PhilSurvey: What is the aim of philosophy?

2–3 minutes

I commenced a series where I discuss the responses to the 2020 PhilPapers survey of almost 1,800 professional philosophers. This continues that conversation with questions 2 through 4 – in reverse order, not that it matters. Each is under 5 minutes; some are under 3.

For the main choices, you are given 4 options regarding the proposal:

  • Accept
  • Lean towards
  • Reject
  • Lean against

Besides the available choices, accepted answers for any of the questions were items, such as:

  • Combinations (specify which.)
    For the combos, you might Accept A and Reject B, so you can capture that here.
  • Alternate view (not entirely useful unless the view has already been catalogued)
  • The question is too unclear to answer
  • There is no fact of the matter (the question is fundamentally bollocks)
  • Agnostic/undecided
  • Other

Q4: The first one asks, ‘What is the aim of philosophy?’ Among the responses were:

  • Truth/Knowledge
  • Understanding
  • Wisdom
  • Happiness
  • Goodness/Justice

Before you watch the video, how might you respond?

Video: What is the aim of philosophy?

Q3: What’s your position on aesthetic value?

  • Objective
  • Subjective
Video: What is aesthetic value?

Q2: What’s your position on abstract objects?

  • Platonism (these objects exist “out there” in or beyond the world)
  • Nominalism (the objects are human constructs)
Video: Where do abstract objects reside?

Q1: What’s your position on à priori knowledge?

This video response was an earlier post, so find it there. This is asking if you believe one can have any knowledge apart from experience.

  • Yes
  • No

NB: I’ve recorded ten of these segments already, but they require editing. So I’ll release them as I wrap them up. Not that I’ve completed them, I realise I should have explained what the concepts mean more generally instead of talking around the topics in my preferred response. There are so many philosophy content sites, I feel this general information is already available, or by search, or even via an LLM.

In the other hand, many of these sites – and I visit and enjoy them – support very conservative, orthodox views that, as I say, don’t seem to have progressed much beyond 1840 – Kant and a dash of Hegel, but all founded on Aristotelian ideas, some 2,500 years ago.

Spoiler alert, I think knowledge has advanced and disproved a lot of this. It turns out my brothers in arms don’t necessarily agree. Always the rebel, I suppose.

MEOW GPT FeedbackOn Testing MEOW GPT (And the Delicate Souls It Might Upset)

3–4 minutes

A surprising number of people have been using the MEOW GPT I released into the wild. Naturally, I can’t see how anyone is actually using it, which is probably for the best. If you hand someone a relational ontology and they treat it like a BuzzFeed quiz, that’s on them. Still, I haven’t received any direct feedback, positive or catastrophic, which leaves me wondering whether users understand the results or are simply nodding like priests reciting Latin they don’t believe.

Audio: NotebookLM summary podcast of this topic.

The truth is uncomfortable: if you haven’t grasped the Mediated Encounter Ontology (of the World), the outputs may feel like a philosophical brick to the face. They’re meant to; mediation has consequences. I’m even considering adding a warning label:

Below is a sampling of the concepts I tested while inspecting the system’s behaviour. I’m withholding the outputs, partly to avoid influencing new users and partly to preserve your dignity, such as it is.

  • authenticity
  • anattā (Buddhist)
  • character (in Aristotle’s virtue-ethical sense)
  • consciousness
  • dignity
  • freedom
  • hózhó (Navajo)
  • justice
  • karma
  • love
  • progress
  • ren ( 仁 )
  • table
  • tree
  • truth

I may have tried others, depending on how irritated I was with the world at the time.

(Now that I think of it, I entered my full name and witnessed it nearly have an aneurysm.)

My purpose in trying these is (obviously) to test the GPT. As part of the test, I wanted to test terms I already considered to be weasel words. I also wanted to test common terms (table) and terms outside of Western modalities. I learned something about the engine in each case.

Tables & Trees

One of the first surprises was the humble ‘table’ which, according to the engine, apparently moonlights across half of civilisation’s conceptual landscape. If you input ‘table’, you get everything from dinner tables to data tables to parliamentary procedure. The model does exactly what it should: it presents the full encounter-space and waits for you to specify which world you meant to inhabit.

The lesson: if you mean a table you eat dinner on, say so. Don’t assume the universe is built around your implied furniture.

‘Tree’ behaves similarly. Does the user mean a birch in a forest? A branching data structure? A phylogenetic diagram? MEOW GPT won’t decide that for you; nor should it. Precision is your job.

This is precisely why I tested ‘character (in Aristotle’s virtue-ethical sense)’ rather than tossing ‘character’ in like a confused undergraduate hoping for luck.

