Tag: Language Insufficiency Hypothesis

The Truth About Truth, Revisited

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6–9 minutes

“Truths are illusions which we have forgotten are illusions.” — Nietzsche

Declaring the Problem

Most people say truth as if it were oxygen – obvious, necessary, self-evident. I don’t buy it.

Nietzsche was blunt: truths are illusions. My quarrel is only with how often we forget that they’re illusions.

Most people say truth as if it were oxygen – obvious, necessary, self-evident. I don’t buy it.

My own stance is unapologetically non-cognitivist. I don’t believe in objective Truth with a capital T. At best, I see truth as archetypal – a symbol humans invoke when they need to rally, persuade, or stabilise. I am, if you want labels, an emotivist and a prescriptivist: I’m drawn to problems because they move me, and I argue about them because I want others to share my orientation. Truth, in this sense, is not discovered; it is performed.

The Illusion of Asymptotic Progress

The standard story is comforting: over time, science marches closer and closer to the truth. Each new experiment, each new refinement, nudges us toward Reality, like a curve bending ever nearer to its asymptote.

Chart 1: The bedtime story of science: always closer, never arriving.

This picture flatters us, but it’s built on sand.

Problem One: We have no idea how close or far we are from “Reality” on the Y-axis. Are we brushing against it, or still a light-year away? There’s no ruler that lets us measure our distance.

Problem Two: We can’t even guarantee that our revisions move us toward rather than away from it. Think of Newton and Einstein. For centuries, Newton’s physics was treated as a triumph of correspondence—until relativity reframed it as local, limited, provisional. What once looked like a step forward can later be revealed as a cul-de-sac. Our curve may bend back on itself.

Use Case: Newton, Einstein, and Gravity
Take gravity. For centuries, Newton’s laws were treated as if they had brought us into near-contact with Reality™—so precise, so predictive, they had to be true. Then Einstein arrives, reframes gravity not as a force but as the curvature of space-time, and suddenly Newton’s truths are parochial, a local approximation. We applauded this as progress, as if our asymptote had drawn tighter to Reality. But even Einstein leaves us with a black box: we don’t actually know what gravity is, only how to calculate its effects. Tomorrow another paradigm may displace relativity, and once again we’ll dutifully rebrand it as “closer to truth.” Progress or rhetorical re-baptism? The graph doesn’t tell us.

Chart 2: The comforting myth of correspondence: scientific inquiry creeping ever closer to Reality™, though we can’t measure the distance—or even be sure the curve bends in the right direction.

Thomas Kuhn was blunt about this: what we call “progress” is less about convergence and more about paradigm shifts, a wholesale change in the rules of the game. The Earth does not move smoothly closer to Truth; it lurches from one orthodoxy to another, each claiming victory. Progress, in practice, is rhetorical re-baptism.

Most defenders of the asymptotic story assume that even if progress is slow, it’s always incremental, always edging us closer. But history suggests otherwise. Paradigm shifts don’t just move the line higher; they redraw the entire curve. What once looked like the final step toward truth may later be recast as an error, a cul-de-sac, or even a regression. Newton gave way to Einstein; Einstein may yet give way to something that renders relativity quaint. From inside the present, every orthodoxy feels like progress. From outside, it looks more like a lurch, a stumble, and a reset.

Chart 3: The paradigm-gap view: what feels like progress may later look like regression. History suggests lurches, not lines, what we call progress today is tomorrow’s detour..

If paradigm shifts can redraw the entire map of what counts as truth, then it makes sense to ask what exactly we mean when we invoke the word at all. Is truth a mirror of reality? A matter of internal coherence? Whatever works? Or just a linguistic convenience? Philosophy has produced a whole menu of truth theories, each with its own promises and pitfalls—and each vulnerable to the same problems of rhetoric, context, and shifting meanings.

The Many Flavours of Truth

Philosophers never tire of bottling “truth” in new vintages. The catalogue runs long: correspondence, coherence, pragmatic, deflationary, redundancy. Each is presented as the final refinement, the one true formulation of Truth, though each amounts to little more than a rhetorical strategy.

