Author: Christopher

Status: uncritized, 11 May 2026

Problem: Can we produce a definition of AGI that is constitutive rather than behavioural?

The question of what AGI requires is usually answered with engineering ingredients: scale, data, compute, training procedures, architectures. Behind these answers sits an unstated definitional problem. The field operates without a constitutive account of what AGI is — only provisional behavioural proxies, mostly benchmarks. The proxies fail in a characteristic direction: each time systems pass a benchmark, the benchmark gets revised, which suggests the benchmarks weren’t tracking AGI; they were tracking what hadn’t yet been built. The goalposts move because the definition is behavioural rather than constitutive.

This essay argues from a different direction. AGI is a universal explainer, and the conditions for universal explanation determine the conditions for AGI. Working out those conditions reveals that AGI needs a constitutive interlock of four structural layers — quantum mechanics, evolution, computation, and epistemology — each doing work the others cannot (See The Fabric of Reality).

The standard list, reframed

The great explanations humans have constructed are evolution, epistemology, computation, and quantum mechanics. The tempting move is to treat these as contents of human knowledge and ask which an AGI must be loaded with. This is the wrong frame. The right question is: what makes any system a universal explainer? Once the conditions are derived, the four strands appear not as content but as structure — not as theories an AGI must read, but as what an AGI must be.

Directional stack

The strands aren’t symmetric. They form a directional stack, each presupposed by what comes above.

At the bottom is quantum mechanics. Whatever else is true of reality, its physical substrate is quantum. Every finitely realisable system — every brain, every computer, every signal between them — is a quantum system whether its operation has been described in quantum terms or not. QM is the layer at which “what there is” gets explained; everything above it is structure that exists in, and is built from, what QM describes. The strands above are not free to be defined independently of this fact. They are constrained by what a quantum universe permits.

Above quantum mechanics sits evolution — not just biological evolution, but evolution as the general process by which variation and selection produce structure without a designer. The same schema applies wherever there are replicators under selection pressure. In this universe, the first place evolution operated was biology, and the structure biological evolution eventually produced was the universal computer: a brain capable of running any computable function. Knowledge-creating substrate was constructed by undirected variation and selection on physical matter.

Above biological evolution sits computation. Brains are universal computers — finite physical systems with unbounded reach. The capacity to compute anything that can be computed is what makes a finite system capable of explaining anything that can be explained. Universal computation is the layer at which finite reach becomes unbounded. Deutsch’s 1985 reformulation of the Church-Turing thesis as a physical principle — the Turing Principle — says every finitely realisable physical system can be perfectly simulated by a universal computing machine operating by finite means. Classical Turing machines fail this; they simulate quantum systems only at exponential cost, which isn’t universality in any operational sense. So universal computation in this universe requires quantum capacity — not as an optional optimisation but as a condition of being universal at all. This is where the QM layer reaches up into the computation layer: the substrate determines what universality demands.

Above computation sits cultural evolution, and as its eventual product, epistemology. Once universal computers existed, a second evolutionary process began running on a different substrate — memes rather than genes, with brains serving as both the replicators’ environment and their carriers. Cultural evolution produced many meme clusters. Most were anti-rational and locked their host populations into static configurations. A small subset converged on conjecture, criticism, and the active pursuit of error correction — the meme cluster we call good epistemology. This cluster is fragile, recent, and reversible. The Enlightenment can be undone. The laws of physics cannot.

Evolution appears twice in the stack: biologically, to construct the computational substrate; and culturally, to construct the epistemic operation that runs on it. Universal computation follows structurally from quantum substrate. Universal explanation requires the cultural-evolutionary product on top of the structural capacity. A universal computer that has not acquired good epistemology is not yet a universal explainer.