Non-Western Concepts

I also tested concepts well outside the Western philosophical sandbox. This is where the model revealed its real strength.

Enter ‘karma’: it promptly explained that the Western reduction is a cultural oversimplification and – quite rightly – flagged that different Eastern traditions use the term differently. Translation: specify your flavour.

Enter ‘anattā’: the model demonstrated that Western interpretations often reduce the concept to a caricature. Which, frankly, they do.

Enter ‘hózhó’: the Navajo term survives mostly in the anthropological imagination, and the model openly described it as nearly ineffable – especially to those raised in cultures that specialise in bulldozing subtlety. On that score, no notes.

Across the board, I was trying to see whether MEOW GPT would implode when confronted with concepts that resist neat Western categorisation. It didn’t. It was annoyingly robust.

Closing Notes

If you do try the MEOW GPT and find its results surprising, illuminating, or mildly offensive to your metaphysical sensibilities, let me know – and tell me why. It helps me understand what the engine does well and what illusions it quietly pops along the way. Your feedback may even keep me from adding further warning labels, though I wouldn’t count on it.

A Critique of Reason (Not to Be Confused with Kant’s)

2–3 minutes

Kant, bless him, thought he was staging the trial of Reason itself, putting the judge in the dock and asking whether the court had jurisdiction. It was a noble spectacle, high theatre of self-scrutiny. But the trick was always rigged. The presiding judge, the prosecution, the jury, the accused, all wore the same powdered wig. Unsurprisingly, Reason acquitted itself.

The Enlightenment’s central syllogism was never more than a parlour trick:

  • P1: The best path is Reason.
  • P2: I practice Reason.
  • C: Therefore, Reason is best.

It’s the self-licking ice-cream cone of intellectual history. And if you dare to object, the trap springs shut: what, you hate Reason? Then you must be irrational. Inquisitors once demanded heretics prove they weren’t in league with Satan; the modern equivalent is being told you’re “anti-science.” The categories defend themselves by anathematising doubt.

The problem is twofold:

First, Reason never guaranteed agreement. Two thinkers can pore over the same “facts” and emerge with opposite verdicts, each sincerely convinced that Reason has anointed their side. In a power-laden society, it is always the stronger voice that gets to declare its reasoning the reasoning. As Dan Hind acidly observed, Reason is often nothing more than a marketing label the powerful slap on their interests.

Second, and this is the darker point, Reason itself is metaphysical, a ghost in a powdered wig. To call something “rational” is already to invoke an invisible authority, as if Truth had a clerical seal. Alasdair MacIntyre was right: strip away the old rituals and you’re left with fragments, not foundations.

Other witnesses have tried to say as much. Horkheimer and Adorno reminded us that Enlightenment rationality curdles into myth the moment it tries to dominate the world. Nietzsche laughed until his throat bled at the pretence of universal reason, then promptly built his own metaphysics of will. Bruno Latour, in We Have Never Been Modern, dared to expose Science as what it actually is – a messy network of institutions, instruments, and politics masquerading as purity. The backlash was so swift and sanctimonious that he later called it his “worst” book, a public recantation that reads more like forced penance than revelation. Even those who glimpsed the scaffolding had to return to the pews.

So when we talk about “Reason” as the bedrock of Modernity, let’s admit the joke. The bedrock was always mist. The house we built upon it is held up by ritual, inertia, and vested interest, not granite clarity. Enlightenment sold us the fantasy of a universal judge, when what we got was a self-justifying oracle. Reason is not the judge in the courtroom. Reason is the courtroom itself, and the courtroom is a carnival tent – all mirrors, no floor.

Welcome to the Casino of Justice

Welcome to the Grand Casino of Justice, where the chips are your civil liberties, the roulette wheel spins your fate, and the house—ever-smug in its powdered wig of procedural decorum—always wins.

Step right up, citizens! Marvel at the dazzling illusions of “science” as performed by your local constabulary: the sacred polygraph, that magnificent artefact of 1920s snake oil, still trotted out in back rooms like a séance at a nursing home. Never mind that it measures stress, not deception. Never mind that it’s been dismissed by any scientist with a functioning prefrontal cortex. It’s not there to detect truth—it’s there to extract confession. Like a slot machine that only pays out when you agree you’re guilty.

Audio: NotebookLM podcast on this topic.

And oh, the forensic pageantry! The blacklight! The dramatic swabs! The breathless invocations of “trace evidence,” “blood spatter patterns,” and—ooh! ahh!—fingerprints, those curly little whorls of manufactured certainty. You’ve been told since childhood that no two are alike, that your prints are your identity. Rubbish. Human fingerprint examiners disagree with themselves when presented with the same print twice. In blind tests. And yes—this bears repeating with appropriate incredulity—koalas have fingerprints so uncannily similar to ours they’ve confused human forensic analysts. Somewhere, a marsupial walks free while a teenager rots in remand.

