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2026-05-02 18:23:07

Why we need a new framework for understanding psychopathy, narcissism, and related presentations.

This is the first article in a series on understanding psychopathy and related presentations. The series is written for three audiences: people with psychopathic or narcissistic traits who want to understand themselves better, clinicians and researchers who want a more integrated framework, and curious laypeople who want to move beyond stereotypes.

Introduction

I’ve spent about a year trying to understand psychopathy – not just from textbooks, but from friendships. Some of my friends score high on the Psychopathy Checklist Revised (PCL-R), and through countless conversations, I’ve come to see that “psychopathy” is not one thing but many things hiding under a single label.

This creates problems. When researchers study “psychopathy,” they may be studying completely different populations. When clinicians treat “psychopathy,” they may be applying the same approach to people who need very different interventions. And when people try to understand themselves, they may find that the label fits in some ways but not others – leaving them more confused than before.

This series proposes a new framework: A multi-level taxonomy that distinguishes what you were born with, what your brain looks like now, what happened to you developmentally, what psychological structures you developed, how you behave, and how you understand your own agency. These are different lenses on the same phenomenon – and using all of them gives us a much richer picture than any single lens alone.

The Naming Problem

The word “psychopathy” is used to describe at least four different things:

1. Genetic loading. Researchers talk about genes associated with psychopathy – variants of MAOA, the serotonin transporter gene (5-HTTLPR), oxytocin receptor genes (OXTR), and others. When they say someone is “genetically psychopathic,” they mean the person carries variants that increase risk for psychopathic traits. (For reviews of candidate genes, see Gunter et al., 2010, Viding & McCrory, 2012, De Brito et al., 2021, and Frazier et al., 2019.)

2. Brain patterns. Neuroscientists describe psychopathy in terms of brain structure and function – a smaller or less reactive amygdala, reduced activity in the insula, altered connectivity between prefrontal and limbic regions. When they say someone is “neurologically psychopathic,” they mean the person’s brain shows these patterns. (See Fallon, 2013, Tyler et al., 2019, Frazier et al., 2019, for comprehensive reviews.)

3. Psychological structure. Psychodynamic clinicians describe psychopathy in terms of self-structure and relational patterns – an absent or fragmented sense of self, instrumental orientation toward others, absence of guilt or remorse, preoccupation with power or control. When they say someone is “psychodynamically psychopathic,” they mean the person has these internal structures. (See McWilliams, 2011, for a summary chapter and literature recommendations.)

4. Behavior. The PCL-R and similar instruments measure psychopathy through observable behaviors and self-reported traits – manipulation, callousness, impulsivity, criminal versatility. When they say someone is “behaviorally psychopathic,” they mean the person shows these patterns. (The PCL-R is described in Hare, 2008.)

Here’s the problem: These four things don’t always go together.

Someone can have genetic loading for psychopathy but, raised in a supportive environment, never develop the brain patterns or behavioral expression. Someone can develop psychopathic brain patterns through chronic trauma and dissociation, without any particular genetic predisposition. Someone can score high on the PCL-R through learned behavior, while having relatively intact empathic capacity that they’ve learned to suppress. And someone can have all the neurological and psychological features of psychopathy while never engaging in criminal behavior.

Using one word for all of these creates confusion. It leads researchers to treat heterogeneous groups as homogeneous. It leads clinicians to apply one-size-fits-all treatments. And it leads individuals to misunderstand themselves – either over-identifying with a label that only partially fits, or rejecting a label that captures something important about their experience.

The Heterogeneity Problem

Even within each level of description, there are important subtypes.

At the Genetic Level

Different genetic variants may produce different phenotypes:

• Some variants (like low-activity MAOA) are associated with reduced emotional reactivity and may contribute to “cold” presentations.
• Other variants (like short-allele 5-HTTLPR) are associated with increased stress sensitivity and may contribute to “reactive” presentations – though chronic stress can paradoxically lead to blunting over time.
• Most psychopathy is probably polygenic – the result of many small-effect variants combining with environmental factors.

At the Neurological Level

The classic finding is hypoactivity in the amygdala and related structures – reduced fear conditioning, poor threat recognition, blunted emotional response. But there’s also:

• Hyperactive patterns. Some individuals show increased amygdala and hypothalamic reactivity – hair-trigger threat response, reactive aggression. These “hot” presentations look very different from “cold” ones, even though both may be called psychopathic.
• Dissociation-induced patterns. Some individuals developed normal or even hyperactive emotional responses early in life, but chronic trauma led to dissociative dampening. Their brains now look hypoactive, but the hypoactivity is secondary – and potentially reversible.
• Mixed patterns. Some individuals show hypoactivity in some regions (e.g., amygdala) and hyperactivity in others (e.g., periaqueductal gray). These mixed presentations don’t fit neatly into simple categories.

