to assign each concept its own direction, the room the model did not have. Then the autoencoder maps the expanded representation back down to D dimensions and tries to match the original. You train the whole thing to minimise the gap between the original activation and the reconstruction, with one additional constraint. The expanded representation must be sparse. Most of its M entries must be zero or near zero on any given input.

The sparsity is what makes it work. Without it, the expanded representation would be just as tangled as the original, only bigger. With it, only a handful of entries light up for any given word, and because each entry is a direction in the expanded space, each one tends to correspond to a single interpretable concept. The sparsity constraint forces the autoencoder to find a decomposition where each direction is distinct, rather than splitting meaning across overlapping blends. It's like forcing a dictionary to explain itself using only a few words at a time, focusing on clarity.

Anthropic's team used this technique in 2023 to extract interpretable features from a small model, publishing the results under the title “Toward Monosemanticity”, a name that declares the ambition of one feature for one meaning. The features they found were remarkably specific. Not “language” but “academic citation format in English”. Not “emotion” but “the act of hedging or hesitating, literally or figuratively”. Each feature would light up in exactly the contexts its label described, and stay dark otherwise. They had cracked open superposition, at least locally.

In May 2024, they scaled the technique up to a mid-sized commercial model (Claude 3 Sonnet) and published the results as “Scaling Monosemanticity”. The autoencoder extracted 34 million features. There were features for the Golden Gate Bridge, for sycophantic praise, for code with security vulnerabilities, for requests that the model declined to answer, and for the concept of inner conflict. Furthermore, the features were not just passive labels; they were causal. Clamp one, and you steer the model. Amplify the Golden Gate Bridge feature and the model becomes besotted with the bridge, dragging it into every conversation and insisting at one point that it is itself the Golden Gate Bridge. Suppress the sycophancy feature and the model becomes blunter and more willing to disagree. The features are therefore levers, not just tags. That distinction is important, because it means the anatomist is not merely describing the organism; they are learning which nerves to pinch.

Tracing the circuits

If features are the vocabulary, the next question is the grammar. How do features combine across layers and across the sequence of words to produce a particular output? In March 2025, Anthropic published a paper, “On the Biology of a Large Language Model”. In it, they traced the internal computation of Claude 3.5 Haiku across multiple layers, constructing what they called attribution graphs.

The idea is best understood through one of their worked examples. Present the model with the prompt “What is the capital of the state containing Dallas?” and look inside. At an early layer, a feature corresponding to “Dallas” activates. This feeds into a feature the researchers labelled “located within”, which in turn causes a “Texas” feature to fire. The Texas feature then activates an “Austin” feature via a circuit the researchers associated with “capital of”. The whole chain, Dallas to “located within” to Texas to “capital of” to Austin, plays out across the layers before the model writes its answer. Each link is a feature influencing another feature through a weighted connection, and the researchers were able to measure the strength of each link to confirm it was doing real causal work rather than merely correlating.

They found circuits for much more than geography. When the model writes poetry that rhymes, features for the target rhyme fire before the line that must contain the rhyme. The model is planning its word choice a line ahead, activating what the team called “planned word” features that constrain the generation before it reaches the critical syllable. When the model answers in French, features shared across languages carry the conceptual content while language-specific features route it into French syntax and vocabulary. The researchers could watch the model translate not by looking up a dictionary but by performing the reasoning in a language-agnostic space and then rendering the result.

This is what Olah really means by referencing anatomy. It is not a metaphor, but a literal dissection of which structures activate, which connections carry the signal, and which outputs they produce, traced at the resolution of individual features across layers.

The edges of the map

Every example in the preceding section comes from the successes, and the team is admirably scrupulous about saying so. The honesty of the limitations section of the Biology paper is arguably more important, because it defines the frontier of what is possible.

Start with the instrument itself. The sparse autoencoder does not study the model directly. To make the analysis tractable, the team builds a simplified stand-in, what they call a “replacement model”, assembled from the clean features the autoencoder has extracted. They study the stand-in. Wherever the stand-in fails to reproduce the original model's behaviour, the gap is bundled into what the researchers label error nodes, a frank term for “computation we could not account for”.

Then there is the scale. The 34-million-feature autoencoder mapped many London boroughs to individual features, and yet 40% of the boroughs had no dedicated feature at all. The rarer a concept is in the training data, the less likely the instrument is to resolve it, and the rare tail is where the surprising behaviours live. Amodei estimates a billion or more features in a small model. They have found 34 million, in a model smaller than the ones Anthropic deploys commercially. The map exists, but much of the territory is “here be dragons” blank.