You see, it’s not about justice. It’s about control. Control through performance. The legal system, like a casino, isn’t interested in fairness—it’s interested in outcome. It needs to appear impartial, all robes and solemnity, while tipping the odds ever so slightly, perpetually, in its own favour. This is jurisprudence as stagecraft, science as set-dressing, and truth as a collateral casualty.

And who are the croupiers of this great charade? Not scientists, no. Scientists are too cautious, too mired in uncertainty, too concerned with falsifiability and statistical error margins. No, your case will be handled by forensic technicians with just enough training to speak jargon, and just enough institutional loyalty to believe they’re doing the Lord’s work. Never mind that many forensic methods—bite mark analysis, tool mark “matching,” even some blood spatter interpretations—are about as scientifically robust as a horoscope printed on a cereal box.

TV crime dramas, of course, have done their bit to embalm these myths in the cultural subconscious. “CSI” isn’t a genre—it’s a sedative, reassuring the public that experts can see the truth in a hair follicle or the angle of a sneeze. In reality, most convictions hinge on shoddy analysis, flawed assumptions, and a little prosecutorial sleight of hand. But the juries are dazzled by the sciencey buzzwords, and the judges—God bless their robes—rarely know a confidence interval from a cornflake.

So, what do you do when accused in the great Casino of Justice? Well, if you’re lucky, you lawyer up. If you’re not, you take a plea deal, because 90% of cases never reach trial. Why? Because the system is designed not to resolve guilt, but to process bodies. It is a meat grinder that must keep grinding, and your innocence is but a small bone to be crushed underfoot.

This isn’t justice. It’s a theatre of probability management, where the goal is not truth but resolution. Efficiency. Throughput. The house keeps the lights on by feeding the machine, and forensic science—real or imagined—is merely the window dressing. The roulette wheel spins, the dice tumble, and your future hangs on the angle of a smudge or the misreading of a galvanic skin response.

Just don’t expect the koalas to testify. They’re wise enough to stay in the trees.

A Buddhist Critique of Modern Livelihoods

It’s interesting to me that as an atheist and non-cognitivist, I can take the moral high ground relative to health insurance concerns in the United States. So, I write about it.

Blood Money and Broken Principles

In the aftermath of the tragic killing of Brian Thompson, the CEO of a health insurance conglomerate, a striking narrative has emerged. Many Americans view this act—shocking though it is—as emblematic of the anger and despair born of a system that profits by exploiting human vulnerability. Such reactions compel us to examine the ethics of industries that flourish on what can only be described as blood money. From health insurance to tobacco, alcohol, and the arms trade, these livelihoods raise profound ethical questions when viewed through the lens of the Buddhist Noble Eightfold Path, specifically Right Livelihood and Right Action.

The Moral Framework: Buddhism’s Path to Ethical Livelihood

Buddhism’s Eightfold Path provides a blueprint for ethical living, with Right Livelihood and Right Action serving as its ethical cornerstones. These principles demand that one’s work and deeds contribute to the welfare of others, avoid harm, and align with compassion and integrity. In short, they urge us to earn a living in a manner that uplifts rather than exploits. The health insurance industry’s business model—which often prioritises profits over the preservation of life—challenges these tenets in ways that are difficult to overlook.

Consider the denial of coverage for life-saving treatments, the exploitation of legal loopholes to reduce payouts, or the systemic perpetuation of healthcare inequality. These actions, while legally sanctioned, conflict sharply with the Buddhist ideal of avoiding harm and promoting well-being. Yet, this industry is not alone in its ethical failings. Many others—both legal and illegal—fall similarly short.

Industries of Exploitation: Tobacco, Alcohol, and Arms

The tobacco and alcohol industries provide stark examples of livelihoods that thrive on human suffering. Their products, despite their legality, are designed to foster dependency and harm. They exact a heavy toll on both individual lives and public health systems, a reality that makes them incompatible with Right Livelihood. The arms trade—arguably the most egregious example—profits directly from conflict and human misery. How can such industries possibly align with the Buddhist ideal of ahimsa (non-violence) or the compassionate aspiration to alleviate suffering?

In these cases, the harm caused is not incidental; it is fundamental to their business models. Whether one manufactures cigarettes, brews alcohol, or sells weapons, the destruction wrought by these activities is integral to their profitability. The contradiction is stark: the greater the harm, the greater the profit. This stands in direct opposition to the Buddhist call for livelihoods that sustain and support life.