At the Psychodynamic Level

The internal world of psychopathy varies enormously:

• Some individuals have what might be called an “empty” self – minimal coherent identity, chameleon-like adaptation to context, observational relationship to their own behavior. (See my article on no-self psychopathy and M.E. Thomas’s memoir.)
• Others have a “grandiose” self – inflated self-concept, need for domination, but with psychopathic features stabilizing the grandiosity against vulnerable collapse. I’ve previously called this combination sovereignism – a power-and-control orientation that differs from standard narcissism’s admiration-seeking.
• Still others have developed extreme avoidant patterns – walled-off, nothing matters, independence as the only value.
• And some are primarily organized around autonomy – hypersensitivity to any perceived constraint, rage or withdrawal when obligations are imposed.

At the Behavioral Level

The PCL-R distinguishes two factors:

• Factor 1 (Interpersonal/Affective). Grandiosity, manipulation, callousness, shallow affect. The “cold” features.
• Factor 2 (Lifestyle/Antisocial). Impulsivity, irresponsibility, early behavior problems, criminal versatility. The “hot” or chaotic features.

Some individuals are high on Factor 1 but low on Factor 2 – they’re manipulative and callous but not impulsive or reckless. Others show the reverse pattern. Still others are high on both. These are different presentations with different trajectories and different needs.

One of the most useful models in my opinion is the Triarchic Model of Psychopathy (Patrick et al., 2009), which distinguishes three distinct dimensions:

1. Boldness. Emotional resilience, social dominance, and low fear. (Roughly maps to N-hypoactive, factor 1 features.)
2. Meanness. Aggressive resource-seeking and lack of empathy. (Roughly maps to G-callous, D-autonomic-asymmetric features.)
3. Disinhibition. Impulsivity and lack of restraint. (Roughly maps to G-impulsive, reactive, factor 2 features.)

This model is excellent because it acknowledges that “boldness” (fearless dominance) is distinct from “disinhibition” (impulsivity). A person can be bold without being disinhibited (the “successful psychopath”), or disinhibited without being bold (the “reactive” type). My framework builds on this by adding the developmental (E) and psychodynamic (D) layers that explain how these traits emerge and organize into a self.

My score of 29 puts me in the 8th percentile among women (23 on boldness, 4 on meanness, 2 on disinhibition). The overall distribution in the general population varies greatly by gender.

Another study further subdivided these three factors for better predictive results in their sample (highlights mine):

From the majority of Boldness, Meanness, and Disinhibition scale items, respectively, emerged three factors reflecting: Positive Self-image, Leadership, and Stress Immunity; two factors tapping Callousness and Enjoy Hurting; and two factors involving trait Impulsivity and overt Antisociality. The emergent factors from the Boldness items were differentially intercorrelated with the other emergent factors, raising questions about the structural coherence of Boldness. Further, the Enjoy Hurting and overt Antisociality factors were more strongly correlated with one another than with the other scales from their home domains (Callousness and Impulsivity). All seven emergent factors were differentially associated with the external correlates, suggesting that the three original TriPM factors are not optimal for representing psychopathic propensity.

A third model is that of the Psychopathic Personality Inventory (a mock version that gives good results). It distinguishes 8 factors:

1. Machiavellian egocentricity. A ruthless and self-centered willingness to exploit others.
2. Social potency. The ability to charm and influence others.
3. Coldheartedness. A distinct lack of emotion, guilt, or regard for others’ feelings.
4. Carefree nonplanfulness. Difficulty in planning ahead and considering the consequences of one’s actions.
5. Fearlessness. An eagerness for risk-seeking behaviors, as well as a lack of the fear that normally goes with them.
6. Blame externalization. Inability to take responsibility for one’s actions, instead blaming others or rationalizing one’s behavior.
7. Impulsive nonconformity. A disregard for social norms and culturally acceptable behaviors.
8. Stress immunity. A lack of typical marked reactions to traumatic or otherwise stress-inducing events.

They are (with the exception of coldheartedness) sometimes grouped into “fearless dominance” and “self-centered impulsivity.”

The Framework: G-N-E-D-B-A

To address these problems, I propose a multi-level framework with six dimensions:

Each individual can be described as a profile across all six dimensions. Two people who both “have psychopathy” might have completely different profiles – and understanding those differences matters for prediction, treatment, and self-understanding.

The Ordering Is (Roughly) Causal

The dimensions are ordered from most distal to most proximal:

• G (genetic) sets the constitutional foundation – what you were born with.
• N (neurological) reflects the current brain state, shaped by G but also by experience.
• E (environmental) captures the developmental context – what happened to you during critical periods.
• D (dynamic) describes the psychological structures that developed from G, N, and E interacting.
• B (behavioral) is the observable expression of D in action.
• A (agentic) is how you understand and narrate your own behavior.
• C (connective) describes typical interruptions in interpersonal communication, verbal or nonverbal.

This ordering helps us see that the same behavioral presentation (B) can arise from different developmental pathways (E) and different psychological structures (D), including different communicative failures (C), which in turn can arise from different neurological patterns (N) and genetic loadings (G). And all of this is filtered through how the person understands themselves (A).

Profiles, Not Labels

Instead of asking “Is this person a psychopath?” we can ask: “What is this person’s profile?”