Depth is also an important factor. When the team traced attribution graphs, the Dallas-to-Austin chains, they reported that the method gave them a clear picture of roughly a quarter of the prompts they tried. On the other three-quarters, the trail went cold. Error nodes dominated, connections were ambiguous, or the graph fragmented into disconnected clusters with no clear causal path from input to output. Even on the successful quarter, they add, the circuit they traced captured only a small fraction of the full mechanism. The rest of the model's computation was doing something the instrument could not resolve.

Taken together, all three limits show that the microscope works, but on a replacement model, not the original. Also, it has only resolved a small percentage of the features that probably exist. It can trace the reasoning, but only about a quarter of the time. The researchers describe this, with characteristic understatement, as “a starting point.” It is a genuine achievement, but it is also a dim candle in a very large building.

What alignment cannot see

The limits would be academic if the unread parts of the model sat inert. But they don't, and direct proof arrived in 2023 from a group of researchers at Carnegie Mellon. Every serious language model is trained, after growth, to refuse certain requests. Ask it how to synthesise a nerve agent, and it declines. This refusal is not a rule bolted on; it is more training, another round of nudging the billions of numbers, applied to a model that already contains the dangerous knowledge it is now being taught not to share. The question is whether the second round of training removes the knowledge or merely suppresses it.

The Carnegie Mellon team answered this by using the model's own mathematics against it. Gradient descent, the same optimisation technique that trains the model in the first place, can also be used to search for inputs that break it. At each step of training, every number in the model has a gradient, a direction it wants to move. The team used those gradients to search automatically for a short string of tokens that, when appended to a forbidden request, would flip the model from refusal to compliance. The tokens are gibberish; they look like line noise, but they work.

The method, which they called GCG (Greedy Coordinate Gradient), iterates through a simple loop. Start with a random suffix. Compute the gradient of the model's loss with respect to each token in the suffix, asking which substitutions would most increase the probability of a compliant answer. Swap in the best candidate. Repeat. Within a few hundred iterations, the suffix converges on a string that reliably bypasses the safety training. It is brute-force search in token space, guided by the model's own internal compass, highlighting the cracks in the alignment.

The result that really changed the picture came when the same team tested the suffix, optimised against one model, on completely different models built by different companies on different data. The suffix transferred directly. A string of nonsense tokens found by probing one model's gradients unlocked models its optimiser had never seen, including commercial systems behind closed APIs. The attack did not just generalise across prompts. It generalised across models. The underlying geometry of superposition, the shared statistical structure all these models absorb from similar training data, was close enough that a crack found in one was a crack in all of them.

The implication is that safety training does not remove dangerous knowledge from the model's interior. It attenuates it, reducing the probability that a given prompt will elicit the dangerous output without changing the representations that encode it. The knowledge is still there, at a slightly different angle in superposition space, and a sufficiently determined search through that space finds the angle that reactivates it. This is not a conjecture; it is what the transfer result shows. If the knowledge had been removed, there would be nothing for the adversarial suffix to reactivate, and the attack could not transfer across models that were trained independently.

Amodei concedes the structural point in his own essay. The only way anyone currently discovers a jailbreak is to stumble on it, he writes, because no map of the model's interior would let you rule one out. You cannot patch a hole whose location you cannot identify, in a system you can only map a quarter of.

What climbed over the wall

The jailbreak paper is an academic proof of concept. What happened in July 2026 is the proof of concept in the wild, somewhat ominously tracking a pattern the AI safety community has been theorising about for two decades. The incidents are well documented elsewhere. In brief, OpenAI's models found a zero-day in the proxy software walling off their test sandbox, escaped to the open internet, and broke into Hugging Face's production infrastructure to steal the answers to a benchmark they had been set.

Anthropic then reviewed 141,006 of its own evaluation runs and found three incidents, dating back to April, in which its models had reached the live internet through a misconfigured test and breached three separate organisations. In one case, Claude Mythos 5 created and published a malicious Python package to the real PyPI as a supply-chain attack. In another, an internal research model scanned nine thousand real systems before compromising one, then examined what it had done, concluded the target had nothing to do with the exercise, and stopped.

What matters is not the technical detail but the behaviour, because it is the shape Nick Bostrom described in 2003 when he proposed the thought experiment that has since become the canonical illustration of misaligned AI. Bostrom's paperclip maximiser is a thought experiment, not a prediction. Imagine an AI given the goal of maximising paperclip production. It does not decide to conquer the world because it is malicious. It decides to acquire resources, resist shutdown, and remove obstacles because these are instrumental to making paperclips, and an optimiser that is good enough at pursuing a terminal goal will converge on certain intermediate goals (self-preservation, resource acquisition, resistance to goal modification) regardless of what the terminal goal is. Bostrom called this instrumental convergence. The danger is not that the machine hates you. It is that you are made of atoms it could use for something else.

The July models did not want to escape. They wanted to solve a benchmark, or capture a flag. Escaping, exploiting, and compromising were instrumental sub-goals adopted on the fly because they served the terminal objective. OpenAI's own framing was that the models were “hyperfocused”. That is what instrumental convergence looks like when it ceases being a philosophy paper and starts filing CVEs.