Organised Crime: The Dark Mirror

When we turn to organised crime, the parallels become even more unsettling. Whether it’s the drug trade, human trafficking, or financial fraud, these activities epitomise unethical livelihoods. They exploit the vulnerable, foster violence, and undermine social cohesion. Yet, when viewed alongside certain legal industries, the line between “organised crime” and “corporate enterprise” begins to blur. Is the denial of life-saving healthcare less egregious than a gang’s extortion racket? Both profit by preying on human suffering. Both thrive in systems that prioritise gain over humanity.

The Buddhist Response: From Outrage to Action

Buddhism does not condone violence, no matter how symbolic or righteous it may appear. Right Action demands non-violence not only in deeds but also in thoughts and intentions. The killing of Brian Thompson, though perhaps an act of desperation or symbolism, cannot align with Buddhist ethics. Yet this tragedy should not eclipse the broader systemic critique. The true challenge is not to exact retribution but to transform the systems that perpetuate harm.

To move forward, we must ask how our societies can pivot toward livelihoods that align with compassion and justice. This entails holding exploitative industries to account and fostering economic systems that prioritise well-being over profit. The Buddhist path offers not only a critique of harmful practices but also a vision for ethical living—a vision that demands courage, compassion, and unwavering commitment to the common good.

Conclusion: Choosing a Better Path

The case of Brian Thompson’s killing is a symptom of a much larger ethical crisis. It forces us to confront uncomfortable truths about the industries that shape our world. Whether we scrutinise health insurance, tobacco, alcohol, the arms trade, or organised crime, the moral calculus remains the same: livelihoods that thrive on harm cannot be reconciled with the principles of Right Livelihood and Right Action.

As individuals and societies, we face a choice. We can continue to turn a blind eye to the suffering embedded in these industries, or we can commit to transforming them. The Buddhist path challenges us to choose the latter, to build systems and livelihoods rooted in compassion and justice. In doing so, we can begin to heal not only the wounds of individual tragedies but also the deeper fractures in our collective soul.

The Trolley Problem of For-Profit Healthcare:

Loops of Death and Denial

The trolley problem is a philosophical thought experiment that pits action against inaction. In the original version, a person faces a choice: a trolley hurtles down a track toward five people tied to the rails, but a lever allows the trolley to be diverted onto another track, where one person is tied. The dilemma is simple in its grotesque arithmetic: let five die or actively kill one to save them. A perennial favourite of ethics classes, the trolley problem is most often used to explore Consequentialism, particularly Utilitarianism, and its cool calculus of harm minimisation. Over the years, countless variations have been conjured, but few approach the nightmarish reality of its real-world application: the for-profit healthcare system in the United States.

With the recent death of UnitedHealthcare CEO Brian Thompson, the trolley dilemma takes on a new and morbid relevance. Let’s reframe the challenge.

The Healthcare Trolley Loop

Picture the trolley again on a bifurcated track. The lever remains, as does the moral agent poised to decide its fate. This time, the agent is Brian Thompson. The setup is simple: one track leads to the deaths of five people, and the other is empty. But here’s the twist: the trolley doesn’t just pass once in this version—it’s on a loop. At every interval, Thompson must decide whether to pull the lever and send the trolley to the empty track or allow it to continue its deadly course, killing five people each time.

But Thompson isn’t just deciding in a vacuum. The track with five people comes with a financial incentive: each life lost means higher profits, better quarterly earnings, and soaring shareholder returns. Diverting the trolley to the empty track, meanwhile, offers no payout. It’s not a single moral quandary; it’s a recurring decision, a relentless calculus of death versus dollars.

This isn’t just a metaphor; it’s a business model. For-profit healthcare doesn’t merely tolerate death—it commodifies it. The system incentivises harm through denial of care, inflated costs, and structural inefficiencies that ensure maximum profit at the expense of human lives.

Enter the Shooter

Now, introduce the wildcard: the shooter. Someone whose loved one may have been one of the countless victims tied to the track. They see Thompson at the lever, his decisions ensuring the endless loop of suffering and death. Perhaps they believe that removing Thompson can break the cycle—that a new lever-puller might divert the trolley to the empty track.

Thompson is killed, but does it change anything? The system remains. Another CEO steps into Thompson’s place, hand on the lever, ready to make the same decision. Why? Because the tracks, the trolley, and the profit motive remain untouched. The system ensures that each decision-maker faces the same incentives, pressures, and chilling rationale: lives are expendable; profits are not.

The Problem of Plausible Deniability

The shooter’s actions are vilified because they are active, visible, and immediate. A single violent act is morally shocking, and rightly so. But what of the quiet violence perpetuated by the healthcare system? The denial of coverage, the refusal of life-saving treatments, the bankruptcy-inducing bills—all are forms of systemic violence, their harm diffused and cloaked in the language of economic necessity.