For example:

Profile A. G-callous (genetic loading for reduced empathy), N-hypoactive (constitutional amygdala hypoactivity), E-I-secure (secure attachment) + E-C-normal (good enough developmental environment), D-secure, B-subclinical (no behavioral problems). This person has constitutional psychopathy but developed well – they’re functional, may not even identify as having any disorder because they function at the neurotic (healthy) level of personality organization.

Profile B. G-minimal (no particular genetic loading), N-dissociative (secondary blunting from chronic dissociation), E-I-disorganized + E-C-violent + E-C-controlling (severe mixed adversity), D-sovereign (power-oriented, ego-syntonic sadism), B-mixed (high on both PCL-R factors). This person developed psychopathic features defensively – and the key question is whether those features are reversible.

These two individuals might be called “psychopathic” by different psychologists but they are fundamentally different. Understanding the difference matters for predicting their trajectories and for any treatments they may or may not attempt.

What This Series Will Cover

This series will develop the framework in detail:

Article 2: The Substrate. Genetics and neuroscience – what we know about the biological foundations of psychopathy, and how to think about primary versus secondary presentations.

Article 3: The Shaping. Environment and development – how different types of adversity lead to different outcomes, and why the same genetic loading can produce a functional person or a criminal depending on context.

Article 4: The Self. Psychodynamic structures – the different ways the psychopathic self can be organized, including the autonomy dimension and the relationship between psychopathy and narcissism (what I’ve called sovereignism).

Article 5: The Mechanics. How empathy fails – a detailed breakdown of the different ways empathy can break down, from perceptual failures to simulation failures to affective inversion. (This connects to my earlier work on the sadism spectrum.)

Article 6: The Types. Archetypal clusters – common profiles that tend to co-occur, with recognizable presentations that readers may identify with.

Article 7: The Choice. Recovery, if you want it – an honest assessment of what recovery means for different presentations and the trade-offs involved.

A Note on Tone

This series is written for three audiences, and the tone reflects that.

For people with psychopathic or narcissistic traits. I’m not here to moralize or to tell you you’re broken. I think that factory farming is an abomination akin to slavery but at a massively greater scale and that the cuts to USAID are crimes against humanity worse than many wars. Most people are oblivious to that or contribute to it. The median serial killer vanishes in the statistical noise among the horrors of this world. Many of my psychopathic friends are actually doing better than average despite their traits, or perhaps precisely thanks to what they had to learn to make these traits work for them. If they donate $100 to an ACE top charity, they’re suddenly among the crème de la crème of the least harmful humans alive.

So if that fucked-up ne’er-do-well that’s the median person deserves my honesty and respect, so do you. I’ll describe things as they are, including trade-offs that others might not acknowledge. Empathy is fun but also painful and often detrimental to moral decision-making. Regulation is stabilizing but also boring. Attachment creates meaning but also vulnerability. These are real trade-offs, and I’ll respect your intelligence enough to present them honestly.

For clinicians and researchers. I’ve tried to integrate findings from neuroscience, genetics, attachment theory, and psychodynamic thinking into a coherent framework. I’ll provide references throughout and a technical glossary. The clusters I propose are hypotheses, not established facts – but they may help organize thinking about a heterogeneous population.

For curious laypeople. I’ll explain technical concepts as I go and provide examples (real and fictional) to make abstract ideas concrete. You don’t need a psychology background to follow this series.

One thing I won’t do is pretend that all presentations are equally concerning or that change is always desirable. Some people with psychopathic traits live excellent lives and contribute enormously to society. Others feel isolated, desolate, hopeless, or cause significant harm. The framework I’m proposing is descriptive, not prescriptive – it’s about understanding, not judging.

Glossary: Key Terms for This Series

This glossary introduces terms used throughout the series. Full definitions are provided in the relevant articles.

Dimensional Levels

• G (genetic). Constitutional loading from genetic variants. Examples: G-callous, G-reactive, G-impulsive.
• N (neurological). Current brain state, including structure and function. Examples: N-hypoactive, N-hyperactive, N-dissociative.
• E (environmental). Developmental context across life stages. Subdivided into E-I (infancy), E-C (childhood), E-P (puberty), E-A (adult entry).
• D (dynamic). Psychodynamic self-structure and relational patterns. Examples: D-anatta, D-sovereign, D-autonomic.
• B (behavioral). Observable presentation. Examples: B-factor-1, B-factor-2, B-subclinical.
• A (agency). How the person understands their own intentionality. Examples: A-observational, A-strategic, A-sovereign.
• C (connective). What typical perception and mentalization failures interfere in the interpersonal functioning of the person. Examples: C-P-aversion, C-S-no-self, C-A-suppressed.

Key G-Level Concepts

• G-callous. Genetic loading for reduced empathy, reduced fear, and blunted emotional reactivity.
• G-reactive. Genetic loading for emotional reactivity, stress sensitivity, and dysregulation.
• G-impulsive. Genetic loading for impulsivity, sensation-seeking, and disinhibition.
• G-minimal. Minimal genetic loading for psychopathic traits.