  • Correspondence theory: Truth is what matches reality.
    Problem: we can never measure distance from “Reality™” itself, only from our models.
  • Coherence theory: Truth is what fits consistently within a web of beliefs.
    Problem: many mutually incompatible webs can be internally consistent.
  • Pragmatic theory: Truth is what works.
    Problem: “works” for whom, under what ends? Functionality is always perspectival.
  • Deflationary / Minimalist: Saying “it’s true that…” adds nothing beyond the statement itself.
    Problem: Useful for logic, empty for lived disputes.
  • Redundancy / Performative: “It is true that…” adds rhetorical force, not new content.
    Problem: truth reduced to linguistic habit.

And the common fallback: facts vs. truths. We imagine facts as hard little pebbles anyone can pick up. Hastings was in 1066; water boils at 100°C at sea level. But these “facts” are just truths that have been successfully frozen and institutionalised. No less rhetorical, only more stable.

So truth isn’t one thing – it’s a menu. And notice: all these flavours share the same problem. They only work within language-games, frameworks, or communities of agreement. None of them delivers unmediated access to Reality™.

Truth turns out not to be a flavour but an ice cream parlour – lots of cones, no exit.

Multiplicity of Models

Even if correspondence weren’t troubled, it collapses under the weight of underdetermination. Quine and Duhem pointed out that any body of evidence can support multiple competing theories.

Chart 4: orthodox vs. heterodox curves, each hugging “reality” differently

Hilary Putnam pushed it further with his model-theoretic argument: infinitely many models could map onto the same set of truths. Which one is “real”? There is no privileged mapping.

Conclusion: correspondence is undercut before it begins. Truth isn’t a straight line toward Reality; it’s a sprawl of models, each rhetorically entrenched.

Truth as Rhetoric and Power

This is where Orwell was right: “War is Peace, Freedom is Slavery, Ignorance is Strength.”

Image: INGSOC logo

Truth, in practice, is what rhetoric persuades.

Michel Foucault stripped off the mask: truth is not about correspondence but about power/knowledge. What counts as truth is whatever the prevailing regime of discourse allows.

We’ve lived it:

  • “The economy is strong”, while people can’t afford rent.
  • “AI will save us”, while it mainly writes clickbait.
  • “The science is settled” until the next paper unsettles it.

These aren’t neutral observations; they’re rhetorical victories.

Truth as Community Practice

Chart 5: Margin of error bands

Even when rhetoric convinces, it convinces in-groups. One group converges on a shared perception, another on its opposite. Flat Earth and Round Earth are both communities of “truth.” Each has error margins, each has believers, each perceives itself as edging toward reality.

Wittgenstein reminds us: truth is a language game. Rorty sharpens it: truth is what our peers let us get away with saying.

So truth is plural, situated, and always contested.

Evolutionary and Cognitive Scaffolding

Step back, and truth looks even less eternal and more provisional.

We spread claims because they move us (emotivism) and because we urge others to join (prescriptivism). Nietzsche was savage about it: truth is just a herd virtue, a survival trick.

Cognitive science agrees, if in a different language: perception is predictive guesswork, riddled with biases, illusions, and shortcuts. Our minds don’t mirror reality; they generate useful fictions.

Diagram: Perception as a lossy interface: Reality™ filtered through senses, cognition, language, and finally rhetoric – signal loss at every stage.

Archetypal Truth (Positive Proposal)

So where does that leave us? Not with despair, but with clarity.

Truth is best understood as archetypal – a construct humans rally around. It isn’t discovered; it is invoked. Its force comes not from correspondence but from resonance.

Here, my own Language Insufficiency Hypothesis bites hardest: all our truth-talk is approximation. Every statement is lossy compression, every claim filtered through insufficient words. We can get close enough for consensus, but never close enough for Reality.

Truth is rhetorical, communal, functional. Not absolute.

The Four Pillars (Manifesto Form)

  1. Archetypal – truth is a symbolic placeholder, not objective reality.
  2. Asymptotic – we gesture toward reality but never arrive.
  3. Rhetorical – what counts as truth is what persuades.
  4. Linguistically Insufficient – language guarantees slippage and error.

Closing

Nietzsche warned, Rorty echoed: stop fetishising Truth. Start interrogating the stories we tell in its name.

Every “truth” we now applaud may be tomorrow’s embarrassment. The only honest stance is vigilance – not over whether we’ve captured Reality™, but over who gets to decide what is called true, and why.

Truth has never been a mirror. It’s a mask. The only question worth asking is: who’s wearing it?