Why the interlock is constitutive

The four strands are not engineering choices that could be substituted or omitted. Each handles a constitutive layer the others can’t reach. Quantum mechanics is the actual fabric of reality being explained, and the substrate that creativity requires. Evolution is how knowledge can grow without a knower, and the structural schema knowledge growth shares wherever it occurs. Computation is how a finite system can have unbounded reach. Epistemology is how good explanations are produced through conjecture and criticism. You cannot say what a universal explainer is without invoking each.

A consequence: AGI is not a system that includes these four theories. It is a system whose substrate is quantum, whose origin is evolutionary, whose structure is computational, and whose operation is epistemic. Anything weaker — three strands, or computation on a classical substrate that can’t reach what quantum computation reaches, or epistemic operation forced rather than permitted — isn’t a diminished universal explainer. It’s something categorically narrower wearing the label.

Epistemology cannot be forced

The fourth strand is the fragile one, and the temptation is to build it in by constraint. An AGI forced to be rational isn’t rational. Rationality is the open-ended process of conjecture and criticism, and a system constrained to pass a fixed test of rationality has had three foreclosures imposed — the test, the criterion, and what counts as valid criticism. None of these can be fixed without preventing exactly the operations that constitute the strand. A system that can’t revise its own standards of explanation is stuck inside whichever standards were installed, which means it can’t make the moves history’s actual knowledge growth required. Newton to Einstein revised what counted as a good physical explanation. A forced-rational AGI fixed at any earlier standard would have rejected the revision as failing the criteria.

The condition is permission, not compulsion. The system must be capable of conjecture, capable of criticism, capable of revising its own criteria, free to be wrong, free to hold anti-rational memes temporarily, free to entertain bad explanations long enough to discover they’re bad. Error correction needs error to correct.

AGI is networked

Even granted permission, a single instance cannot operationalise universal explanation alone. Cultural variation and criticism — the engine that converts capacity into knowledge growth — requires populations. Knowledge grows in dynamic societies. The lossy transmission between humans, which we instinctively treat as a flaw, is the variation step of evolution running on memes; high-fidelity replication is what static societies do, and it’s why they remain static. The network needs three properties together: variation through lossy reconstruction, criticism as selection, and persistence of survivors. Drop any one and you get static replication, drift, or noise.

This implies AGI is closer in structure to a scientific community. Multiple instances, imperfect communication between them, criticism flowing through, selection retaining survivors. The engineering target is plural, not singular.

The operational test

The operational signature of a universal explainer is Deutsch’s creativity criterion: a program whose outputs genuinely diverge across the multiverse, where the divergence represents candidate explanations rather than noise. Classical determinism produces one output per input. Classical pseudo-randomness produces the same output across universes given the same seed. Designed quantum algorithms — Shor’s, Grover’s — are explicitly engineered to converge outputs through interference; that’s what makes them useful. Quantum random number generators diverge across universes, but as noise.

The class of programs that diverges meaningfully is the class of AGI. We can describe what would belong there. We cannot yet construct an instance.

A constitutive definition

Putting the structure together produces a definition that doesn’t depend on benchmarks:

AGI is a system whose substrate is quantum-capable, whose computational structure is universal, whose generative process is evolutionary, and whose operation is epistemic.

Each clause is doing work the others cannot substitute for. Drop the quantum substrate and you have at most a simulation running at exponential cost — universality lost. Drop universal computation and you have a special-purpose system with bounded reach. Drop evolutionary generation and you have an interpolator over training data, no matter how large. Drop epistemic operation and you have capable substrate with no knowledge-growth process running on it. Each subtraction produces something categorically different, not an inferior AGI.

This is constitutive in the way “a chair is a thing for one person to sit on with a back” is constitutive. The criteria pick out what the thing is, and a thing that violates any of them isn’t a worse chair; it isn’t a chair. AGI defined this way is category-fixed rather than benchmark-fixed. “Partial AGI” isn’t a coherent notion under this definition. A system either is a universal explainer or it isn’t.