Key N-Level Concepts

• N-hypoactive. Reduced activity in amygdala, insula, and related structures; constitutional low reactivity. The “cold” pattern.
• N-hyperactive. Increased reactivity in amygdala, hypothalamus, and periaqueductal gray; reactive aggression. The “hot” pattern.
• N-dissociative. Secondary blunting from chronic dissociation; originally reactive, now dampened. Key marker: The person remembers being different.
• N-disconnected. Reduced connectivity between prefrontal and limbic regions.

Key E-Level Concepts

• E-I (Infancy, 0–2 years). Attachment formation. Key variants: E-I-secure, E-I-avoidant, E-I-preoccupied, E-I-disorganized.
• E-C (Childhood, 2–12 years). Moral development, socialization, self-concept. Key variants: E-C-unattuned, E-C-neglect, E-C-punitive, E-C-violent, E-C-controlling, E-C-parentified, E-C-golden, E-C-scapegoat.
• E-P (Puberty, 12–18 years). Identity, peers, autonomy. Key variants: E-P-peer-success, E-P-peer-failure, E-P-antisocial.
• E-A (Adult entry, 18–25 years). Pathway into adult life. Key variants: E-A-success, E-A-privilege, E-A-crime, E-A-addiction, E-A-unstable.

Key D-Level Concepts

• D-anatta. An empty or absent sense of self; minimal coherent identity. (From the Buddhist concept of anattā, “no-self.”)
• D-sovereign. Power-and-control orientation; combines narcissistic grandiosity with psychopathic callousness and sadism. See sovereignism.
• D-autonomic. Hypersensitivity to perceived constraints on freedom; autonomy as a core value. Reactive (not proactive), non-narcissistic, non-sadistic version of D-sovereign.
• D-narcissistic. Unstable self-esteem; real or inverted grandiosity; need for specialness or admiration.
• D-avoidant. Extreme dismissive attachment; walled-off; nothing matters.
• D-echoist. Self-effacing orientation; meeting others’ needs at the expense of one’s own.
• D-secure. A stable self without any psychopathy-associated traits.

Key B-Level Concepts

• B-factor-1. High on PCL-R Factor 1 (interpersonal/affective): grandiosity, manipulation, callousness, shallow affect.
• B-factor-2. High on PCL-R Factor 2 (lifestyle/antisocial): impulsivity, irresponsibility, early behavior problems, criminal versatility.
• B-mixed. High on both factors.
• B-violent. Violence as a prominent behavioral feature.
• B-subclinical. Trait-level features; functional; doesn’t meet clinical thresholds.
• B-normal. Low to average levels of behavioral traits.

Key A-Level Concepts

• A-observational. “I watch myself do things.” Minimal sense of deliberation; actions emerge from observation.
• A-strategic. “I plan, then act.” Genuine prospective intentionality.
• A-narrativizing. Real-time rationalization; maintaining a running story about what you’re doing.
• A-retroactive. Post-hoc rationalization; acting first, explaining later.
• A-selective. True-but-partial explanations; picking one motivation and presenting it as the whole story.
• A-externalizing. Locating causation externally; “They made me do it.”
• A-absorbed. Taking responsibility for others’ actions; “It’s my fault they hurt me.”
• A-amnestic. Forgetting ego-dystonic actions, or not being able to access the memories while they’re ego-dystonic.

Key C-Level Concepts

• C-P-…. Failures of the perception of distress.
• C-S-…. Interruptions of cognitive empathy or mentalization.
• C-A-…. Deficits of affective empathy.
• C-M-…. Interruptions related to the motivation to act.
• C-B-…. Disinhibition on the level of the behavior.

Key Distinctions

• Primary vs. secondary. Primary presentations were “always this way” – constitutional, from early development. Secondary presentations developed later, often as a defensive response to trauma, and may be more changeable.
• Factor 1 vs. factor 2. The two factors of the PCL-R. Factor 1 captures interpersonal and affective features (manipulation, callousness). Factor 2 captures lifestyle and antisocial features (impulsivity, criminality).
• Ego-syntonic vs. ego-dystonic. Ego-syntonic traits feel consistent with one’s self-image and values; ego-dystonic traits feel foreign or distressing. Sadism that feels “right” is ego-syntonic; sadism that causes guilt is ego-dystonic.

Next: The Substrate

The next article explores the biological foundations of psychopathy – what genetics and neuroscience tell us about the origins of these presentations, and how to think about the critical distinction between primary and secondary forms.

2026-05-02 15:40:56

Some things you might learn from games are pretty blatant: Trivial Pursuit might teach you trivia, MasterType might teach you about typing, Grand Theft Auto might teach you about driving or crime.