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The Myth of Causa Sui Creativity

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(or: Why Neither Humans nor AI Create from Nothing)

In the endless squabble over whether AI can be “creative” or “intelligent,” we always end up back at the same semantic swamp. At the risk of poking the bear, I have formulated a response. Creativity is either whatever humans do, or whatever humans do that AI can’t. Intelligence is either the general ability to solve problems or a mysterious inner light that glows only in Homo sapiens. The definitions shift like sand under the feet of the argument.

Audio: NotebookLM podcast on this topic

Strip away the romance, and the truth is far less flattering: neither humans nor AI conjure from the void. Creativity is recombination, the reconfiguration of existing material into something unfamiliar. Intelligence is the ability to navigate problems using whatever tools and heuristics one has to hand.

I cannot be causa sui, and neither can you.

The Causa Sui conceit, the idea that one can be the cause of oneself, is incoherent in art, thought, or physics. Conservation of energy applies as much to ideas as to atoms.

  • Humans consume inputs: books, conversations, music, arguments, TikTok videos.
  • We metabolise them through cognitive habits, biases, and linguistic forms.
  • We output something rearranged, reframed, sometimes stripped to abstraction.

The AI process is identical in structure, if not in substrate: ingest vast data, run it through a model, output recombination. The difference is that AI doesn’t pretend otherwise.

When a human produces something impressive, we call it creative without inspecting the provenance of the ideas. When an AI produces something impressive, we immediately trace the lineage of its inputs, as if the human mind weren’t doing the same. This is not epistemic rigour, it’s tribal boundary enforcement.

The real objection to AI is not that it fails the test of creativity or intelligence; it’s that it passes the functional test without being part of the club. Our stories about human exceptionalism require a clear line between “us” and “it,” even if we have to draw that line through semantic fog.

My Language Insufficiency Hypothesis began with the recognition that language cannot fully capture the reality it describes. Here, the insufficiency is deliberate; the words “creativity” and “intelligence” are kept vague so they can always be shifted away from anything AI achieves.

I cannot be causa sui, and neither can you. The only difference is that I’m willing to admit it.

The Rhetoric of Realism: When Language Pretends to Know

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Let us begin with the heresy: Truth is a rhetorical artefact. Not a revelation. Not a metaphysical essence glimmering behind the veil. Just language — persuasive, repeatable, institutionally ratified language. In other words: branding.

Audio: NotebookLM podcast on this topic.

This is not merely a postmodern tantrum thrown at the altar of Enlightenment rationalism. It is a sober, if impolite, reminder that nearly everything we call “knowledge” is stitched together with narrative glue and semantic spit. Psychology. Neuroscience. Ethics. Economics. Each presents itself as a science — or worse, a moral imperative — but their foundations are built atop a linguistic faultline. They are, at best, elegant approximations; at worst, dogma in drag.

Let’s take psychology. Here is a field that diagnoses your soul via consensus. A committee of credentialed clerics sits down and declares a cluster of behaviours to be a disorder, assigns it a code, and hands you a script. It is then canonised in the DSM, the Diagnostic Scripture Manual. Doubt its legitimacy and you are either naïve or ill — which is to say, you’ve just confirmed the diagnosis. It’s a theological trap dressed in the language of care.

Or neuroscience — the church of the glowing blob. An fMRI shows a region “lighting up” and we are meant to believe we’ve located the seat of love, the anchor of morality, or the birthplace of free will. Never mind that we’re interpreting blood-oxygen fluctuations in composite images smoothed by statistical witchcraft. It looks scientific, therefore it must be real. The map is not the territory, but in neuroscience, it’s often a mood board.

And then there is language itself, the medium through which all these illusions are transmitted. It is the stage, the scenery, and the unreliable narrator. My Language Insufficiency Hypothesis proposes that language is not simply a flawed tool — it is fundamentally unfit for the task it pretends to perform. It was forged in the furnace of survival, not truth. We are asking a fork to play the violin.

This insufficiency is not an error to be corrected by better definitions or clever metaphors. It is the architecture of the system. To speak is to abstract. To abstract is to exclude. To exclude is to falsify. Every time we speak of a thing, we lose the thing itself. Language functions best not as a window to the real but as a veil — translucent, patterned, and perpetually in the way.

So what, then, are our Truths™? They are narratives that have won. Stories that survived the epistemic hunger games. They are rendered authoritative not by accuracy, but by resonance — psychological, cultural, institutional. A “truth” is what is widely accepted, not because it is right, but because it is rhetorically unassailable — for now.