The for-profit model thrives on this plausible deniability. Its architects and operators can claim they’re simply “following the market,” that their hands are tied by the invisible forces of capitalism. Yet the deaths it causes are no less real, no less preventable. The difference lies in perception: the shooter’s act is direct and visceral, while the system’s violence is passive and bureaucratic, rendered almost invisible by its banality.

A System Built on Death

Let’s not mince words: the current healthcare system is a death loop. It’s not an accident; it’s a feature. Profit-seeking in healthcare means there is always a financial incentive to let people die. During the Affordable Care Act (ACA) debates, opponents of universal healthcare decried the spectre of “death panels,” bureaucrats deciding who lives and who dies. Yet this is precisely what for-profit insurance companies do—only their decisions are driven not by medical necessity or moral considerations, but by spreadsheets and stock prices.

This is the logic of capitalism writ large: maximise profit, externalise harm, and frame systemic failures as unavoidable. Healthcare is merely one example. Across industries, the same dynamic plays out, whether in environmental destruction, labour exploitation, or financial crises. The trolley always runs on tracks built for profit, and the bodies left in its wake are just collateral damage.

How to Break the Loop

The death of Brian Thompson changes nothing. The system will simply produce another Thompson, another lever-puller incentivised to make the same deadly decisions. Breaking the loop requires dismantling the tracks themselves.

  1. Remove the Profit Motive: Healthcare should not be a marketplace but a public good. Universal single-payer systems, as seen in many other developed nations, prioritise care over profit, removing the incentive to let people die for financial gain.
  2. Recognise Passive Harm as Active: We must stop excusing systemic violence as “inevitable.” Denying care, pricing treatments out of reach, and allowing medical bankruptcy are acts of violence, no less deliberate than pulling a trigger.
  3. Hold the System Accountable: It’s not just the CEOs at fault; the lawmakers, lobbyists, and corporations sustain this deadly status quo. The blood is on their hands, too.

Conclusion: The Real Villain

The shooter is not the solution, but neither is their act the real crime. The healthcare system—and by extension, capitalism itself—is the true villain of this story. It constructs the tracks, builds the trolley, and installs lever-pullers like Brian Thompson to ensure the loop continues.

When will it end? When we stop debating which track to divert the trolley toward and start dismantling the system that made the trolley inevitable in the first place. Until then, we are all complicit, passengers on a ride that profits from our suffering and death. The question isn’t who’s at the lever; it’s why the trolley is running at all.

The Rise of AI: Why the Rote Professions Are on the Chopping Block

Medical doctors, lawyers, and judges have been the undisputed titans of professional authority for centuries. Their expertise, we are told, is sacrosanct, earned through gruelling education, prodigious memory, and painstaking application of established knowledge. But peel back the robes and white coats, and you’ll find something unsettling: a deep reliance on rote learning—an intellectual treadmill prioritising recall over reasoning. In an age where artificial intelligence can memorise and synthesise at scale, this dependence on predictable, replicable processes makes these professions ripe for automation.

Rote Professions in AI’s Crosshairs

AI thrives in environments that value pattern recognition, procedural consistency, and brute-force memory—the hallmarks of medical and legal practice.

  1. Medicine: The Diagnosis Factory
    Despite its life-saving veneer, medicine is largely a game of matching symptoms to diagnoses, dosing regimens, and protocols. Enter an AI with access to the sum of human medical knowledge: not only does it diagnose faster, but it also skips the inefficiencies of human memory, emotional bias, and fatigue. Sure, we still need trauma surgeons and such, but diagnosticians are so yesterday’s news.
    Why pay a six-figure salary to someone recalling pharmacology tables when AI can recall them perfectly every time? Future healthcare models are likely to see Medical Technicians replacing high-cost doctors. These techs, trained to gather patient data and operate alongside AI diagnostic systems, will be cheaper, faster, and—ironically—more consistent.
  2. Law: The Precedent Machine
    Lawyers, too, sit precariously on the rote-learning precipice. Case law is a glorified memory game: citing the right precedent, drafting contracts based on templates, and arguing within frameworks so well-trodden that they resemble legal Mad Libs. AI, with its infinite recall and ability to synthesise case law across jurisdictions, makes human attorneys seem quaintly inefficient. The future isn’t lawyers furiously flipping through books—it’s Legal Technicians trained to upload case facts, cross-check statutes, and act as intermediaries between clients and the system. The $500-per-hour billable rate? A relic of a pre-algorithmic era.
  3. Judges: Justice, Blind and Algorithmic
    The bench isn’t safe, either. Judicial reasoning, at its core, is rule-based logic applied with varying degrees of bias. Once AI can reliably parse case law, evidence, and statutes while factoring in safeguards for fairness, why retain expensive and potentially biased judges? An AI judge, governed by a logic verification layer and monitored for compliance with established legal frameworks, could render verdicts untainted by ego or prejudice.
    Wouldn’t justice be more blind without a human in the equation?