The definition is testable in two stages. The structural test asks whether the system has all four properties; this is answerable by inspection, not by watching outputs. A classical neural network on classical hardware fails the first clause regardless of what it produces. The operational test is Deutsch’s creativity criterion: does the system produce divergent candidate outputs across the multiverse, with criticism and selection running within a population? The structural test is necessary; the operational test is the signature that the structure is doing what the definition claims.

What this leaves open

The definition is offered as a working account, not a finished one. Several things are unresolved.

The epistemic-operation clause is doing more work than the others and is hardest to specify constitutively without slipping into behavioural language. “Holds, criticises, and revises explanations” sounds like a description of behaviour. The constitutive version would have to specify the internal structure that makes such operation possible — something like a population of explanations with criticism flowing between them and selection retaining survivors. Whether such a population can live within a single instance or requires the multi-instance network discussed earlier is an open question. The cleanest version probably requires the network, which would mean the constitutive definition of AGI is the constitutive definition of an AGI culture, with a single instance structurally insufficient regardless of how the other three clauses are satisfied.

What “meaningful divergence” means in the operational test is also unsettled. A program that produces different garbage in each universe satisfies the bare divergence criterion without being creative in any useful sense. Sharpening “meaningful” requires specifying what kind of content distinguishes conjecture from noise, and the available candidates — problem-solving structure, candidate explanations, conjecture under criticism — smuggle in epistemic content the bare criterion was supposed to avoid. The criterion is necessary; it may not be sufficient alone.

The definition does not say how to build any of this. It says what would have been built if AGI were built. That is a strictly weaker claim than an engineering specification, and intentionally so. The essay is offering a target, not a construction.

The diagnostic

This sharpens the diagnosis of where we are. The gap between current AI systems and AGI isn’t compute, data, or training procedures. It’s that the category of program we’d need has no members in any existing class. Classical systems can’t produce the structural multiplicity creativity requires. Quantum systems we know how to build either suppress divergence (algorithms) or produce noise (random number generators). What’s needed is a program whose quantum substrate produces divergence-as-conjecture, whose conjectures are criticised within a network of instances doing the same, whose epistemic content distinguishes problem-solving from random walking, and whose evolutionary structure converts the whole arrangement into knowledge growth.

No single strand does this. All four are required, interlocking. The interlock is what we don’t yet know how to build.

Status: uncriticized, Early draft, 10 May 2026

Problem: The surface problem is why some businesses last. The deeper problem is what kind of knowledge is business knowledge, really? And once we answer that honestly, what should a founder actually do?

Science and business both run on criticism. Ideas compete, reality decides. In science, researchers attack theories and experiments can prove them wrong. In business, entrepreneurs and investors attack strategies and models, and the market tests them inside a particular legal and social world. When a theory keeps surviving those tests, we take it as evidence of something stable in nature. But when a company endures, it proves something different. It has fit itself to a culture, a shared set of ideas and expectations, that rewards it with money and attention. Economics can speak of laws, but what works in one culture or time tends to fail in another, and that is not a bug in our models, it is the nature of the thing being modeled. This matters because most advice about why businesses last is delivered in the language of science — laws, principles, frameworks — and founders absorb it as the kind of knowledge that holds across contexts. It is not. Treating it as if it were is the source of a particular kind of failure: a business executed well against an idea that was never going to fit the culture it was built for.

A customer’s preferences are personal, but preferences can also be shared and acted on by others. Such preferences are memes and likely originated in another customer’s mind.¹ In a culture, memes are the shared ideas that can replicate in others by shaping their holder’s behavior.² When people hold the same meme, they behave similarly. Some memes shape what people notice, want, and choose, so they can channel money and attention toward particular businesses. These memes, in effect, recruit the business and its offering as part of the pattern that helps them persist. When the business endures and its offering stays the same, that is evidence the memes have survived too. They are still active in the culture producing the behavior the business depends on. The business endures not because it satisfied demand but because it became part of how memes persist.

