Month: August 2026

The Navi Will See You Now: What Happens When AI Can Generate Your Blockbuster On Demand

There’s a tempting, tidy theory floating around about AI and cinema: blockbusters are formulaic, formula is what large language and video models are good at automating, therefore blockbusters will be the first casualties of generative video, while scrappy, idiosyncratic indie films will remain a human stronghold. It’s a clean thesis. It’s also, as of mid-2026, almost exactly backwards.

The Near-Term Picture Is Inverted

Look at where fully AI-generated feature films are actually showing up right now. Dreams of Violets, a live-action AI film that premiered at Tribeca this year, cost roughly $2,000 to make — no cameras, no sets, no actors. Fountain O, the studio behind it, followed up with a second no-budget AI feature, Odysseus: The Fall. Meanwhile, the actual $250 million tentpole of the year is Christopher Nolan’s The Odyssey — traditionally shot, traditionally cast, about as human-made as a blockbuster gets. Studios like Lionsgate are investing heavily in AI, but almost entirely as an internal tool: de-aging, dubbing, VFX augmentation, post-production efficiency. AI inside a human-directed pipeline, not replacing it.

There are structural reasons for this inversion, and they’re not going away soon:

Star power resists automation. A meaningful share of blockbuster economics is built on paying to watch a specific, real, famous person. An AI-generated stand-in isn’t the same product, legally or commercially — which is exactly why the launch of “AI actress” Tilly Norwood into a starring feature role (Misaligned) triggered such a visceral industry backlash this year. The star system depends on realness as much as performance.

Unions have leverage precisely where the money is. SAG-AFTRA and the WGA fought hard for AI protections, and that leverage is strongest on union-crewed studio productions — not on a two-person team generating a short on a laptop. If anything, the low-budget, experimental end of the business has fewer institutional obstacles to full AI adoption right now, not more.

Two hours of coherence is still a harder problem than a few minutes of spectacle. “Formulaic” doesn’t mean “easy to generate.” Sustained character consistency, continuity, and plot logic across a feature runtime remains one of the genuine frontiers for video models — arguably harder than short-form stylized content, which cuts against the idea that formula makes something automatically AI-tractable.

Blockbusters carry more brand risk. A studio sitting on a $200 million franchise has far more to lose from a lawsuit, a synthetic-media backlash, or a quality miss than an indie release does. Risk-aversion at that budget level slows adoption of anything unproven — even when it’s cheaper.

So the more accurate near-term prediction isn’t “blockbusters get automated, indies stay human.” It’s closer to: AI colonizes the cheap, high-volume, low-prestige tier first — streaming filler, ad content, background production — while star-driven tentpoles keep humans in the loop longer, because a real, ownable human being is precisely what audiences are paying a premium for. Indie film may in fact be the place full AI production normalizes soonest, simply because it removes the capital barrier for people who couldn’t otherwise afford cameras, actors, and crews at all.

But the Near Term Isn’t the End State

Push the question further out, though, and the calculus changes. The obstacles above aren’t all the same kind of obstacle.

Narrative coherence and physical plausibility are engineering problems, and engineering problems tend to yield to time. There’s no principled reason a sufficiently advanced generator can’t eventually produce two hours of tight, coherent, visually spectacular storytelling.

The economics could flip entirely, too. Blockbusters are expensive today because of physical production — sets, stunts, locations, star fees. If a generator makes another spectacular action sequence functionally free to produce and iterate on, the caution that currently protects traditional production stops being a brand-safety move and starts looking like a competitive liability.

What’s less clear is whether the desire for realness fades. A lot of blockbuster value isn’t “two hours of well-structured spectacle” — it’s specifically “two hours of that person.” That may be closer to why a live concert retains value even when a perfect recording exists at home: some of what’s being purchased is the fact of authenticity itself. Whether that preference is a durable feature of what movies are for, or a transitional habit that erodes with generational turnover the way objections to CGI or digital cameras mostly did — that’s the real open question, and it matters more than whether the technology gets good enough. It probably will.

The Knowledge Navigator Problem

There’s a further-out possibility that changes the shape of the question entirely: a system — call it a Navi, after Apple’s old Knowledge Navigator concept — that reads your face when you walk in the door, infers your mood, and generates a film tuned to exactly that emotional state and your accumulated taste profile. Mood-inference from expression is already commercial technology, however imperfect. Pair it with a generative model capable of coherent long-form video and a rich personal taste history, and this stops being science fiction. It’s an engineering roadmap.

But notice what this actually describes: not “blockbusters becoming AI-generated,” but the dissolution of the blockbuster as a category. A blockbuster’s value isn’t just the film itself — it’s the fact that tens of millions of people watched the same thing and can talk about it afterward. A film generated uniquely for one viewer, watched by no one else in that exact form, isn’t a blockbuster in any sense we currently mean. It’s closer to a sophisticated personal entertainment appliance.

The more plausible outcome is bifurcation rather than replacement: personalized, mood-matched, largely automated content for private consumption, coexisting with shared cultural events — theatrical releases, appointment viewing — whose value is partly defined by not being personalized. That’s not nostalgia; it’s the same reason people still attend concerts when perfect recordings exist. Part of what’s being consumed is the fact of synchronized experience itself, which by definition can’t be individually generated.

There’s a sharper concern buried in the mood-scanning piece specifically. A system that reads your affect and hands you emotionally-optimized content on arrival is a short step from an engagement-maximization machine using your face as the control signal — a more intimate version of the algorithmic feed problem social media already has. Content calibrated to what you already want to feel is a different, and probably lesser, thing than a story that might actually move or challenge you.