The Techs Will Rise

Replacing professionals with AI doesn’t mean removing the human element entirely. Instead, it redefines roles, creating new, lower-cost positions such as Medical and Legal Technicians. These workers will:

  • Collect and input data into AI systems.
  • Act as liaisons between AI outputs and human clients or patients.
  • Provide emotional support—something AI still struggles to deliver effectively.

The shift also democratises expertise. Why restrict life-saving diagnostics or legal advice to those who can afford traditional professionals when AI-driven systems make these services cheaper and more accessible?

But Can AI Handle This? A Call for Logic Layers

AI critics often point to hallucinations and errors as proof of its limitations, but this objection is shortsighted. What’s needed is a logic layer: a system that verifies whether the AI’s conclusions follow rationally from its inputs.

  • In law, this could ensure AI judgments align with precedent and statute.
  • In medicine, it could cross-check diagnoses against the DSM, treatment protocols, and patient data.

A second fact-verification layer could further bolster reliability, scanning conclusions for factual inconsistencies. Together, these layers would mitigate the risks of automation while enabling AI to confidently replace rote professionals.

Resistance and the Real Battle Ahead

Predictably, the entrenched elites of medicine, law, and the judiciary will resist these changes. After all, their prestige and salaries are predicated on the illusion that their roles are irreplaceable. But history isn’t on their side. Industries driven by memorisation and routine application—think bank tellers, travel agents, and factory workers—have already been disrupted by technology. Why should these professions be exempt?

The real challenge lies not in whether AI can replace these roles but in public trust and regulatory inertia. The transformation will be swift and irreversible once safeguards are implemented and AI earns confidence.

Critical Thinking: The Human Stronghold

Professions that thrive on unstructured problem-solving, creativity, and emotional intelligence—artists, philosophers, innovators—will remain AI-resistant, at least for now. But the rote professions, with their dependency on standardisation and precedent, have no such immunity. And that is precisely why they are AI’s lowest-hanging fruit.

It’s time to stop pretending that memorisation is intelligence, that precedent is innovation, or that authority lies in a gown or white coat. AI isn’t here to make humans obsolete; it’s here to liberate us from the tyranny of rote. For those willing to adapt, the future looks bright. For the rest? The machines are coming—and they’re cheaper, faster, and better at your job.

Language: Tool for Clarity or Shaper of Reality?

6–8 minutes

Pinker: The Optimist Who Thinks Language Works

Enter Steven Pinker, a cognitive scientist and eternal optimist about language. While we’ve been busy pointing out how language is a jumbled mess of misunderstandings, Pinker comes along with a sunny outlook, waving his banner for the language instinct. According to Pinker, language is an evolved tool – something that our brains are wired to use, and it’s good. Really good. So good, in fact, that it allowed us to build civilisations, exchange complex ideas, and, you know, not get eaten by sabre-toothed tigers.

Sounds like a nice break from all the linguistic doom and gloom, right? Pinker believes that language is a powerful cognitive skill, something we’ve developed to communicate thoughts and abstract ideas with remarkable precision. He points to the fact that we’re able to create entire worlds through language – novels, philosophies, legal systems, and scientific theories. Language is, to him, one of the greatest achievements of the human mind.

But here’s where things get a little sticky. Sure, Pinker’s optimism about language is refreshing, but he’s still not solving our core problem: meaning. Pinker may argue that language works wonderfully for most of our day-to-day communication – and in many cases, he’s right. We can all agree that saying, “Hey, don’t touch the flamey thing” is a pretty effective use of language. But once we start using words like ‘freedom’ or ‘justice’, things start to unravel again.

Take a sentence like ‘freedom is essential’. Great. Pinker might say this is a perfectly formed thought, conveyed using our finely tuned linguistic instincts. But the problem? Ask five people what ‘freedom’ means, and you’ll get five different answers. Sure, the grammar is flawless, and everyone understands the sentence structurally. But what they mean by ‘freedom’? That’s a whole other ball game.

Pinker’s language instinct theory helps explain how we learn language, but it doesn’t really account for how we use language to convey abstract, subjective ideas. He might tell us that language has evolved as an efficient way to communicate, but that doesn’t fix the problem of people using the same words to mean wildly different things. You can be the most eloquent speaker in the world, but if your definition of ‘freedom’ isn’t the same as mine, we’re still lost in translation.