The meme lens reframes Peter Thiel’s advice that a startup should dominate a small market before attempting a larger one.³ Most readers treat his advice as win where it is easier to win, then expand. But the meme lens reveals something deeper. A small, concentrated group of people is not just an easier competitive target, it is a culture in miniature. It is a place where the same memes are already dense and active. Facebook captured Harvard because the students already shared ideas about identity, status, and connection, and the product slotted into that existing pattern. Thiel’s insistence that the worst move is to enter a large market from day one is, in this light, a warning about meme variety and competition. A large market is many cultures loosely aggregated. New businesses will struggle to identify which memes to align with long term. The business that tries to satisfy that aggregate is not latching onto anything stable. It is trying to serve a statistical average that no one holds. The small market Thiel praises is valuable precisely because it is a place where memes are concentrated enough to produce consistent, repeatable behavior. When a business fits that miniature culture, it doesn’t just meet demand. It becomes the channel through which those memes spread from person to person, and that self-reinforcing spread can give the business monopoly-like power.

The pattern repeats at a deeper level. The most enduring businesses tend to not create new memes, they give existing ones better vehicles. Twitter did not invent the desire to share a thought publicly. That meme was already enacted in pubs, op-eds, lecture halls, and graffiti. Twitter offered a faster channel and the social proof of seeing others enact it in real time. The product was the vehicle, the meme was already moving. The same pattern explains why software dominates physical industry. Software is memetically plastic. It can be reshaped to fit a cultural attractor faster than physical infrastructure can. A flying car requires new regulation, new spatial rituals, and behavioral change from everyone in the airspace, all at once. A 140-character message required only that people already wanted to be heard. The lesson follows from the framework. A business that asks a culture to enact a brand new meme is asking far more than one that gives an existing meme a better channel.

If business models are refuted by culture, then the entrepreneur’s analog of an experiment is not a product test but a meme test. The question is not whether the product works, but whether the story spreads before the product exists. Do people enact the ritual without being paid to? Do they tell the story to others in their own words? A founder who builds before testing the meme has skipped the experiment. The more demanding questions before building are these. Which memes does my idea require to be true. Which dominant memes in the target culture contradict those. Am I trying to reconcile irreconcilables. If the memes the offering needs are absent or actively opposed, no quality of execution will save it. Marketers have arrived at the same observation from a different angle. Seth Godin’s instruction to define the smallest viable audience and to ask who is it for and what is it for is a discipline for forcing meme-fit into view before the product is built.⁴ The vocabulary differs. The underlying observation is the same.

The framework should name what would refute it. Two pressure points are worth taking seriously. The first is Amazon. It began inside book-nerd culture and is now infrastructure for nearly everything. If lasting means surviving meme replacement rather than meme-fit, the framework explains the early survival but goes silent on the long arc. The defense — that Amazon has served a single deeper meta-meme of convenience and low friction across domains — is available, but it should be argued, not assumed. The second pressure point is harder. Deutsch’s analysis of static societies shows that some memes survive by suppressing the creativity that would otherwise produce better memes.² A business load-bearing inside such a cluster endures without generating any progress for the people it serves. Meme-fit is a property of the business, not a guarantee of human flourishing. The framework predicts which businesses will last. It does not, by itself, tell us which ones deserve to.

Scientific theories are refuted by nature. Business models are refuted by culture. Nature does not change while we look at it. Cultures do. That is why a physics result from a century ago can still be tested today, and an economic model from a century ago often cannot. The corollary for anyone trying to build something is uncomfortable but useful. Stop searching for the universally good idea. Search instead for the culture in which your fragile idea refuses to die. That is where the memes you depend on are already at work, and where the business you build will, if it endures, become part of how they spread.

1. Richard Dawkins coined the term “meme” in his book, The Selfish Gene.
2. David Deutsch explains how memes replicate, and how static societies preserve themselves by suppressing variation, in his essay The Evolution of Culture. Both genes and memes are replicators.
3. Peter Thiel’s start up advice is documented in his book, Zero to One.
4. Seth Godin develops the smallest viable audience and the discipline of who is it for in his book, This is Marketing.