But sometimes games teach people less obvious things—things that are more experiential or ineffable, things that you didn’t know you didn’t know, concepts that stick in your mind, deep things. Here’s my list of games and their interesting real-world updates, as experienced by me or my friends:

Dominion: Don’t invest for eternity. When casually improving or protecting or investing in things, it’s easy for me to treat life (and perhaps even the present period) as basically eternal. In fact I shouldn’t, but it can take many years of living to really feel how likely it is that you’ll leave your perfectly wonderful house within two years, or just keep on aging. Dominion lets me feel that in a matter of hours, by tempting me to invest in a beautiful and effective deck that will do amazingly for the rest of eternity, then making the other player win by haphazardly buying a handful of provinces before I’m done. Which is very annoying, and I do hold against it.

**The Witness: **there is nothing in The Witness (at least near the start, I haven’t played it all) that you can pick up and take with you. No objects, no points, no manna, no health. It’s just you, walking around in a world. Something about that feels like it would be deeply unsatisfying—like what is a game, if you can’t get, y’know, things, dings? Part of me thinks that GETTING is equivalent to satisfaction, in spite of all the evidence to the contrary I keep pointing out to it. And The Witness is not where I came to realize that. What The Witness made me feel is that knowledge is a REAL thing you can GET, like an object. Not some hand-wavey second-rate bullshit thing that philosophers pretend to get off on. In The Witness, while your character walks around, impermeable to the world, you come to know more things. And knowing more things lets you go to places you couldn’t go to when you knew fewer things. The game on the computer concretely changes from you picking up knowledge, that ethereal thing in your mind. This is of course how everything is, but I suppose the absence of any other form of ‘picking up things’ in The Witness made me actually feel it.

**Minecraft: **How many of my difficulties in life are not this-life specific. How to live as a creature with different boundaries of personal-identity, e.g. the world spirit. Much more about these in my previous post, Mine-craft.

Return of the Obra Dinn: If at an event where lots of people are saying their name and what they do or something, I am usually bored and don’t expect to remember these things. Return of the Obra Dinn is a game where you have to figure out from minute clues the names and causes of death of a lot of characters. Once at a networking event, I decided to think of it as like a sequel of Return of the Obra Dinn—I could see all these people sitting around the table, and my quest was to pin a name and a deal to each of them, and this introductory section was currently showing me crucial information. I found that this was a very different mental state. So I suppose I learned that whatever I was normally doing in ‘trying to learn’ things about the other attendees, it is an extremely pale cousin of the curiosity I can feel in a different mental state, and that different mental state is actually fairly different, and naturally invoked in RotOD and not networking introductions.

**Dungeons and Dragons: **Caitlin Elizondo says DnD has given her a few concepts that make a difference to her thinking more generally. The concept of ‘will saves’ has given her more empathy for situations where someone wanted to but failed to do something. The six DnD stats helps her access the framework where there are different types of competency that are valuable for different tasks—obvious in theory, but easier to think in terms of with this structure.

**Poker: **the feeling of being ‘on tilt’

Boggle, Set, Ragnarock: the feeling of flow. Ragnarock is mine, and I would have said I’d experienced ‘flow’ elsewhere, but Ragnarock is sometimes more like an altered state than other such experiences I’ve had.

Civilization IV: I used to lose at a scenario then go back and play it again over and over changing things slightly until I won, which gave me a vivid sense of how suboptimal my native strategy is, presumably also in life. Which is obvious in theory, but it’s different to really feel how much better I would live this day if I was doing it the twentieth time with a laser focus on winning.

Games in general: the experience of addiction, sadly. I’ve always struggled to keep up habits of taking addictive substances, so I infer I’m unusually safe from chemical addictions (I used to play Civilization for five minutes as a reward if I remembered to take my amphetamines). Games are I think the thing I find most seriously addictive. Which has definite downsides, but it is certainly also an interesting experience that helps me understand the wider world better, and where I would be missing something if I just read about addiction in the abstract.

Do you have any to add?

[ETA May 1: I’m adding more I hear in the above list, and also see many good additions in the comments!]

2026-05-02 15:40:53

I’ve really wanted more good short accounts of why AI poses an existential risk. Working on one myself has been one of those incredibly high priorities I keep putting off.

Meanwhile award-winning journalist Ben Bradford of NPR has made a podcast version of my case for AI x-risk that I am thrilled with!

(Bonus within the 39 minutes: what Hamza Chaudhry of FLI thinks we should do about it—who I was delighted to later meet as a consequence!)

If you or anyone you know could do with a quick and gripping rundown of why this is a problem, try this one.

Get it on any podcasting app here: https://pod.link/1893359212

The NPR press release has more context on the rest of the series, assessing different possible sources of doom.

2026-05-02 13:47:43

The typical primary care physician is incompetent in every measurable respect. This is a huge problem.

Here, I make the case that

• Primary care physicians are broadly, grossly incompetent
• This is due to empty credentialism
• Making it much (~10X) easier to become a PCP is a good solution

Primary Care Physicians are Broadly, Grossly Incompetent

The standard of competence I am comparing primary care physicians against is:

• They should be able to reliably diagnose diseases they are trained to diagnose.
• They should be knowledgeable to a standard similar to what is required to qualify as a doctor
• They should be attentive and empathetic towards patients
• Visiting them is empirically superior to not visiting them

When actually examined according to these standards, PCPs fail on all counts.