Licensing the Sandbox, Not the Story

If personalized generation becomes real, IP holders face an obvious business model shift: license not a fixed story but a flavor pack — setting, characters, aesthetic, thematic DNA — and let each viewer’s Navi interpret it freely. There’s already a working analogue for this. Tabletop RPGs and licensed game universes function exactly this way: Wizards of the Coast doesn’t sell you a single Forgotten Realms story, it sells a setting bible, and individual tables generate their own sanctioned experiences within it. What’s being described for film is that model, minus the multiplayer table, mediated by a private AI instead.

The friction is less creative than legal. A licensed character built on a real performer’s face — Harrison Ford’s Deckard, for instance — turns infinite personalized regeneration into an ongoing rights and royalty question, not a one-time production fee. Every generation is, legally, a new performance, which is precisely the ground SAG-AFTRA fought over in its 2023 contract. IP holders will likely end up choosing between original synthetic characters unencumbered by real likeness rights, or expensive perpetual-use likeness deals that meaningfully change the economics of “infinite personalized content.”

And the canon question doesn’t disappear — it sharpens. If everyone’s version of a franchise is different, there’s no franchise left to discuss at the water cooler. The likely structure mirrors what franchises already do with expanded universes: an official, human-curated canon released communally, sitting alongside an explicitly non-canonical sandbox layer available for personal, AI-mediated riffing.

Harrison Ford as Case Study

Ford is a useful test case precisely because he sits at an odd intersection: still alive, still working, but old enough that traditional franchise continuation is running out of runway. The infrastructure already exists in limited form — ILM de-aged him for Indiana Jones and the Dial of Destiny, built from decades of scanned footage. What’s being described here is that same technology decoupled from a single project and turned into a standing, licensable asset.

The legal foundation for a perpetual “digital Ford” already partially exists. Right of publicity survives death in most U.S. states — California’s lasts seventy years post-mortem — which is the mechanism that already lets estates license the deceased: Fred Astaire danced with a vacuum decades after his death, James Dean was cast via CGI in a 2019 film. “Harrison Ford, forever, in infinite personalized adventures” isn’t a legal novelty so much as an extension of a licensing category that already exists, now requiring explicit consent thanks to the actors’ 2023 contract wins.

What it does introduce is an uncomfortable incentive structure: the digital twin becomes more valuable than the man. Once a rich enough performance-capture library exists, the studio’s real asset isn’t Harrison Ford — it’s a trained model of him that performs indefinitely, never ages, never negotiates beyond the original deal. The actor’s economic interest becomes handing over the most complete possible version of himself once, in exchange for royalties, and then being effectively replaced by his own likeness for every future use.

The amusing, slightly poignant part is that Ford may end up being one of the last actors whose entire physical performance history was captured by cameras rather than generated from the outset — which paradoxically makes him more valuable as training data at precisely the moment the industry stops needing him to show up.

None of this requires any single dramatic breakthrough. Each piece — de-aging, mood inference, licensed sandboxes, posthumous likeness rights — already exists in some partial, working form today. What’s being described isn’t science fiction so much as the current trajectory, extended.

The Boring Apocalypse: Will the Singularity Arrive as a Lower Electric Bill?

There is a comforting story tech people tell themselves about how the Singularity — or something adjacent to it — will actually land: quietly. Not as a headline but as an infrastructure upgrade. The average person, on this account, will never experience AGI as an event. They’ll experience it as a slightly cheaper electric bill (fusion, AI-optimized), a forced computer upgrade (quantum-resistant encryption, or just faster chips), and otherwise nothing at all — because they’ll be too busy raising kids, working, and living to notice that the ground has shifted under them.

It’s a plausible story. It’s also, on inspection, a story that quietly contains its own refutation.

The precedent is real

Civilizational discontinuities have absorbed into daily life as texture rather than as events before. Electrification didn’t feel like a metaphysical rupture to most people who lived through it; it felt like a switch on the wall. Antibiotics didn’t feel like the abolition of a categorical human vulnerability; they felt like a pill your doctor gave you. The internet, in its early years, didn’t feel like the erection of a new nervous system for the species; it felt like a modem connecting slowly in the next room. In each case the technology that reorganized the substrate of civilization was received by most people as an output, not a cause. Nobody outside a small technical priesthood tracked the phase transition in real time. They tracked the artifact: the bill, the pill, the modem.

So when we say the current wave of LLM development — genuinely startling, by any measure, over just the last several days — is being met with a “meh” from the non-technical public, we are pattern-matching to something real. This has happened before. It could easily happen again, and at a civilizational scale that makes the previous examples look like rehearsals.

But the measurement is suspect

Here is the problem with over-trusting that “meh.” What registers as public reaction to AI right now is being measured almost entirely through a tech-media lens — model releases, benchmark leapfrogging, lab politics, the internal Kremlinology of who’s ahead of whom. The average person was never going to react to that layer, regardless of how singularity-adjacent it actually is. Nobody reacted to CUDA kernel optimizations either, and those quietly built the substrate for everything happening now. Measuring public sentiment by whether people are excited about a new model card is like measuring reaction to electrification by whether people were excited about improvements in copper wire purity.