This is the dirty secret of epistemology: coherence masquerades as correspondence. If enough concepts link arms convincingly, we grant them status. Not because they touch reality, but because they echo each other convincingly in our linguistic theatre.

Libet’s experiment, Foucault’s genealogies, McGilchrist’s hemispheric metaphors — each peels back the curtain in its own way. Libet shows that agency might be a post-hoc illusion. Foucault reveals that disciplines don’t describe the subject; they produce it. McGilchrist laments that the Emissary now rules the Master, and the world is flatter for it.

But all of them — and all of us — are trapped in the same game: the tyranny of the signifier. We speak not to uncover truth, but to make truth-sounding noises. And the tragedy is, we often convince ourselves.

So no, we cannot escape the prison of language. But we can acknowledge its bars. And maybe, just maybe, we can rattle them loudly enough that others hear the clank.

Until then, we continue — philosophers, scientists, diagnosticians, rhetoricians — playing epistemology like a parlour game with rigged dice, congratulating each other on how well the rules make sense.

And why wouldn’t they? We wrote them.

The Scourge: They’re Really Fighting Is Ambiguity

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A Sequel to “The Disorder of Saying No” and a Companion to “When ‘Advanced’ Means Genocide”

In my previous post, The Disorder of Saying No, I explored the way resistance to authority is pathologised, particularly when that authority is cloaked in benevolence and armed with diagnostic manuals. When one refuses — gently, thoughtfully, or with a sharp polemic — one is no longer principled. One is “difficult.” Or in my case, oppositional.

Audio: NotebookLM podcast on this topic.

So when I had the gall to call out Bill Maher for his recent linguistic stunt — declaring that a woman is simply “a person who menstruates” — I thought I was doing the rational thing: pointing out a classic bit of reductionist nonsense masquerading as clarity. Maher, after all, was not doing biology. He was playing lexicographer-in-chief, defining a term with centuries of philosophical, sociological, and political baggage as though it were a checkbox on a medical form.

I said as much: that he was abusing his platform, presenting himself as the sole arbiter of the English language, and that his little performance was less about clarity and more about controlling the terms of discourse.

My friend, a post-menopausal woman herself, responded not by engaging the argument, but by insinuating — as others have — that I was simply being contrary. Oppositional. Difficult. Again. (She was clearly moved by When “Advanced” Means Genocide, but may have missed the point.)

So let’s unpack this — not to win the debate, but to show what the debate actually is.

This Isn’t About Biology — It’s About Boundary Maintenance

Maher’s statement wasn’t intended to clarify. It was intended to exclude. It wasn’t some linguistic slip; it was a rhetorical scalpel — one used not to analyse, but to amputate.

And the applause from some cisgender women — particularly those who’ve “graduated” from menstruation — reveals the heart of the matter: it’s not about reproductive biology. It’s about controlling who gets to claim the term woman.

it only works if you pretend the world is simpler than it is.

Let’s steelman the argument, just for the sport of it:

Menstruation is a symbolic threshold. Even if one no longer menstruates, having done so places you irrevocably within the category of woman. It’s not about exclusion; it’s about grounding identity in material experience.

Fine. But now let’s ask:

  • What about women who’ve never menstruated?
  • What about intersex people?
  • What about trans women?
  • What about cultures with radically different markers of womanhood?

You see, it only works if you pretend the world is simpler than it is.

The Language Insufficiency Hypothesis: Applied

This is precisely where the Language Insufficiency Hypothesis earns its keep.

The word woman is not a locked vault. It is a floating signifier, to borrow from Barthes — a term whose meaning is perpetually re-negotiated in use. There is no singular essence to the word. It is not rooted in biology, nor in social role, nor in performance. It is a hybrid, historically contingent construct — and the moment you try to fix its meaning, it slips sideways like a greased Wittgensteinian beetle.

“Meaning is use,” says Wittgenstein, and this is what frightens people.

If woman is defined by use and not by rule, then anyone might claim it. And suddenly, the club is no longer exclusive.

That’s the threat Maher and his defenders are really reacting to. Not trans women. Not intersex people. Not language activists or queer theorists.

The threat is ambiguity.

What They Want: A World That Can Be Named

The push for rigid definitions — for menstruation as membership — is a plea for a world that can be named and known. A world where words are secure, stable, and final. Where meaning doesn’t leak.

But language doesn’t offer that comfort.

It never did.

And when that linguistic instability gets too close to something personal, like gender identity, or the foundation of one’s own sense of self, the defensive response is to fortify the language, as though building walls around a collapsing church.