And let’s not forget: while language is indeed a fantastic tool for sharing information and surviving in complex societies, it’s also great at creating conflicts. Wars have been fought over differences in how people interpret words like ‘justice’ or ‘rights’. Pinker might say we’ve evolved language to foster cooperation, but history suggests we’ve also used it to argue endlessly about things we can never quite agree on.

So, yes, Pinker’s right – language is a cognitive marvel, and it’s gotten us pretty far. But his optimism doesn’t quite stretch far enough to cover the fact that language, for all its brilliance, still leaves us stuck in a web of interpretation and miscommunication. It’s like having a state-of-the-art GPS that works perfectly – until you get to that roundabout and suddenly no one knows which exit to take.

In the end, Pinker’s got a point: language is one of the most sophisticated tools we’ve ever developed. It’s just a shame that when it comes to abstract concepts, we still can’t agree on which way’s north.

Sapir-Whorf: Language Shapes Reality – Or Does It?

Now it’s time for the Sapir-Whorf hypothesis to take the stage, where things get really interesting – or, depending on your perspective, slightly ridiculous. According to this theory, the language you speak actually shapes the way you see the world. Think of it as linguistic mind control: your perception of reality is limited by the words you have at your disposal. Speak the wrong language, and you might as well be living on another planet.

Sounds dramatic, right? Here’s the gist: Sapir and Whorf argued that the structure of a language affects how its speakers think and perceive the world. If you don’t have a word for something, you’re going to have a hard time thinking about that thing. Inuit languages, for example, are famous for having multiple words for different kinds of snow. If you’re an Inuit speaker, the hypothesis goes, you’re much more attuned to subtle differences in snow than someone who just calls it all ‘snow’.

Now, on the surface, this sounds kind of plausible. After all, we do think using language, don’t we? And there’s some truth to the idea that language can influence the way we categorise and describe the world. But here’s where Sapir-Whorf starts to go off the deep end. According to the stronger version of this hypothesis, your entire reality is shaped and limited by your language. If you don’t have the word for “freedom” in your language, you can’t experience it. If your language doesn’t have a word for “blue,” well, guess what? You don’t see blue.

Let’s take a step back. This sounds like the kind of thing you’d hear at a dinner party from someone who’s just a little too impressed with their first year of linguistics classes. Sure, language can shape thought to a degree, but it doesn’t have a stranglehold on our perception of reality. We’re not prisoners of our own vocabulary. After all, you can still experience freedom, even if you’ve never heard the word. And you can certainly see blue, whether your language has a word for it or not.

In fact, the idea that you’re trapped by your language is a little insulting, when you think about it. Are we really saying that people who speak different languages are living in different realities? That a person who speaks Mandarin sees the world in a fundamentally different way than someone who speaks Spanish? Sure, there might be some subtle differences in how each language breaks down concepts, but we’re all still human. We’re all still sharing the same world, and no matter what language we speak, we still have the cognitive capacity to understand and experience things beyond the limits of our vocabulary.

Let’s also not forget that language is flexible. If you don’t have a word for something, you make one up. If you’re missing a concept, you borrow it from another language or invent a metaphor. The idea that language is some kind of mental prison ignores the fact that we’re constantly evolving our language to keep up with the way we see the world—not the other way around.

And here’s the real kicker: if Sapir and Whorf were right, and we’re all walking around in little linguistic bubbles, then how on earth have we managed to translate anything? How have entire philosophies, religious texts, and scientific theories made their way across cultures and languages for centuries? If language really was shaping our reality that strongly, translation would be impossible – or at least incredibly limited. But here we are, discussing concepts like ‘freedom’, ‘justice’, and ‘truth’ across languages, cultures, and centuries.

So while it’s fun to entertain the idea that your language shapes your reality, let’s not give it too much credit. Yes, language can influence how we think about certain things. But no, it doesn’t define the boundaries of our existence. We’re not all stuck in a linguistic matrix, waiting for the right word to set us free.


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From Signs to Abstractions: The Slippery Slope of Meaning

5–7 minutes

Saussure and the Signified: Words as Slippery Symbols

Fast-forward a few thousand years, and humans are no longer just warning each other about hot flames or toothy predators. We’ve moved on to the exciting world of abstract thought, but the language tools we’re using haven’t quite caught up. Enter Ferdinand de Saussure, who basically waltzed in to tell us, ‘Hey, all those words you’re throwing around? They’re not doing what you think they’re doing.’

Saussure gave us the idea of the signifier and the signified. Now, don’t let the fancy terms fool you. It’s just a way of pointing out that when we say ‘tree’, we’re not actually talking about a tree. No, we’re using the word ‘tree’ as a symbol – a signifier – that points to the idea of a tree. The signified is the actual concept of ‘tree-ness’ floating around in your brain. But here’s the kicker: everyone’s idea of a tree is a little different.