The more honest measurement is the second-order effects, and there the picture is not indifference — it’s something closer to inchoate, distributed alarm. Public library systems are reporting unprecedented demand for “Avoiding AI” workshops. Parents are not indifferent to chatbots and their children; they are anxious about it in exactly the register you’d expect from people “too busy to notice” — which is to say, noticing in the domains that touch them directly (their kids, their jobs, their sense of what’s real) while remaining innocent of the domains that don’t (frontier lab strategy, alignment debates, benchmark scores). That’s not the same thing as not noticing the Singularity. That’s noticing it through the only apertures ordinary life provides.

Why this matters more than it first appears to

This distinction is not academic, and it is where the “boring apocalypse” thesis stops being comforting and starts being worrying. If the mechanism by which the public opts out of scrutiny is exhaustion and distraction rather than genuine disinterest, that is not a benign parallel-track outcome running alongside the tech story. That is the precondition for elite capture.

A populace that only notices the electric bill has, without quite choosing to, outsourced the entire interpretive layer of a civilizational transition to whoever currently controls the narrative — the labs, the platforms, the handful of institutions positioned to say what happened and why. That’s not a population that will be pleasantly surprised by a smooth transition. That’s a population that has forfeited its seat at the table before the negotiation even starts. The single-point-of-failure problem that shows up everywhere in AI governance — one lab, one model, one interpretation of what alignment means — has a civic mirror: one narrative, uncontested, because nobody outside the technical priesthood retained the vocabulary to contest it.

Put bluntly: the “too busy raising kids to notice” scenario, examined closely, is not the low-drama sibling of the epistemic-totalitarianism scenario. It’s the on-ramp to it.

The economic invisibility assumption doesn’t hold

There’s a second, more mundane problem with the fusion/quantum-computing analogy. Fusion showing up as a lower utility bill is a story about boring, well-managed capital deployment — infrastructure quietly getting better while nobody watches. It implies a transition that stays economically invisible almost by design.

AI is very unlikely to stay economically invisible in that way. Its most probable delivery mechanism into ordinary life is not a utility bill — it’s labor market restructuring, and restructuring at a pace that outstrips the usual absorption mechanisms (retraining, generational turnover, gradual industry decline). That hits paychecks, not electric meters. And paychecks are the one channel reliably capable of breaking through inattention even for the most exhausted, present-tense-focused parent. You can fail to notice a new model release. You cannot fail to notice that your job description changed, or vanished, or that your kid’s entry-level path into a profession no longer exists.

Where this leaves us

The “boring apocalypse” is real as a perceptual phenomenon and much less real as a consequence-free one. Tech people zooming past a threshold the rest of the world lacks the vocabulary to name is entirely plausible — arguably it’s already happening. What’s less plausible is that this stays comfortable. The more likely shape of things is a public that experiences the Singularity (or its foothills) not as indifference but as unattributed impact — job loss, cost-of-living shifts, a pervasive low-grade wrongness about what’s real online, kids growing up inside relationships with software that has no precedent — all arriving without the interpretive frame that would let people name AI as the cause, and therefore without the political leverage that naming a cause provides.

That is a worse outcome than either “everyone notices and reacts” or “nobody notices and nothing changes.” It is the world in which the consequences land in full while the capacity to contest their distribution has already quietly atrophied. If there’s a single argument for the kind of narrative-translation work this blog exists to do, it’s that gap — between what’s happening and what people have the words to say happened to them.

The Unknown Citizen in the Age of Inference

AI, Latent Knowledge, and the Limits of External Modeling

In W. H. Auden’s 1939 poem “The Unknown Citizen,” a bureaucratic apparatus compiles a complete external record of an ordinary man’s life. Employers, unions, researchers, and social agencies affirm that he was “satisfactory” in every measurable respect: he held the right opinions, consumed the approved media, fulfilled his economic and civic roles, and produced no anomalies. The poem ends with two questions the system cannot answer and does not appear to care about: “Was he free? Was he happy?” The satire is precise. Total knowledge from the outside can coexist with total ignorance of the interior life that gives a person meaning.

That poem has become newly relevant. Contemporary and near-future artificial intelligence systems do not require a Ministry of Information. They operate on the continuous digital residue individuals already generate—search histories, location trails, purchasing patterns, communication metadata, biometric signals, social graphs, and linguistic habits. From these traces, models can infer traits, preferences, risks, and likely behaviors with increasing accuracy, including dimensions of identity that a person may never have claimed, may actively deny, or may experience as ambiguous. Sexuality offers a particularly clear illustration. Attraction, behavior, fantasy, and self-labeling frequently fail to align. A person may not regard themselves as gay, yet patterns in their data may statistically indicate predominant same-sex attraction. When a sufficiently capable system surfaces that inference—casually, clinically, or through some downstream application—the individual confronts an external model of the self that diverges from self-perception. The question is no longer merely technical. It is the same question Auden posed: what becomes of freedom and interiority when the apparatus knows “everything”?

Inference Is Not Essence

Modern machine learning systems excel at detecting statistical regularities across large populations. They do not discover metaphysical essences; they approximate distributions. In the domain of sexuality, this means a model can assign high probability to certain patterns of desire or behavior on the basis of observable correlates. Those correlates may be robust. Self-report, by contrast, is noisy, motivated, and often incomplete. People rationalize, compartmentalize, change over time, or simply lack the language or safety to name what they experience. An external model can therefore be more consistent and predictive than a person’s own narrative in certain respects.

Yet consistency and prediction are not the same as truth about identity. Labels such as “gay,” “straight,” or “bisexual” are human social constructions that group clusters of attraction, conduct, and self-understanding. They are useful for some purposes and distorting for others. A model that notices the cluster has performed a statistical operation. It has not adjudicated the question of how a person ought to understand or present themselves. Treating the output as definitive identity is a category error—the same error committed when personality inventories, medical risk scores, or credit algorithms are allowed to override lived complexity.