Maher’s defenders aren’t making scientific arguments. They’re waging semantic warfare. If they can hold the definition, they can win the cultural narrative. They can hold the gates to Womanhood and keep the undesirables out.

That’s the fantasy.

But language doesn’t play along.

Conclusion: Words Will Not Save You — but They Might Soothe the Dead

In the end, Maher’s definition is not merely incorrect. It is insufficient. It cannot accommodate the complexity of lived experience and cannot sustain the illusion of clarity for long.

They are defending nostalgia.

And those who cling to it — friend or stranger, progressive, or conservative — are not defending biology. They are defending nostalgia. Specifically, a pathological nostalgia for a world that no longer exists, and arguably never did: a world where gender roles were static, language was absolute, and womanhood was neatly circumscribed by bodily functions and suburban etiquette.

Ozzy and Harriet loom large here — not as individuals but as archetypes. Icons of a mid-century dream in which everyone knew their place, and deviation was something to be corrected, not celebrated. My friend, of that generation, clings to this fantasy not out of malice but out of a desperate yearning for order. The idea that woman could mean many things, and mean them differently across contexts, is not liberating to her — it’s destabilising.

But that world is gone. And no amount of menstruation-based gatekeeping will restore it.

The Real Scourge Is Ambiguity

Maher’s tantrum wasn’t about truth. It was about fear — fear of linguistic drift, of gender flux, of a world in which meaning no longer obeys. The desire to fix the definition of “woman” is not a biological impulse. It’s a theological one.

And theology, like nostalgia, often makes terrible policy.

And theology, like nostalgia, often makes terrible policy.

This is why your Language Insufficiency Hypothesis matters. Because it reminds us that language does not stabilise reality — it masks its instability. The attempt to define “woman” once and for all is not just futile — it’s an act of violence against difference, a linguistic colonisation of lived experience.

So Let Them Rest

Ozzy and Harriet are dead. Let them rest.
Let their picket fence moulder. Let their signage decay.

The world has moved on. The language is shifting beneath your feet. And no amount of retroactive gatekeeping can halt that tremor.

The club is burning. And the only thing left to save is honesty.

When “Advanced” Means Genocide: A Case Study in Linguistic Implosion

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This post draws on themes from my upcoming book, A Language Insufficiency Hypothesis. The transcript below is taken from a publicly available exchange, which you can view here. Consider it Exhibit A in language’s ongoing failure to bear the weight of meaning.

Transcript:

KK: Konstantin Kisin
DFW: Deborah Frances-White

KK: I’m saying we were technologically more advanced.
DFW: So you’re saying we’re superior to Australian Aboriginals?
KK: That’s quite the opposite of what I’m saying. I’m not saying we were superior, I’m saying we were technologically more advanced.
DFW: So, how is that the opposite?
KK: Superior implies a moral quality. I’m not making any moral implication. You seem to be, but what I’m saying is…
DFW: I think most people would hear it that way.
KK: No.
DFW: Again, you’re a very intelligent man. How would most people hear that?
KK: Most people would hear what I’m saying for what I’m saying, which is…
DFW: I don’t think they would.
KK: You seem to get quite heated about this, which is completely unnecessary.
DFW: Um…
KK: You think it’s necessary?
DFW: I’m a bit stunned by what you’re implying.
KK: No, you’re acting in a kind of passive aggressive way which indicates that you’re not happy…
DFW: I genuinely… I’m being 100% authentic. My visceral reaction to a white man sitting and saying to me, “And why were we able to commit genocide on them?” and then just pausing—
KK: Yes.
DFW: …is very visceral to me.
KK: Well, let’s go back. First of all, it’s interesting that you brought up my skin colour because I thought that was the exact opposite of the point you’re trying to make in the book.

Audio: NotebookLM podcast on this topic.

The Language Insufficiency Hypothesis begins with this premise: language is not merely flawed, it is structurally inadequate for mediating complex, layered realities

Dissection: Language as Battlefield

The Language Insufficiency Hypothesis begins with this premise: language is not merely flawed, it is structurally inadequate for mediating complex, layered realities – especially those laced with power, morality, and history. This transcript is not a debate. It is a linguistic trench war in which every utterance is laced with shrapnel, and each side thinks they’re defending reason.

Let’s pull a few of the shell casings from the mud.