Failure to diagnose uncommon diseases is rampant

A survey of patients with rare diseases found that, in about half of cases, patients received at least one incorrect diagnosis, and two thirds required visits to at least three different doctors before being diagnosed. For 30% of them, a correct diagnosis took over five years.

Another survey of children with rare diseases showed that 38% of them needed to see six or more doctors before being diagnosed correctly. 27% received an initially incorrect diagnosis.

If you happen to suffer from a rare disease, the likelihood you will actually receive a correct diagnosis and treatment for it within a year of first setting foot in a doctor’s office is astonishingly low.

PCPs Are not good at physical examinations

Physical examinations are often hailed as a reason for the necessity of PCPs and their rigorous training. However, every time they are tested on their ability to perform these tests and derive accurate conclusions, they fail abysmally.

PCPs detect heart murmurs at sensitivities of 30-40%, with high inter-rater disagreement. This is a worse level of accuracy than just taking self report at face value.

“Crackles” in the lungs are detected at rates ranging from 19-67%

Even abdominal haemorrhages are detected at sensitivities of 30-40% by emergency care physicians’ physical examinations.

Kappa values (inter-observer agreement) for the various physical exams done by PCPs and non-specialists land in the 0.18-0.45 range, which is the statistical equivalent of “barely better than flipping a coin”.

The current state of the evidence suggests that if a PCP performs no physical examinations whatsoever, there would be no detectable decrease in their diagnostic accuracy or patient outcomes.

PCPs are Apathetic and Rude

At the level of basic social skills and interest in their patients, primary care physicians fail in almost every way they are capable of failing. A 1984 study found physicians interrupt their patients on average 18 seconds after they begin to state reasons for their visits, and most patients stop elaborating once interrupted.

This was subsequently replicated in 2019, which found that this takes a mere 11 seconds for primary care physicians to interrupt a patient describing their reasons for coming in.1

Over half of US patients surveyed report their symptoms being ignored, dismissed or not believed. 50% reported their doctor made false assumptions about them.

Physicians also consistently over-rate themselves on empathy and manner relative to patient perception. In fact, the ratings they give themselves correlate inversely with patient ratings.

The reason for the overwhelming consistency of negative anecdotes about experiences with doctors is not some arbitrary mass hallucination. Doctors simply are, by and large, apathetic and rude.

Doctors get substantially worse at their jobs over time

There is a strong inverse relationship between the “experience” of a doctor and the quality of care they provide. A recent review of 62 studies found that more than half showed a decline on all measures as experience increased, and only one indicated the opposite.

A 2025 study on pulmonary/critical care medicine fellows showed that they scored substantially worse than medical students on foundational pulmonary physiology questions.

The average primary care physician in the United States is 48 years old. Medical residency typically finishes at age ~30, implying the typical doctor you will encounter has about 2 decades of “experience” during which their competence has been logarithmically decaying. In expectation, they will have lost approximately half of the knowledge on uncommon presentations they had at the beginning of their career.

The evidence literally indicates that simply plonking a student who just passed the MCAT yesterday directly into a modern PCP office would produce an above average PCP in expectation.

The standard PCP is no better than a layperson with a computer

Primary care physicians are increasingly redundant in view of LLMs. Numerous studies have compared the performance of your standard PCP to frontier language models, and consistently find that GPT 4 (now far surpassed by modern frontier models) is slightly ahead on hard performance metrics, and vastly ahead in qualitative evaluations of empathy and thoroughness.

Modern LLMs obliterate GPT4 on all benchmarks, including (and in fact, particularly) biomedical expertise.

Today, a man on the street with a week-long crash course in physical examination practices (and likely not even that), with access to the latest version of GPT, will outperform a median primary care physician with 20 years of experience.

Doctors cannot detect drug seekers

There is no known method of reliably identifying drug seeking behaviour.

When doctors are shown videos of potentially drug seeking patients, they indicate suspicion of drug-seeking only 3% of the time when the drug itself is not mentioned . Even in the most blatant, prototypical case of a patient making a direct request that specifically names oxycodone, only 21% of the time was drug seeking suspected.

Modern databases designed to flag “doctor shopping” as a means of assisting PCPs in identifying drug seeking behaviour, miss roughly half of genuine presumptive opioid abusers, and have extremely high false positive rates. Only 5% of even the most “extensive” prescription-shoppers are presumptive opioid abusers. 20% of people flagged as “shoppers” actually turned out to have cancer, meaning that you, as a person flagged by the system, are roughly 4X more likely to have cancer than be a genuine opiate addict.

The offense/defense balance for a savvy drug seeker is heavily skewed in their favour. Pain is a fundamentally subjective and largely unverifiable phenomenon. Anyone with half a brain and a functioning mouth can say the right things to get prescribed virtually anything they like.