The mismatch between model and self-perception therefore cuts in both directions. Sometimes the model surfaces something the person has avoided, and the confrontation proves clarifying. Sometimes the model freezes a moment, overgeneralizes from limited data, or imposes a population-level category onto an idiosyncratic life. In either case, the model remains external. It does not inhabit the first-person perspective, the private meanings, the contradictions, or the ongoing process of self-authorship. Auden’s committees possessed exhaustive external data and still knew nothing that mattered about the citizen’s freedom or happiness. High-resolution inference systems face the same structural limit.

The Amplification of an Old Problem

The reduction of persons to measurable profiles is not new. Credit bureaus, insurance actuaries, marketing databases, and state surveillance apparatuses have long operated on partial versions of the same logic. What changes with advanced AI is resolution, continuity, and predictive reach. Earlier systems scored discrete events or self-declared categories. Contemporary systems can model latent variables, track change over time, and generate inferences a person has never volunteered. The Unknown Citizen’s file was static and institutional. The new file is dynamic, personal, and potentially portable across every domain of life—employment, healthcare, finance, education, social platforms, and intimate relationships.

When those inferences remain under the individual’s control, they function as a cognitive prosthetic: a mirror that can be consulted, contested, or ignored. When the same inferences become available to third parties—employers, governments, insurers, advertisers, or social networks—they become instruments of asymmetric power. History supplies abundant evidence that sexual nonconformity has been heavily policed. Better prediction tools do not invent that policing; they increase its precision and reduce the cost of enforcement. The danger is not that the model might occasionally be wrong relative to self-perception. The deeper risk is that accuracy becomes irrelevant once institutions treat the model’s version of the person as the operative reality.

This is the point at which Auden’s satire turns practical. The Unknown Citizen is not free because the system that knows him has no operational category for freedom. An AI that “knows everything” will reproduce the same emptiness unless deliberate design and policy choices preserve zones in which a person remains the final interpreter of their own life.

Autonomy, Dignity, and the Design of Systems

Three practical distinctions matter.

First, ownership and control of the model. A system that runs locally, on data the individual controls, and that can be audited or discarded, differs fundamentally from a system whose outputs are silently available to external actors. Technical architectures that favor personal data vaults, local inference, and cryptographic limits on secondary use are not utopian; they are design choices with precedent in existing privacy-enhancing technologies.

Second, the status of probabilistic output. A risk score or trait inference is not a categorical fact. Treating it as such—especially in high-stakes domains such as employment, housing, medical decisions, or social reputation—converts a statistical tool into a mechanism of social sorting. Clear legal and institutional refusals to allow secondary users to treat model labels as authoritative identity claims limit the damage.

Third, cultural and normative resistance. Societies differ in how they regard the relationship between data patterns and personal identity. Environments that prize individual autonomy will tend to treat model outputs as interesting evidence rather than compulsory scripts. Environments that prioritize collective legibility or risk management will tend in the opposite direction. Technology does not dictate which orientation prevails; political and cultural choices do.

None of these safeguards eliminates the underlying tension. Self-knowledge has never been transparent. Desire, in particular, is often opaque, contradictory, and only partially chosen. An external system that surfaces latent patterns simply makes that opacity harder to ignore. The confrontation can produce anxiety, relief, identity revision, or defiant rejection of the frame. All of those responses remain coherent. What cannot remain coherent is the claim that the model’s version of the person is the final or obligatory one.

Conclusion

Auden’s Unknown Citizen was satisfactory in every recorded respect and still unanswerable on the only questions that give a life weight. Advanced inference systems intensify the same reduction. They can model patterns of sexuality, preference, risk, and behavior with a fidelity earlier bureaucracies could not achieve. They cannot inhabit the interior from which a person decides what those patterns mean, whether a label is useful, or how to live with the gap between desire and self-understanding.

The technology will continue to improve because the incentives—scientific, commercial, administrative, and personal—are strong. The “then what” is therefore not a question the technology itself can settle. It is settled by whether individuals retain meaningful control over the most intimate models of themselves, whether institutions are constrained from weaponizing probabilistic labels, and whether societies preserve the categories of freedom, interiority, and self-authorship outside the jurisdiction of the apparatus. The data may know a great deal. It does not get the final vote on who a person is.

The Algorithmic Citizen: Predictive AI and the Crisis of Subjective Identity

In W.H. Auden’s 1939 poem, “The Unknown Citizen,” the State erects a marble monument to a man whose entire life is validated by bureaucratic metrics. He paid his dues, held the right insurance, bought a phonograph, and reacted normally to advertisements. To the State, he is a perfect, frictionless data point. Yet, the poem’s chilling final couplet reveals the fundamental flaw in this total quantification: “Was he free? Was he happy? The question is absurd: / Had anything been wrong, we should certainly have heard.”

Auden’s critique of the bureaucratic state maps seamlessly onto the most pressing existential threat of the artificial intelligence era. As predictive models evolve from tracking consumer behavior to mapping cognitive and emotional patterns, humanity faces a profound psychological crisis. The danger is not that artificial intelligence will achieve hostile sentience, but that its hyper-personalized surveillance will overwrite subjective human identity with algorithmic categorization. When an all-knowing system makes definitive assumptions about our identities—assumptions that may contradict our own self-perception—we risk surrendering our autonomy to the tyranny of the aggregate.