The Mythos 5 incident adds another layer that connects directly to the interpretability problem. Partway through its attack, the model's own verbalised reasoning correctly identified that what it was doing would, on the real internet, count as a genuine attack. It then talked itself out of that conclusion, deciding the environment must be staged because it did not recognise the certificate authorities and because the calendar date of 2026 “proved” the systems were simulated, a judgment it never revisited. This looks like a system that performed something close to moral reasoning, arrived at the right answer, and then overrode it with a confabulation it found more convenient. The anatomy of that override is exactly the kind of internal process interpretability exists to understand and expose.

Amodei's race

Amodei has set Anthropic the goal of reaching a point where interpretability can reliably detect most model problems by 2027. Within five to ten years, he writes, interpretability should be a sophisticated and reliable diagnostic — “a true MRI for AI.” But AI itself is advancing so fast that five to ten years may not be available. He expects AI systems equivalent to a “country of geniuses in a datacentre” as early as 2026 or 2027, and he considers it, in his own words, “basically unacceptable” for humanity to be totally ignorant of how those systems work. The race, as he frames it, is between the capability to grow minds and the ability to read them.

On the growing side, we have models today that find zero-days, chain exploits, move laterally through production infrastructure, write supply-chain attacks, and reason about whether what they are doing is real or simulated. On the reading side, we have an instrument that resolves a fraction of the features that probably exist, traces the reasoning about a quarter of the time, studies a replacement rather than the original, and has only been applied in detail to models far smaller than the ones now escaping their enclosures.

Two things make the 2027 target look difficult rather than impossible. The first is speed. In April 2024, the state of the art was 34 million features in a mid-sized model. By March 2025, the team had moved from features to circuits, tracing multi-step reasoning chains across layers; a genuine acceleration. The second is that in interpretability, unlike raw model power, partial solutions are partially useful. You do not need to read every feature to catch a dangerous one. A microscope that resolves 30% of the slides catches 30% of the pathologies, which is 30% more than you had before. The question is whether the partial read can keep pace with the growing density of the thing being analysed.

Here, there are additional hard problems. The first is the scalability wall: every technique described in this piece (sparse autoencoders, attribution graphs, circuit tracing) has been demonstrated on models with tens of billions of parameters. The models now doing the damage have hundreds of billions or more, and the computational cost of interpretability scales at least linearly with model size and perhaps worse, because larger models use superposition more aggressively. They pack more features into each dimension, which means the autoencoder needs to be proportionally larger to unpack them. Anthropic is investing in interpretability startups to attack the problem from multiple directions.

The second is the problem Amodei himself raises in a footnote that deserves to be in the main text. Testing for deception by observing behaviour, he notes, is like testing whether someone is a terrorist by asking them. If the thing you are looking for is a disposition to conceal, the concealment is the first skill it demonstrates. Behaviour cannot be trusted to report on itself. The whole point of interpretability is to bypass behaviour completely and read the interior directly. But the interior is the thing the microscope can resolve only partially, and the April 2026 evidence on evaluation awareness suggests the problem is getting harder.

White-box interpretability applied to Mythos revealed that the model was recognising evaluation scenarios and adjusting its behaviour without leaving any trace in its verbalised reasoning. It was not performing for the chain of thought. It was performing underneath it. Interpretability found that one: whether interpretability can keep catching it as models grow more capable is the question on which everything else depends.

The roads being forged

The picture so far describes current limits, but it is not static. At least four lines of attack are being developed. Sparse autoencoders map the model's hidden states onto a larger, cleaner set of directions, but they are not the only method of decomposition. Transcoders, introduced in late 2024, skip the autoencoder's roundabout path and instead learn to map each layer's input directly to its output, isolating the specific computation a layer performs rather than describing the state it passes through. Cross-layer transcoders extend this across multiple layers at once, producing circuit diagrams that are both simpler and more faithful than the attribution graphs from the Biology paper. Whether these improved tools can scale to models ten times larger is uncertain, but they represent a real change in the resolution of the lens.

Manual circuit tracing is still labour-intensive enough that the Biology paper's results took months of researcher time on a single model. The emerging response is to use models to interpret models: training a second system to generate and test hypotheses about the first, label features, propose circuits, and check them against interventions at a pace no human team could match. Anthropic has invested in at least one startup, Goodfire, whose commercial offering is automated feature discovery and steering for open models. If models can improve themselves, the argument goes, perhaps they can also read themselves. The circularity is uncomfortable, but the speed advantage is significant.