1. Semantic Contamination: “Technologically more advanced”

KK attempts to offer a dry, neutral descriptor. DFW hears supremacist teleology. Why? Because “advanced” is culturally radioactive. It doesn’t merely denote a technical state—it connotes a ladder, with someone inevitably on the bottom rung.

When language carries historical residue, neutrality is a delusion. Words don’t just mean. They echo.

KK is making a semantic distinction. DFW hears a moral claim. Both are right. And both are talking past one another, because language is attempting to cleave affect from description, and it simply can’t.

2. Disambiguation Does Not Save You

KK’s insistence—“I’m not saying we’re superior”—is a textbook example of denotative desperation. He believes clarification will rescue intent. But as any linguist (or postcolonial theorist) will tell you: intent does not sterilise implication.

Language cannot be laundered by explanation. Once spoken, words belong to context, not intention.

KK thinks he’s holding a scalpel. DFW hears a cudgel. And here we are.

3. Phatic Collapse: “Most people would hear…”

This is where the wheels come off. KK argues from semantic specificity. DFW argues from sociolinguistic reception. It’s Saussure versus the TikTok algorithm. Neither will win.

Communication disintegrates not because anyone is lying, but because they are playing incompatible games with the same tokens.

4. Identity as Index: When the Speaker Becomes the Speech

DFW’s invocation of “a white man” is not a derailment—it’s the inevitable endpoint of a system where words no longer float free but are yoked to their utterer. This is the moment the failure of language becomes a failure of interlocution. Argument collapses into indexical entrapment.

At this point, you’re no longer debating ideas. You’re defending your right to use certain words at all.

Which brings us to the final breakdown.

5. Moral Authenticity vs Logical Precision: Unbridgeable Grammars

KK: I am making a logical distinction.
DFW: I am having a visceral reaction.

The failure isn’t moral. It isn’t historical. It’s grammatical. One is operating in a truth-function logic game. The other is reacting within a trauma-informed, socially indexed register. These are grammars that do not overlap.

Conclusion: Language Did What It Always Does—It Failed Us

If this brief and brutal dialogue proves anything, it’s this: you cannot extract meaning cleanly from words when the words themselves are sponges for history, hierarchy, and harm. The moment we ask language to do too much—to carry precision, affect, ethics, and identity—it folds in on itself.

And that, dear reader, is precisely the argument of A Language Insufficiency Hypothesis: that meaning does not reside in words, and never has. It lives in the gaps, the silences, the misfires. That’s where the truth—whatever’s left of it—might be hiding.

Follow the wreckage. That’s where the signal lives.

What’s in a word?

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The winding down seems to take longer than the winding up. I’m trying to finish my Language Insufficiency Hypothesis manuscript, and I’m hung up on word choices. I’ve shared this chart before. My conundrum is the name of the Y-axis.

I’ve been calling it Language Effectiveness, but I’ve been losing sleep. Rather than effectiveness, should it rather be efficiency or efficacy? These are all near-synonyms, but they have nuanced differences, so I turned to ChatGPT.

The terms efficiency, efficacy, and effectiveness are often used interchangeably but have distinct meanings, especially in professional, scientific, and practical contexts. Here is a breakdown of their differences:

Efficiency

  • Definition: The ability to achieve a desired result with minimal waste of resources (time, money, energy, materials).
  • Focus: Resource utilisation.
  • Key Question: How well are resources being used to achieve the goal?
  • Example: A fuel-efficient car travels further on less petrol, minimising waste.

In short, efficiency measures how economically resources are used to accomplish a task.

Efficacy

  • Definition: The capacity to produce the desired result under ideal conditions.
  • Focus: Capability and potential.
  • Key Question: Does it work as intended when tested in controlled settings?
  • Example: A new medication shows high efficacy in clinical trials, curing 95% of patients in controlled environments.

In short, efficacy refers to the theoretical or tested success of an intervention under optimal conditions.

Effectiveness

  • Definition: The degree to which something produces the desired result in real-world conditions.
  • Focus: Practical success.
  • Key Question: Does it work in everyday use or the real world?
  • Example: A medication might be effective in treating 80% of patients in a hospital, accounting for factors like adherence and side effects.

In short, effectiveness measures how well something performs when applied practically, outside idealised settings.

Comparison

  • Efficiency concerns how something is done (minimal resource use).
  • Efficacy assesses if it can be done successfully (ideal conditions).
  • Effectiveness evaluates how well it works in real situations (practical application).