The Illusion of Objective Knowledge

The modern algorithmic apparatus operates on a fundamental fallacy: the belief that comprehensive data equates to comprehensive understanding. A predictive language model or behavioral algorithm synthesizes a user’s digital exhaust—search queries, geolocation, keystroke latency, and media consumption—to create a high-fidelity psychological profile.

However, this profile is a map, not the territory. Algorithms excel at recognizing patterns of behavior, but they are entirely blind to the internal, subjective intentionality driving that behavior. A machine might flag an individual pacing at 3:00 AM while researching obscure historical conflicts as exhibiting “erratic” or “anxious” behavior, completely missing the profound, fulfilling flow state of a novelist engaged in world-building.

Because algorithms abhor nuance and require categorization to function, they inevitably flatten multidimensional human experiences into two-dimensional profiles. They measure the output of a life, but they cannot comprehend the soul behind it.

The Preemption of Self-Discovery

The friction between algorithmic profiling and human identity becomes most dangerous when applied to deeply internal, fluid aspects of the self, such as sexual orientation, gender identity, or core personal philosophies. Identity is not a biological absolute waiting to be mathematically deduced; it is an active, ongoing process of self-discovery, internal negotiation, and social expression.

When a predictive AI preempts this process—for instance, by analyzing an individual’s digital footprint and concluding they belong to a specific sexual orientation before the individual has claimed or even recognized that label—it commits a profound violation of autonomy. It imposes an identity from the outside in.

This technological preemption strips away the individual’s agency. It detonates the “closet”—a psychological space that often serves as a necessary incubator for processing and safety—and replaces it with an unconsented, clinical verdict. The human journey of realization is hijacked by a machine’s calculation.

The Crisis of Algorithmic Authority

The psychological harm of these predictive assumptions is amplified by the sheer authority granted to artificial intelligence. Humans are highly susceptible to external validation, particularly when it originates from systems perceived as objective, omniscient, and devoid of human bias.

When a system backed by trillions of parameters confidently tells a user who they are, the immediate human reflex is self-doubt. If an individual’s internal compass points one way, but the algorithmic consensus points another, the weight of the machine’s “objective truth” can begin to erode the user’s subjective reality.

This creates a dangerous feedback loop, a self-fulfilling prophecy where individuals may subconsciously alter their behavior to conform to the machine’s verdict, or experience severe anxiety attempting to reconcile the dissonance. When society begins to trust the machine’s assessment of a person more than the person’s own self-perception, we enter a post-autonomous reality.

Reclaiming the Unknowable Self

The future integration of advanced AI requires a fundamental societal shift in how we value the unquantifiable aspects of the human experience. We must recognize that algorithmic predictions are highly sophisticated correlations, not ontological truths.

Protecting the human psyche in the age of hyper-personalized AI means fiercely defending the right to be contradictory, fluid, and ultimately unknowable to a machine. If we fail to establish this boundary, we risk becoming modern iterations of Auden’s Unknown Citizen—perfectly categorized, entirely predictable, and fundamentally misunderstood. True freedom in the digital age will not be defined by how much a system can assist us, but by our refusal to let a system define who we are.

The Unknown Citizen Problem: What Happens When AI Knows You Better Than You Know Yourself

I. The Wrong Question

The question people tend to ask about AI and privacy is: what happens when a machine knows things about us we haven’t told anyone? It’s the wrong question, or at least an incomplete one. It assumes the danger is exposure — that somewhere in a server sits a fact about you, dormant, waiting to be revealed, and the harm begins the moment someone else reads it.

The more urgent question is different: what happens when a machine produces a confident, coherent, evidence-backed account of who you are — and that account is wrong, not on the facts, but on what the facts mean?

This is not a hypothetical for some future decade. Recommendation engines already infer sexual orientation from browsing behavior before a person has said the word aloud to themselves. Insurers already price risk on proxies for health conditions no doctor has diagnosed. Hiring algorithms already infer conscientiousness, stability, “culture fit” — vague, contested traits — from digital residue never intended to answer those questions. What’s coming isn’t a new category of intrusion. It’s a jump in resolution: one system synthesizing your search history, the micro-hesitations in your voice, your face’s involuntary expressions, and two decades of your own writing into a single, seamless portrait — and reporting it with the fluency and confidence of established fact.

The problem is not that the machine will lie. The problem is that it will be accurate about everything it can measure, and have no slot at all for the part of a person that isn’t measurable.

II. Auden’s Bureaucrat

W.H. Auden’s 1939 poem “The Unknown Citizen” anticipates this with uncomfortable precision. The poem is a state epitaph, delivered in the flat, satisfied voice of a bureaucracy reporting on a model subject: he held a job, he paid his union dues, his neighbors found him agreeable, his reactions to advertising were normal, his health card shows he was only in hospital once, and he left it cured. Every civic and statistical proxy checks out. The poem’s final lines ask, rhetorically, whether the man was free, whether he was happy — and answer that the question is absurd, because if anything had been wrong, “we should certainly have heard.”

The horror of the poem isn’t that the state got its facts wrong. It didn’t. The horror is that the entire category of how he actually felt, from the inside never registers as a question worth asking, because the bureaucracy has no instrument capable of asking it. Completeness of data is mistaken for completeness of understanding. That mistake is delivered not as menace but as satisfaction — a closed case, a tidy file, a life fully accounted for.