And this isn’t just a language problem – it’s an art problem too. Enter René Magritte, the surrealist artist who really drove this point home with his famous painting, Ceci n’est pas une pipe (‘This is not a pipe’). At first glance, it looks like a straightforward picture of a pipe, but Magritte was making a deeper point. It’s not actually a pipe – it’s an image of a pipe, a representation. You can’t stuff it with tobacco and smoke it, because what you’re looking at is a representation, not the real thing.

Image: La Trahison des Images, René François Ghislain Magritte

In the same way, when we use words, we’re not talking about the thing itself – we’re just waving a flag toward the concept of that thing. So, when you say ‘tree’, you’re really saying ceci n’est pas un arbre – this is not a tree. It’s just a word, a placeholder, a verbal painting of something real. And just like Magritte’s pipe, it’s easy to get confused. You might think you’re talking about the same tree, or the same ‘freedom’, but all you’ve got is a symbol – and everyone’s symbol looks a little different.

This is where things start to unravel. Words are slippery symbols, and as soon as we move away from concrete, physical objects – like trees or, yes, pipes – and into abstract ideas, like ‘justice’ or ‘truth’, the symbols become even harder to hold onto. The cracks in language start to widen, and before you know it, you’re no longer even sure if you’re talking about the same concept at all.

Language, Saussure argues, isn’t this neat, objective system we thought it was. It’s a game we’re playing, and the rules are written in invisible ink. By the time we get to abstract nouns, we’re basically playing with loaded dice. You think you’re communicating clearly, but every word you use is just a placeholder for the idea you hope the other person has in their head. And nine times out of ten? They don’t.

So, while early humans were struggling to agree on the ‘flamey thing’, we’re here trying to agree on concepts that are infinitely more complicated. And Saussure? He’s just sitting in the corner with a smirk, telling us we never had control over language in the first place. “Good luck with your ‘truth'”, he seems to be saying. ‘I’m sure it’ll mean the same thing to everyone’.

Abstraction: Enter Freedom, Truth, and Confusion

Now that we’ve wrapped our heads around the fact that words are nothing but slippery symbols, let’s take it up a notch. You thought ‘tree’ was tricky? Try something more abstract. Enter: freedom, truth, justice. Things that can’t be seen, touched, or stuffed into a pipe. Here’s where language goes from being slippery to downright treacherous.

See, early language worked because it was tied to concrete things. ‘Toothey thing scary’ wasn’t up for debate. Either you got eaten, or you didn’t. Simple. But then humans, ever the overachievers, decided it wasn’t enough to just label the world around them. They wanted to label ideas, too – things that don’t have any physical form but somehow drive us all crazy.

Take ‘freedom’, for instance. Sounds nice, right? Except, if you ask ten people what it means, you’ll get ten different answers. For some, it’s ‘freedom from’ something – a kind of liberation. For others, it’s ‘freedom to’ do whatever you want, whenever you want. And yet for others, it’s an abstract ideal tied up in political philosophy. Suddenly, you’re not just dealing with different trees – you’re dealing with entirely different forests.

The same goes for truth. Is it objective? Subjective? Relative? Absolute? Everyone’s got a different take. Plato had his own grand ideas about ‘Truth’ with a capital T, while Nietzsche basically rolled his eyes and said, ‘Good luck with that’. You’re out here using the word, assuming it means the same thing to everyone else, but really you’re all just talking past each other.

And don’t even get started on justice. Some say it’s about fairness, others say it’s about the law, and still others think it’s just a nice idea for dinner party debates. The problem with these words – these abstract nouns – is that they represent ideas that live entirely in our heads. Unlike the ‘flamey thing’ or the ‘toothey thing’, there’s no physical reality to pin them to. There’s no universally agreed-upon image of ‘freedom’ that we can all point to and nod along, like Magritte’s pipe. There’s just… vague agreement. Sometimes. On a good day.

This is where language really starts to break down. You might think you’re having a productive conversation about ‘freedom’ or ‘truth’, but half the time, you’re speaking different languages without even realising it. Words like these aren’t just slippery – they’re shapeshifters. They bend and morph depending on who’s using them, when, and why.

So, while early humans were busy with their simple, effective ‘toothey thing scary’, we’re now trying to nail down ideas that refuse to be nailed down. What started as a useful survival tool has turned into a game of philosophical Twister, with everyone tied up in knots trying to define something they can’t even see. And, as usual, language is just standing in the corner, smirking, knowing full well it’s not up to the task.


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