Think of a machine:

  • Its efficacy is its ability to perform a task perfectly when running in optimal conditions.
  • Its efficiency is how little energy or resources it uses while running.
  • Its effectiveness is how well it performs the task in a chaotic, real-world factory.

Interestingly, this was a helpful inquiry. Notice that the chart has three curves. My first choice seems to capture the entire framework, but the other two better capture the curves themselves.

The lower curve reflects efficiency — though effectiveness given the real-world circumstances. The middle curve represents efficacy. This is because conditions are ideal if the speaker and listener are on the same page and the speaker is a skilled communicator. The chart illustrates that even in this perfect situation, language communication cannot reach one hundred per cent for abstract concepts. The top curve illustrates the cognitive phenomenon that makes some people believe they communicate at a higher level than they do. This is akin to the illusory superiority cognitive bias, where people tend to believe they are above average.

I’m leaning towards naming the bottom curve language effectiveness and the middle curve the language efficacy horizon. Please stand by.

NB: If the cover image makes no sense, it’s because I entered ‘efficiency effectiveness efficacy’ into Midjourney, and this was one of the images it spat out.

Slice of Life

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This is a timeline of foundational ideas on which I’ve built my Language Insufficiency Hypothesis. I spent a day compositing this timeline in Adobe Illustrator. I hadn’t used Illustrator in decades. It’s got a lot of options, so I’ve been leveraging ChatGPT as a help guide. It seems the UI/UX could be improved, but I’m sure I’ll get used to it. I’ve got another couple dozen to go. I’m hoping a learning/efficiency curve kicks in.

WordPress wouldn’t accept or render my first few file types, even though they are listed as acceptable – SVG, PNG, TIF, WEBP – so I opted for gold, old-fashioned BMP, so it’s pretty hefty for inline rendering on a blog. I want to share, and so here is a late draft.

I’m no graphic artist, so it’s relatively primitive. I’ve been experimenting with colours, but the book is black and white, so I’ll probably just keep it the same.

There are a lot of data points to fit on this timeline, and I’m limited to a 6″ x 9″ form factor. Except for the first 3 entries, the items are to-scale by year. I have more information, but I can’t really fit it in. In fact, this started as two timelines. There was so much cross-over that I merged them into one, forcing me to remove some meta information and lose some detail. Design is hard.

It looks fine on the page. I don’t want to reduce the font sizes to cram in more info. I’ll probably put the more descriptive copy in an appendix, if not on a facing page.

Language Insufficiency Hypothesis, Chapter One

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Well, a word cloud anyway.

I’m busy focusing on the manuscript for Chapter One of my Language Insufficiency Hypothesis. Scanning the copy—over 4,000 words—I noticed some repetition. My eye caught ‘fundamental’, which I use often in speech as well. I decided to try a word cloud to illustrate common words.

As this is a hypothesis on the limitations of language, the fact that these are prominent is no surprise, but 34 instances of ‘fundamental’ seem fundamentally off—see what I did there? And this is only the first chapter of eight.

I’ll review the content to see if I can remove some instances—perhaps entire sentences—or choose a synonym to see where it leads.

The manuscript is in its fourth full revision, and it feels closer to completion. I aim for an early 2025 release date, but time will tell.

Ass usual, I employ AI (Claude, in this case) to do some heavy lifting.

Having analysed the chapter, I’ve identified several places where “fundamental” could be substituted or removed to enhance the linguistic diversity and rhetorical effectiveness of the text. Here are some examples:

  1. “These limitations, we argue, manifest not merely as practical challenges but as fundamental features of how meaning systems operate…”
    • Alternative: “These limitations, we argue, manifest not merely as practical challenges but as intrinsic features of how meaning systems operate…”
  2. “This theoretical perspective fundamentally reframes several longstanding problems in linguistics and philosophy.”
    • Alternative: “This theoretical perspective radically reframes several longstanding problems in linguistics and philosophy.”
  3. “These patterns suggest inherent rather than circumstantial constraints – limitations that emerge from language’s structure itself rather than from inadequate definition or imprecise usage.”
    • Here, “fundamental” could be omitted entirely without losing the core meaning of the sentence.
  4. “Understanding these constraints as systematic features rather than implementation failures provides a crucial theoretical foundation for the Language Insufficiency Hypothesis.”
    • Alternative: “Understanding these constraints as systematic features rather than implementation failures provides a vital theoretical foundation for the Language Insufficiency Hypothesis.”
  5. “These cognitive constraints manifest not as mere practical impediments but as necessary features of how meaning is processed and transmitted through human neural networks.”
    • Here, “fundamental” could be replaced with “essential” or simply omitted.