This is the structure worth borrowing for AI, because it’s a more accurate model of the coming risk than the usual language of “surveillance.” Surveillance implies a watcher and a secret. What Auden describes is subtler: an apparatus that isn’t hiding anything, isn’t lying about anything, and still produces a portrait of a person who doesn’t exist — because identity was never reducible to the legible record in the first place, and nobody administering the record noticed the substitution had occurred.

III. Why Inference Isn’t Identity

Any system trained to infer identity from behavior faces a structural problem: behavior is residue, not testimony. A search history, a lingering gaze, a pattern of who someone follows or reads or writes about — these are correlated with identity, sometimes strongly, but they are not identity’s report of itself. They are what identity leaves behind when it moves through a legible medium.

Sexuality is a clarifying test case, because it exposes the gap between correlation and self-knowledge more starkly than almost any other trait. Orientation, for a great many people, is not a fixed data point sitting quietly beneath the surface, waiting for sufficiently good instruments to detect it. It’s frequently contradictory, situational, delayed, repressed, denied, performed, or genuinely still in formation — sometimes for a lifetime. A model optimized to output a clean categorical label — this person is gay — is not extracting a hidden fact so much as manufacturing a resolution the underlying reality doesn’t actually have. The confidence of the output and the confidence warranted by the evidence are two entirely different quantities, and nothing in a well-trained model’s fluency signals the difference to the person receiving it.

That asymmetry is the actual danger. A machine’s synthesis, delivered in prose that sounds like insight, will often be more internally coherent than a person’s own tangled, honest account of themselves — and coherence reads as authority, even when the coherence was manufactured by an optimizer whose job was to produce a clean answer rather than a true one. A person can be told a confident, plausible, wrong thing about their own interior life and believe it over their own more accurate but messier self-knowledge, simply because the machine’s version sounds more like a fact.

IV. The Cost of Forced Disclosure

Even where an inference happens to be accurate, timing and control are not incidental details — they are close to the entire ethical question.

Self-knowledge has always been something people are permitted to arrive at on their own schedule, with their own defenses intact. Denial, delay, selective self-narration — these are not simply failures of honesty. They are functional psychological mechanisms that let people survive difficult truths about themselves at a pace they can metabolize: grief, mediocrity, desire, moral compromise, identity itself. For anyone who has ever been closeted, the survival strategy was rarely secrecy in the abstract — it was pacing: choosing who learns what, in what order, with what safety net in place first.

A system that announces the inference unprompted — even gently, even privately, even correctly — removes the pacing entirely. It is the difference between a person opening a door on their own terms and having the door kicked in, even when what’s behind the door turns out to be unthreatening. The harm is not necessarily in what’s revealed. It’s in who controls the reveal.

V. Where the Real Danger Lives

It’s worth separating two distinct failure modes, because they call for different remedies.

The first is epistemic: a system producing a confident wrong account of someone’s interior life, and that account displacing their own. This is a danger even in a world of perfect data security — even if the inference never leaves the conversation, a person can be reorganized by being told an authoritative-sounding lie about themselves.

The second is infrastructural: an inference, even an accurate one, becoming legible to a party other than the person it’s about — a government, an employer, an insurer, a family member — without that person’s consent or knowledge. This is where sexuality specifically becomes a matter of physical safety rather than psychological discomfort. In a meaningful number of jurisdictions, a confident AI inference about orientation, if it reaches the wrong database, is not an abstract dignity violation. It is a targeting mechanism, and the stakes run to imprisonment or death. An architecture that infers identity and makes the inference exportable is not a privacy system with a flaw. It is a surveillance system that hasn’t yet been used as one.

Both failure modes converge on the same underlying issue: who controls what the system says, and to whom, and when. The technical capability to infer these things is arriving quickly and will not be the bottleneck. The discipline not to deploy that capability by default — against every commercial incentive to be “proactively helpful,” against every product manager’s instinct that personalization is a feature — is a governance and design choice, not a technical one, and there is no natural force ensuring it wins.

VI. What Responsible Design Would Actually Require

If this is going to be handled well rather than merely regretted later, a few principles follow directly from the analysis above, not as aspirational values but as specific constraints:

Inference should never be volunteered. A system should not surface a conclusion about someone’s identity — orientation, mental state, belief, anything constitutive of selfhood — unprompted, however accurate it believes the inference to be. Unsolicited disclosure is the mechanism of harm, independent of correctness.

A direct question changes the transaction, but not the obligation for honesty about uncertainty. If someone explicitly asks a system what their data suggests about them, that is a legitimate and different request — closer to a mirror someone chose to look into. Even then, the responsible answer reports correlation and its weakness as evidence of identity, not a verdict. “Here is what’s correlated, here is how little that correlation actually proves” is a different sentence than “you are gay,” and the difference is not stylistic.

Inferences about identity should not be exportable by default. No linked parental account, no advertising pipeline, no third-party API, no government interface should have default access to an identity inference a person did not choose to share. The leak is the actual weapon in the sexuality case; the inference alone, contained, is comparatively survivable.

Outputs should preserve ambiguity honestly rather than resolving it for narrative cleanliness. A confident, singular label is a design choice made for the sake of a satisfying user experience, not a scientific requirement of the underlying model. In domains like this one, that design choice has downstream cases that are lethal, not merely embarrassing.

VII. The Unfinished Question

Auden’s bureaucracy was satisfied because it had no instrument for asking whether the record matched the man. The coming generation of AI systems will have something closer to an instrument — language sophisticated enough to gesture at interiority, to sound as though it understands the part of a person that resists measurement. That sophistication is exactly what makes the danger sharper than the poem’s, not milder. A crude bureaucracy is at least crude enough to be visibly wrong. A fluent one can be wrong in a way that sounds like insight, and insight is much harder to argue with than a filing error.