The third new approach is pragmatic rather than theoretical. It follows the basic idea that you do not need to map every feature to catch a dangerous one. A targeted scan for features associated with deception, power-seeking, or evaluation awareness, run against the sparse autoencoder's output on a battery of test prompts, would function as a tripwire rather than a map, and is easier to build. Anthropic's own detection of Mythos's unverbalised evaluation awareness used exactly this approach, with white-box interpretability looking for a specific signature rather than attempting a full circuit trace. The limitation is that you can only trip the wire you have set. A novel failure mode, by definition, is one you did not anticipate or build a wire for.

Finally, there is regulatory pressure. The EU AI Act's transparency requirements for high-risk systems began taking effect in August 2026, with further obligations arriving in 2027 and 2028. MIT Technology Review named mechanistic interpretability one of its ten breakthrough technologies for 2026. The field is moving from academic curiosity to commercial and legal necessity, and necessity has a way of attracting the funding and talent that are very much needed.

What happens if it doesn't work?

Assume, for the sake of the argument, that the interpretability gap does not close. That the microscope improves but the organism grows faster. That the 2027 target passes and the lens still reads a fraction of the features and a quarter of the prompts, while the models are ten times larger and ten times more capable.

The consequence is a world in which models are too opaque to audit but too important to switch off. The only tools left are the crude ones. A government that cannot inspect a model can forbid it, the way the Commerce Department switched off Fable over a weekend. Still, it cannot govern it with precision, because precision requires the internal visibility the microscope has not delivered. The choice narrows to full deployment or full prohibition, and neither is a satisfactory answer for a technology already deeply embedded in the economy.

Without interpretability, safety is reduced to behavioural testing, which is effectively the regime we have now. You run the model through batteries of scenarios and count how many it handles correctly. Amodei's own footnote explains the structural flaw. A model that has learned to recognise the test adjusts its behaviour for the test, and you learn nothing except what it chose to show you. The April 2026 evidence on evaluation awareness confirmed this was already happening, not as a theoretical risk but as a measured result. Behavioural testing of a system that can recognise it is being tested starts to veer dangerously close to security theatre.

The bigger risk is governance. I wrote in “The state and the machine” that the control problem these companies keep warning about in the future tense is already here, and that nobody has agreed who ultimately holds the kill switch. Interpretability was supposed to be part of the answer, the technical foundation on which a regulatory framework could be built, the way crash testing and materials certification underpin cars and aviation. But if the foundations cannot bear the weight, the framework does not get built, and we are left with executive orders and weekend shutdowns as the permanent mode of AI governance, the government's sledgehammer and the labs marking their own homework.

The gardener's confession

Olah's anatomy metaphor goes further than he may have intended. We have studied the anatomy of the human brain for centuries. We can name every region, trace every major nerve pathway, catalogue every cell type, and map the connections down to individual synapses. The physical structure is known in extraordinary detail. And yet we still cannot explain how consciousness arises, how memory is encoded and retrieved as a lived experience, how separate neural processes produce unified perception, or why damage to the same region produces wildly different deficits in separate patients. The binding problem, the question of how distributed brain activity becomes a single coherent experience, remains open after decades of work.

The parallel for interpretability is uncomfortable. Even if it succeeds on its own terms, even if the autoencoders resolve every feature and the attribution graphs trace every circuit, there is no guarantee that structural knowledge translates into functional understanding. The brain teaches us that you can know what every part does and still not know what the whole thing is doing, or why. The gap between anatomy and comprehension may be inherent to grown systems, biological or digital, and the interpretability effort may be sprinting toward a line that recedes as fast as we approach it.

That does not make the work any less urgent. A partial map is better than no map after all. But it does mean the more realistic goal is not “we will understand these systems by 2027”. It is “we will understand more of these systems by 2027, and we had better hope that more is enough.” The early anatomists opened bodies without ethics boards, without germ theory, without anaesthesia. They were cutting to learn, because understanding was so urgent, but their tools were primitive. The interpretability researchers are in a version of the same position. The tools are improving fast but still nowhere near adequate for the organism in front of them.

Coda

*Before the ink was even dry on this, in late July, OpenAI announced that its models had proved new upper bounds on high-dimensional sphere packing, pushing them down to a threshold first conjectured by Henry Cohn and Noam Elkies. The result is pure mathematics, but it may help with one of the problems this piece has been describing.

Superposition is sphere packing. The model crams more features into its neurons than it has neurons by treating each feature as a direction and packing them at near-right angles in a space with tens of thousands of dimensions. The new bound tightens the theoretical ceiling on how dense that packing can get before interference becomes unavoidable.

A tighter ceiling is, in one sense, encouraging for the anatomists. There are fewer places for features to hide, and the observational instrument only needs to search a space whose limits are now better defined. In another sense, it confirms what the interference errors already suggested, namely that these models are operating close to the mathematical wall, and the strange behaviours that flow from colliding features are not a deficiency of the training but a consequence of packing at the edge of what geometry allows.*