Notice that 3 of the 5 recommendations didn’t include the offending word. Rather, they already employed synonyms. This gives us insights into how LLMs translate language with fuzzy logic. Perhaps that’s an article for another day.

Where you from, Homie?

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This skit is a comical take on in-group versus out-group language insufficiency. It’s a couple years old, so you may have seen it before.

This video illustrates how easy it is for miscommunication to occur in mixed-group settings.
Trigger Warning: The humour is a bit weak and the focus is on stereotypes. If this isn’t quite up your street, just move on. Nothing to see here.

Beware the Bots: A Cautionary Tale on the Limits of Generative AI

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Generative AI (Gen AI) might seem like a technological marvel, a digital genie conjuring ideas, images, and even conversations on demand. It’s a brilliant tool, no question; I use it daily for images, videos, and writing, and overall, I’d call it a net benefit. But let’s not overlook the cracks in the gilded tech veneer. Gen AI comes with its fair share of downsides—some of which are as gaping as the Mariana Trench.

First, a quick word on preferences. Depending on the task at hand, I tend to use OpenAI’s ChatGPT, Anthropic’s Claude, and Perplexity.ai, with a particular focus on Google’s NotebookLM. For this piece, I’ll use NotebookLM as my example, but the broader discussion holds for all Gen AI tools.

Now, as someone who’s knee-deep in the intricacies of language, I’ve been drafting a piece supporting my Language Insufficiency Hypothesis. My hypothesis is simple enough: language, for all its wonders, is woefully insufficient when it comes to conveying the full spectrum of human experience, especially as concepts become abstract. Gen AI has become an informal editor and critic in my drafting process. I feed in bits and pieces, throw work-in-progress into the digital grinder, and sift through the feedback. Often, it’s insightful; occasionally, it’s a mess. And herein lies the rub: with Gen AI, one has to play babysitter, comparing outputs and sending responses back and forth among the tools to spot and correct errors. Like cross-examining witnesses, if you will.

But NotebookLM is different from the others. While it’s designed for summarisation, it goes beyond by offering podcasts—yes, podcasts—where it generates dialogue between two AI voices. You have some control over the direction of the conversation, but ultimately, the way it handles and interprets your input depends on internal mechanics you don’t see or control.

So, I put NotebookLM to the test with a draft of my paper on the Language Effectiveness-Complexity Gradient. The model I’m developing posits that as terminology becomes more complex, it also becomes less effective. Some concepts, the so-called “ineffables,” are essentially untranslatable, or at best, communicatively inefficient. Think of describing the precise shade of blue you can see but can’t quite capture in words—or, to borrow from Thomas Nagel, explaining “what it’s like to be a bat.” NotebookLM managed to grasp my model with impressive accuracy—up to a point. It scored between 80 to 100 percent on interpretations, but when it veered off course, it did so spectacularly.

For instance, in one podcast rendition, the AI’s male voice attempted to give an example of an “immediate,” a term I use to refer to raw, preverbal sensations like hunger or pain. Instead, it plucked an example from the ineffable end of the gradient, discussing the experience of qualia. The slip was obvious to me, but imagine this wasn’t my own work. Imagine instead a student relying on AI to summarise a complex text for a paper or exam. The error might go unnoticed, resulting in a flawed interpretation.

The risks don’t end there. Gen AI’s penchant for generating “creative” content is notorious among coders. Ask ChatGPT to whip up some code, and it’ll eagerly oblige—sometimes with disastrous results. I’ve used it for macros and simple snippets, and for the most part, it delivers, but I’m no coder. For professionals, it can and has produced buggy or invalid code, leading to all sorts of confusion and frustration.

Ultimately, these tools demand vigilance. If you’re asking Gen AI to help with homework, you might find it’s as reliable as a well-meaning but utterly clueless parent who’s keen to help but hasn’t cracked a textbook in years. And as we’ve all learned by now, well-meaning intentions rarely translate to accurate outcomes.

The takeaway? Use Gen AI as an aid, not a crutch. It’s a handy tool, but the moment you let it think for you, you’re on shaky ground. Keep it at arm’s length; like any assistant, it can take you far—just don’t ask it to lead.