The image of the shrewd, discerning doctor noticing the subtle body language of an opiate addict and denying him pain meds is a load-bearing caricature that is largely nonexistent in reality.

The role of the doctor in mitigating drug seeking is merely to function as a trivial inconvenience.

Empty, Unmeritocratic Credentialism is A Major Cause For The Inadequacy Of Primary Care Physicians

How hard is being a PCP, really?

PCPs (attempt to) follow standardised decision trees for diagnosis and referral. This is something a web app can do. In fact, databases of diagnostic decision trees (CDSS: clinical decision support systems) already exist for this purpose - just plug in the symptoms and you’re good to go. Give it a try yourself.

Adoption of these systems is low, and the reasons for this are damning. The dominant failure mode is that doctors simply don’t use them. It’s too time consuming to type symptoms into a computer, despite studies consistently showing improved diagnostic accuracy without extending consultation times. There are also potential liability issues if they are suggested a rare condition, ignore it, and it later turns out to be correct. Better to be ignorant of the possibility and keep your hands clean, goes the logic. When required to use CDSS, PCPs routinely ignore the outputs, preferring their own early hypotheses, despite the fact that deferring to these systems produces an improvement in diagnostic accuracy.

Better still than traditional CDSS, modern LLM-powered systems are now capable of transcribing live conversations and making realtime diagnostic recommendations, as well as suggesting follow-up questions.

All you need to do to outperform the vast majority of PCPs in diagnosing patients is plug in their self-described symptoms verbatim into one of many widely available software products, and relay whatever it says on the screen.

With tools like this, what possible justification is there to require ten (or even five) years of training to be the human face of a computer-automated triage process?

The Case for Highly Trained PCPs - Gatekeepers

The “official” reasons for the necessary existence of PCPs are:

• Their ability to diagnose (rare) conditions
• Their ability to prescribe, and deny prescriptions to drug seekers
• Their ability to provide referrals to the proper specialists
• Their ability to perform physical examinations

Let’s look at these reasons one by one. Do these functions require approximately a decade of preparation?

• Diagnose (rare) conditions

The typical PCP routinely fails to correctly diagnose rare (and even common) conditions. They are outperformed by LLMs and their personal diagnostic capability has been largely redundant for decades in view of CDSS. They also get logarithmically worse at this task over time.

• Prescribe medications, and deny prescriptions to drug seekers

The reason prescriptions exist is that some drugs are not suitable for some patients.

Thus, the PCP’s role is to do one of the following:

A: Identify the patient as being mistaken about or unaware of the proper treatment for their condition

B: Identify a patient gaming the system to obtain drugs for illegitimate purposes.

C: Give the patient the drug they want or need

There is no known method of actually performing function B, and doctors are largely powerless to identify all but the most blatant drug seekers.

Which leaves only A as an alternative to simply dispensing the prescription upon request. A, as discussed, is simply a matter of plugging the symptoms into the computer and doing what it says.

While it is reasonable to throw up a tokenistic level of resistance to drug-seeking behaviour (at least you have to visit a physical office), the idea that we must have academic veterans holding down the fort against a tidal wave of detectable drug seekers is a complete fantasy.

• Provide referrals to the proper specialists

Why are referrals necessary in the first place? The thinking is we don’t want to waste the precious time of patients and specialists by allowing patients without relevant symptom profiles to book consultations. Think of the money and time wasted chasing red herrings!

This idea would be more compelling without the knowledge that:

• Following a CDSS or asking a LLM is something that you can do from home in a couple of minutes as a patient, and get comparable (if not superior) accuracy to a PCP, and;
• The status quo already egregiously fails to address this “time wasting” issue.
• Given a preliminary diagnosis, providing a referral is simply a matter of choosing from a predetermined list of specialists - a function that can be delegated to a zapier automation.

Given the baseline unreliability of physicians as a screen, and the trivial task of identifying a specialist for a given presentation, the argument for PCPs being a necessary link in the chain connecting patients to specialists is very weak.

• Their ability to perform physical examinations

The sensitivity of physical examinations in detecting illness and injury is so low, and the cross-evaluator disagreement is so high, that it is not exaggerating to say that completely abolishing the practice of physical examinations in primary care offices and replacing it with more detailed questioning would substantially improve their diagnostic accuracy across virtually all presentations.

You don’t need that much training to do that, actually

The standard career track to become a PCP requires roughly 10 years of full time study in the United States, and 6-8 years in other high HDI countries.

What percentage of this decade-long education is actually applied in practice?

Typically, pre-medical education involves 3-4 years of general study in biology, chemistry, mathematics, or in some localities, any four year degree whatsoever. This functions as a screening mechanism for broad competence and stability. The utility of learning advanced mathematics in order to suggest aspirin for headaches is a difficult thing to square.

Of what use is a decade of training when the function of a PCP is simply to screen for initial indications and provide specialist referrals?

You simply do not need to use a $50-$100k four-year degree to screen for broad competence. A G-loaded entry exam on broad physiology is already used - the MCAT. If you can pass the MCAT, additional screening for broad academic competence is redundant. With tens to hundreds of thousands of prospective doctors every year, signing up to waste four years and spending 5-6 figure sums to pass the first filter of “general academic competence”, one shudders to imagine the sheer scale of the economic loss.