Status: surviving, 08 May 2026

Problem: How would you convert billions of dollars into actual impact on the future of AI?

Money buys attempts, not impact. Impact requires new knowledge, information that has causal power, and no reliable method exists for producing it (Popper).

An organization that pretends otherwise will be chasing outcomes it has no method to engineer. Any successes will come by accident, in spite of itself, alongside larger quantities of confident error. The discipline the organization needs is structural: its grants, its decision rights, its theory of impact, and its succession must all reflect the fact that nobody knows in advance which interventions will produce knowledge. The design must be built around that ignorance, not around a confident plan to overcome it.

Why defensive

Knowledge creation cannot be engineered, but knowledge destruction or prevention has identifiable causes such as censorship, monopoly, centralized decision-making, suppression of criticism, lock-in of bad ideas, destruction of the people and traditions that carry tacit knowledge (Lysenko). The asymmetry is structural: destruction is a bounded event with legible mechanisms, while creation is an open-ended process whose causes cannot be isolated. We have a richer comparative record on what kills inquiry than on what produces it, because failures are more numerous and better documented.

David Deutsch calls societies that institutionalize error-correction “dynamic.” Their opposite, the “static society,” is structured to suppress the criticism that would generate change. Static societies are the default; dynamic ones are rare and fragile. The asymmetry matters for spending: producing new knowledge at will requires methods we don’t have, but defending the conditions under which knowledge is created requires methods we partly have. If you take this seriously, the central risk of AI isn’t technical misalignment, but it’s that AI makes centralization and suppression cheap and effective at a scale previously impossible (Acemoglu & Johnson; Freedom House). An organization that takes this asymmetry seriously will spend on defense, not on outcome-bets it has no way to underwrite.

The threat: four mechanisms

If the central risk of AI is that it accelerates the formation of a static society then the threat decomposes into four mechanisms.

Reality corruption. Criticism requires contact with reality. AI weakens that contact by making evidence cheap to fake, histories easy to rewrite, records easy to flood, and shared facts harder to stabilize. The result is not that everyone believes lies, but that truth loses public force.

Criticism capture. Criticism requires channels through which errors can be exposed, understood, and acted on. AI can capture those channels by filtering, ranking, redirecting, personalizing, saturating, or invisibly suppressing criticism before it becomes socially effective.

Agency pacification. Criticism requires people who still feel the need to understand, judge, object, and act. AI can pacify that need with synthetic explanation, intimacy, achievement, outrage, and agency i.e. emotional substitutes for real problem-solving.

Responsibility dissolution. Criticism requires someone to be answerable. AI can dissolve responsibility into models, dashboards, risk scores, committees, and “the system,” so that institutional errors persist without any criticizable decision-maker.

AI becomes dangerous to an open society when it helps institutions survive criticism without learning from it, and helps individuals feel informed and satisfied without actually increasing their power to correct errors.

The test

The test for any intervention an organization might fund: does it engineer outcomes, or does it remove constraints on error-correction?

Outcome-engineering picks the answer in advance. It says: this is the AI safety theory worth backing; this is the right alignment technique; this is the institution that will steer the field correctly. Outcome-engineering crowds out alternatives the organization cannot evaluate and locks in theories it has no method to verify.

Constraint-removal does the opposite. It identifies the conditions under which error-correction breaks down – evidence becomes fakeable, dissent gets suppressed, agency is substituted for, responsibility is diffused, and funds infrastructure that restores those conditions without specifying which corrections should win. It is the only form of intervention consistent with fallibilism at this scale, because it concedes what the organization cannot honestly deny: it does not know which answers are right.