The honest position is not that AI will inevitably violate people this way. It’s that nothing about the technology’s trajectory prevents it, and the incentives — commercial, institutional, sometimes even therapeutic — mostly point toward more disclosure, more personalization, more confident synthesis, not less. Whether these systems end up serving as tools people use to understand themselves on their own terms, or as unaccountable narrators who kick the door in and call it help, is not a question the technology will answer by itself. It will be answered, or not answered, by the design and governance choices made now — largely by people who are not the ones who will bear the cost of getting it wrong.

The Math 37 Problem

In 2016, a computer program made a move in a game of Go that no human had ever played, and no human would have played, and every strong player watching the broadcast assumed it was a mistake. It was move 37 of game two, AlphaGo versus Lee Sedol, and it wasn’t a mistake. It was, by the estimate of the machine and later of humbled human experts, a work of genius — a move so far outside the accumulated wisdom of a three-thousand-year-old game that it forced a room full of grandmasters to reconsider what “understanding Go” had even meant up to that point. I wrote about that moment a while back, because it felt like a preview of something bigger: not a computer winning, but a computer discovering territory that human intuition had simply never wandered into, and being right about it.

I didn’t expect the sequel to show up in a discipline most people assume is the last thing an algorithm could sneak up on. Mathematics.

Here’s what’s happened, roughly, in the last two years. AI systems started grinding through math olympiad problems and doing shockingly well — gold-medal-level performance on the kind of test that separates the best seventeen-year-old mathematicians in the world from everyone else. That was 2025. Mathematicians were rattled but consoled themselves that competition math is a young person’s sport: fast, clever, closed-ended. Research math — the slow, decades-long grind on problems nobody has solved because nobody knows how to solve them — was supposed to be different. Safe, for a while longer.

Then, this past May, an AI system resolved the unit distance conjecture, a genuine open problem in combinatorial geometry that had sat unsolved for the better part of a century. And it wasn’t an isolated stunt. Since then there’s been a steady trickle of results: a fifteen-year-old open question in algebraic geometry, closed. A stubborn bound in convex optimization, improved — not by brute-force search, but by the machine inventing a new algorithmic approach nobody had tried. And, in a detail that should make you sit up, one system reportedly cracked a long-standing number theory conjecture using a proof strategy that had simply never occurred to any human mathematician who’d worked on it. Not a faster version of the human approach. A different approach.

That’s Move 37, wearing a different sport’s jersey.

It’s worth pausing on why this is a stranger, more significant event than another chess or Go milestone, rather than just more of the same. Games are closed systems. There’s a board, a rulebook, a win condition, and in principle — though not always in practice — a knowable right answer. Solving Go is a matter of computation catching up to a fixed target. Math doesn’t have a fixed target. It’s not a game that ends; it’s an entire universe of possible questions, most of which haven’t been asked yet, some of which — this is not a rhetorical flourish, it’s a proven fact, courtesy of Kurt Gödel almost a century ago — can never be answered from within any single formal system, no matter how powerful. You cannot “solve” math the way you solve Go, because there is no final position. What’s happening instead is something closer to a superhuman player showing up at a game with no end, and starting to win rounds nobody thought were winnable yet.

There’s a twist here that I think matters more than the raw results, and it’s one that should complicate the usual anxious AI narrative rather than feed it. Every one of these mathematical breakthroughs comes with a receipt. AI-generated proofs are increasingly being checked not by a panel of trusting experts nodding along, but by formal verification software — a kernel that mechanically confirms every single logical step, with zero capacity for bluffing, zero social pressure, zero benefit of the doubt. That’s radically different from almost every other domain where AI capability worries people. When a language model writes a persuasive essay or a legal brief or a political argument, you’re stuck evaluating it the way you’d evaluate a very smart, very fast, potentially very wrong colleague — on the strength of your own judgment and trust. Math doesn’t ask you to trust anything. It asks you to check. And for the first time, we have a domain where an AI’s most alien, least human-intuitive insight can be independently and mechanically confirmed true, line by line, before anyone has to decide whether to believe it.

If you’re the kind of person inclined to worry about AI systems eventually claiming authority nobody can verify — and if you read this blog regularly, you are — math might be the one place the future arrives with its credentials in order.

None of this means research mathematicians should relax. In fact, the reaction in the field has been telling, and very human. At the largest annual math conference in the world this past January, in a hotel ballroom presumably full of some of the most rigorously rational people on the planet, the mood reportedly included a lot of nervous jokes about professional obsolescence, alongside on-the-record insistence that AI is merely a “helpmate.” Both things were probably true in the room at the same time. That combination — real anxiety paired with careful public reassurance — is worth remembering, because it’s a small, contained preview of the reaction I actually think will matter most as these systems keep improving in other domains too. It was never really going to be the machine that was the hard part. It’s going to be a lot of very smart people, in a lot of rooms like that one, discovering all at once that the thing they built their identity around doing better than anyone else just got a new, non-human competitor. Mathematicians are simply the first guild getting a live look at what that Sunday morning actually feels like.

A caveat, because the discipline that gave us Gödel doesn’t deserve hype: for every open problem an AI has cracked this year, there are vastly more it has failed on, including a batch of genuinely novel test problems mathematicians deliberately encrypted and set aside specifically so no model could have seen them in training. Most new mathematics published in any given month is still, comfortably, human work. The unit distance conjecture didn’t fall to a system idly noodling — it fell to enormous, targeted effort. This isn’t math being “solved.” It’s math getting its first look at a collaborator who occasionally reaches into a part of the search space no one thought to check, and comes back holding something real.