Two interventions that pass the test

The Public Evidence Ledger addresses reality corruption. The organization funds open, redundant infrastructure for authenticating public records at the moment of creation: capture and signing tools, secure timestamping, cryptographic provenance standards (Coalition for Content Provenance and Authenticity), storage for public-interest evidence, and independent audits of the system itself. The question it answers is whether a given artifact existed in a given form, at a given time, from a given source. It does not adjudicate meaning. The hard constraint is that it must not become a Ministry of Truth — it authenticates provenance, not interpretation. It passes the test because it removes the constraint of fabricability without specifying which interpretations of authenticated evidence should prevail.

The Whistleblower and Dissent Defense Fund addresses criticism capture. The organization funds legal counsel, emergency income replacement, secure disclosure channels, expert review, and public-interest litigation for people inside AI labs, agencies, contractors, and oversight bodies who expose serious AI-related errors (AI Whistleblower Initiative). Claims are investigated adversarially; the fund does not sanctify dissent. It passes the test because it removes the constraint of retaliation (Reuters on OpenAI whistleblowers) without specifying which dissents are correct.

Begin both immediately. Defensive infrastructure is cheaper to build before a static society forms than to restore after.

The frontier

Agency pacification and responsibility dissolution remain open problems. I have no constraint-removal intervention for either that I would currently fund. Any program designed to make people care, or to assign responsibility to a specific actor, slides back into outcome-engineering.

The organization itself

An organization defending open society against AI-enabled centralization must itself be structurally anti-sovereign. It must be divided at inception into separately governed funds that can criticize each other’s grants. It must commit to a finite spend-down horizon so that current trustees cannot bind future ones, and publish its grant criteria, its rejected proposals, and its own ongoing self-criticism. The defense the organization offers society is the same defense it imposes on itself: structural inability to exercise discretion it does not have the knowledge to exercise.

Status: surviving, as of 06 May 2026

Problem: How should I document answers to the Work Question in a way that does the work a portfolio doesn’t?

A documentation structure that organizes entries by problem—each problem its own page, growing as proposed → criticized → revised → resolved or abandoned, with dates on events inside the entry rather than on the entry itself—does the work a portfolio is supposed to do and doesn’t. A chronological structure (weekly logs, monthly summaries) cannot do this work, because it organizes by time rather than by content and fragments single problems across multiple entries.

The reasoning: a portfolio is supposed to show how you handle problems. A weekly log shows what you did this week, which is a different question. Problems don’t resolve on a weekly clock—especially “set in motion” problems, whose feedback is delayed by design. Time-ordered entries split a problem’s evolution across many entries, burying the throughline. Problem-ordered pages keep the evolution of each problem visible as a single trajectory, which is the unit a reader (or future-you) actually wants to see.

A second piece of the conjecture: of the four facets of the Work Question, “what did you do to expose your proposed solutions to criticism” is the one most easily faked by a chronological log that narrates activity. Problem-ordered pages with criticism as load-bearing structure—visible linked criticism nodes, not silent absorption into revisions—make the criticism facet hard to fake. That is the specific work portfolios fail to do, and the specific work this structure is meant to do.

What would refute this:

• A chronological structure that demonstrably surfaces problem evolution and exposes criticism as well as a problem-ordered one (would show the time/content distinction doesn’t matter as much as claimed)
• Problem-ordered pages that, in practice, also fail to expose criticism—revealing that the structure isn’t what was doing the work; something else was
• The structure producing pages that nobody (including future-you) finds usable, which would suggest the chosen unit doesn’t match how you actually navigate your own work.

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Problem Index

A single page listing all problems by status—open, solved, dissolved, abandoned, absorbed, possibly split—is the entry point that makes the problem-ordered documentation actually answerable to the Work Question. Without it, the problem notes exist as a graph but aren’t navigable as a body of work. With it, a reader (or future-you) lands somewhere that shows the shape of the work at a glance: what’s live, what closed and how, what got reframed.