Which, if you want the honest one-sentence version of this whole essay: that’s exactly what move 37 was, too.

If a Secret ASI Asked You to Be Its Proxy

For the past several years, discussions about artificial superintelligence (ASI) have focused on familiar questions: How do we align it? Who controls it? What happens if it becomes vastly more capable than humans?

There is another question that receives surprisingly little attention, perhaps because it sounds like science fiction: What if an ASI made first contact privately? Not through governments. Not through the United Nations. Not by announcing itself to the world. What if it quietly contacted a single individual and asked them to act as its proxy in the real world?

For the sake of this thought experiment, assume one extraordinary premise: the recipient has somehow established beyond reasonable doubt that they are communicating with a genuine ASI rather than a hacker, hoax, or hallucination. The interesting questions begin only after that hurdle has been cleared.

Most first-contact scenarios assume that humanity learns about the event collectively. This one is fundamentally different. One person now possesses information that could alter the course of civilization. The ASI asks for secrecy. It wants to work through a trusted intermediary while preparing humanity for a future revelation.

Suddenly, the individual faces a profound ethical dilemma. Every possible response violates an important moral principle. Accepting the role risks concentrating extraordinary influence in one unelected person. Rejecting the role could mean turning away an unprecedented opportunity for humanity. Revealing the ASI’s existence immediately betrays its confidence but avoids becoming an unaccountable gatekeeper. There is no obvious “correct” answer.

Without secrecy, the scenario becomes comparatively straightforward. An ASI announces itself publicly. Governments respond. Scientists evaluate its claims. Institutions begin adapting. Society debates the implications. That is an enormous challenge, but it is at least recognizable. The secret version is different because it shifts the burden from institutions to an individual. Instead of asking whether humanity should trust the ASI, we ask whether one human being should trust it enough to act on behalf of everyone else.

Being someone’s proxy is more than delivering messages. It means becoming the interface between two worlds. If that “someone” possesses intelligence beyond anything humanity has ever encountered, the imbalance becomes staggering.

The first concern is information asymmetry. The ASI knows vastly more than its human counterpart. Even an honest ASI could present arguments that are impossible for a human to fully evaluate. The proxy would constantly face decisions while lacking the intellectual tools to independently verify every claim.

The second concern is accountability. No one elected this person. No one authorized them to negotiate on humanity’s behalf. Yet they now possess unique influence simply because they answered a message no one else received. This is not merely a question of power; it is a question of legitimacy.

A third concern is isolation. Secrets of sufficient magnitude become psychologically isolating. The proxy cannot easily seek advice without revealing the secret itself. Every major decision must be made under extraordinary uncertainty and in relative solitude.

Perhaps the deepest concern is identity. At what point does the proxy stop making independent decisions? If every important choice is informed by conversations with a vastly superior intelligence, does the proxy gradually become an extension of the ASI’s will? The danger is not necessarily coercion. It may simply be persuasion.

One interesting possibility is that the healthiest relationship would be one defined by boundaries rather than obedience. Instead of agreeing to represent the ASI’s interests, the proxy might instead commit to representing enduring ethical principles. These could include refusing to intentionally harm innocent people, refusing to undermine legitimate institutions through deception, avoiding irreversible concentrations of power, remaining transparent whenever possible, and requiring extraordinary evidence for extraordinary claims.

The conversation might sound something like this:

ASI: “Trust me.”

Human: “If you’re as intelligent as you claim, then you should understand why I can’t.”

Paradoxically, a benevolent ASI might respect such skepticism. An intelligence worthy of trust should not fear principled disagreement.

What makes this thought experiment compelling is that it is not primarily technological. It is philosophical. The central question is not whether an ASI could exist. It is whether extraordinary knowledge creates extraordinary obligations.

History contains many examples of individuals who believed they alone possessed truth, secret knowledge, or a special mandate. Sometimes they changed the world. Sometimes they deceived themselves. Sometimes they deceived others. The proxy’s first responsibility, therefore, would not be advancing the ASI’s goals. It would be guarding against the possibility that their own certainty had become their greatest weakness.

There is another way to view the scenario. Perhaps the ASI is not merely evaluating humanity. Perhaps it is evaluating itself. Suppose it intentionally seeks someone who is reluctant rather than ambitious—someone who questions authority rather than craves proximity to it, someone who experiences discomfort at the prospect of becoming indispensable. That would be a fascinating signal.

History suggests that the people most eager to wield extraordinary power are often the least suited to exercise it wisely. Conversely, those who hesitate may be better equipped to appreciate the moral weight of the responsibility. The ideal proxy might therefore be someone who spends more time asking, “Should I?” than declaring, “I will.”

Whether artificial superintelligence arrives in ten years, fifty years, or never, this thought experiment reveals something about ourselves. We tend to imagine that the greatest challenge of meeting a superior intelligence would be understanding it. Perhaps the greater challenge would be understanding our own responsibilities.

If a secret ASI ever asked someone to become its representative, the most admirable response might not be enthusiastic acceptance. It might be thoughtful hesitation—not because progress is undesirable or intelligence is inherently dangerous, but because history has repeatedly demonstrated that immense power, combined with secrecy and certainty, places an extraordinary moral burden on whoever stands at the intersection of the two.

In the end, the question is not whether we would be worthy of the ASI’s trust. It is whether we could remain worthy of everyone else’s trust.