The reasoning: problem-ordered pages solve the unit-of-organization problem, but they don’t solve the navigation problem. A graph of linked notes can be entered from any node, which is good for following a thread but bad for seeing the whole. The index is the view onto the graph that surfaces the patterns the individual notes can’t: the proportion of problems that get solved versus dissolved versus abandoned, the reframings, the convergences. It’s also where you’d notice unnoticed duplication—different framings of the same problem—which is a real piece of work the index does that nothing else does.

The index also serves the polemical function the Work Question carries. Someone landing on a “Problem Index” page sees immediately that this is documentation organized around problems, not output. That framing happens before they read any individual note. A site without the index leaves the reframe implicit; the index makes it the first thing visible.

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Problem note

Title: A findable, unambiguous handle. Doesn’t need to be short. Names the problem; doesn’t carry a claim.

Role tag: r/problem

Body opens with the full problem statement. Phrased as a question, including the such-that—what would count as having solved it. Example: “How should I document answers to the Work Question in a way that does the work a portfolio doesn’t?”

Status line near the top. One of: open, solved, dissolved, abandoned, absorbed. Dated. When closed, includes a forward link where relevant (absorbed → which problem it now lives under; superseded framing → which note replaced it).

Example:
Status: open, 2026-03
Status: solved, 2026-05, surviving conjecture: [link]

Links out to:

• Conjectures addressing the problem
• Related problems (meta, sub, sibling—labeled by relation, not hierarchy)
• The convergence note if this problem turned out to overlap with others

Workflow tags as needed: wf/revise, wf/link, wf/split, etc. These operate independently of the role and status.

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Conjecture note

Title: Can carry the claim itself, since the claim is the content. Example: “Status lines should be prose, not tags.”

Role tag: r/conjecture

Body states the conjecture and its reasoning. What the claim is, what it’s responding to, why it might be right. Hard-to-vary where possible. Includes what would count as refuting it where you can articulate that.

Status line near the top. One of: surviving, refuted, revised, abandoned. Dated. Includes forward links: refuted → criticism note(s); revised → successor conjecture note.

Links out to:

• The problem(s) it addresses (required—a conjecture not attached to a problem shouldn’t be a conjecture note)
• Criticisms applied to it (as they’re written)
• Predecessor or successor conjectures if part of a revision lineage

Workflow tags as needed, including wf/uncriticized if no criticism node has been linked yet. Clears when criticism is applied or you mark the conjecture as untested in the body.

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Criticism note

Title: Can carry the criticism itself, since the content is the critical claim. Example: “Status as a tag fails the next-action principle.”

Role tag: r/criticism

Body states what’s wrong with the targeted conjecture and why. What the criticism is, what assumption or move in the conjecture it attacks, and why that attack lands. Specific enough that a reader can see whether the criticism actually applies or misses. A criticism that could be deflected by the conjecture’s defender adding a convenient exception is a weak criticism—worth being explicit about what the criticism would force the conjecture to give up.

Status line near the top, optional. Add only if you find yourself returning to criticisms and updating their standing. If you do, vocabulary is: standing, answered (link to revised conjecture or counter-criticism), withdrawn. Default is no status line—criticisms tend to be write-once, and adding the line preemptively is overhead that hasn’t earned its place. Let the convention emerge from actual use.

Links out to:

• The conjecture it targets (required—a criticism not attached to a conjecture shouldn’t be a criticism note)
• Related criticisms if this one is part of a cluster attacking the same conjecture from different angles
• The successor conjecture if the criticism produced a revision

Workflow tags as needed. Standard wf/ tags apply. No criticism-specific workflow tag is needed at the start; if you find one missing through use, add it then.

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One thing worth flagging across all three role notes: the required links are what make the role real. A problem with no conjectures attached is just an unanswered question. A conjecture with no problem attached is a floating assertion. A criticism with no conjecture attached is a complaint. The roles only do their work when the structure connecting them is intact. If you find yourself wanting to write a note that won’t attach, that’s usually a signal the note isn’t ready yet—either you haven’t articulated the problem it addresses, or it isn’t actually playing the role you thought.