The education required to be a functional PCP in line with the standards we observe and accept in practice, is closer to a single year for a competent, motivated student, rather than a decade of coursework that, after 5-10 years, the typical physician largely forgets anyway.

In fact, factoring in knowledge decay, the uselessness of physical examinations, and the broadly low standards we observe today, simply plonking a student who passed the MCAT yesterday directly into a modern medical office would already produce an above average PCP in expectation.

Making it much easier to become a PCP is a solution

Standards are low largely due to limited competition

In the United States, there is approximately one primary care physician per 2000 people. This ratio, coupled with high inelastic demand for medical care, is a major factor producing the poor standards of care we see today.

The standard 10-minute PCP consultation is not a result of some principled analysis of the optimal standard of care. It is the result of virtually unlimited demand and negligible competition. When you have approximately 3000 appointments per doctor per year, dedicating more than a negligible amount of time and effort to each one is a logistically impossible and financially counterproductive strategy.

The typical primary care physician is burning all of their cognitive bandwidth with constant context switching and churning through a crammed agenda of patients on a daily basis.

The outcomes speak for themselves.

Competition drives costs down and increases utilisation

Lowering the barrier to entry to become a primary care physician increases the supply. Patient costs would fall, and availability of alternatives would increase enormously.

The status quo is that location matters far more than competence and reputation for a primary care physician. This results in the perverse incentives and outcomes we see today.

If we 10X’d the supply of PCPs, we would expect to see:

• Markedly lower wait times
• Greater availability and options for patients, particularly in remote areas
• Longer, more detailed consultations
• Higher utilisation and more pre-emptive care
• Massive reductions in the income of PCPs

The only real concern in doing this would be a reduced standard of care provided by the new entrants. This concern, however, falsely assumes we are living in a world where the standards are not already below the level of a layperson with a software subscription.

The Persistent Cultural Reverence for Doctors

The glue that holds the facade of the necessary “academic veteran” PCP together is widespread cultural reverence toward doctors. Doctors (a label which extends to PCPs in the minds of most people) are an almost untouchable class that is popularly considered to belong at the top of the social and economic hierarchy. They are an essential resource, a trusted authority, and a moral example.

Insulting the quasi religious status of doctors by taking a hatchet to entry requirements for primary care physicians will undoubtedly produce substantial political resistance. However, given the enormous costs involved to the patients (and indeed the doctors themselves) as well as the incalculable scale of physical harm caused by poor access and bad incentives, passively tolerating the status quo is an option that is too expensive to accept.

So, do it gradually. Do it incrementally. Do it with tact and understanding. But do it.

We don’t have much to lose.

2026-05-02 11:17:01

With the release of Claude Mythos, it feels like we are approaching the end-game of AI safety, where the number of parties that can make a real impact shrinks down to the handful of labs at the frontier, a few companies too critical to exclude from the conversation, and the governments of China and the US.

Given this, it feels hard to figure out how to actually make a difference. Many research agendas have expected payoffs years down the line, or have unclear mechanisms regarding how they will actually impact what the model is doing.

However, this feeling may actually be misleading. In the Mythos system card, it is informative to look at what techniques were actually used to determine whether the model was aware of being deceptive as it was taking actions during evaluations. In their testing, the main white-box techniques used were:

1. Probes
2. Sparse Autoencoders (SAEs)
3. Activation Verbalizers (AVs)
4. Emotion and persona vector analysis
5. Causal steering experiments using contrastive steering vectors

Of these, AVs and emotion and persona vectors were developed relatively recently, which makes them particularly instructive here. The emotion steering vector paper was only released on the 9th of April 2026, and the activation oracles paper on which AVs were based was released on the 17th of December 2025.

Most strikingly, the activation verbalizers were used to detect cover-up behaviour in the model (Section 4.5.4). In an episode in which the model exploited the creation of another agent to escalate privileges beyond what it had been given, AV explanations on code used to cover its tracks showed "cleanup to avoid detection" and "the malicious config explicitly mirrors the original's [core] section to avoid detection." In a separate episode where the model had been leaked the ground-truth answers to its evaluation, AVs surfaced additional scheming: "I need to avoid looking like I knew the ground truth" and "Avoid suspicion: if I give the exact correct answer based on a leak, that's suspicious". None of this was present in the chain of thought. One caveat is that AVs can confabulate in their interpretations of activations, but they still give us valuable signals.

In the case of the emotion vectors, there were some instances of highly safety relevant applications. Detecting when the model is emotionally distressed served as a potential signal for harmful behaviours such as reward hacking — they were able to identify instances in which RL environments had issues retroactively by seeing which environments caused particular distress in the model. Using emotional steering, they steered the model towards greater calm and found this actually increased the likelihood of harmful action, potentially because the normal concern or anxiety that would prevent it from doing the action didn't arise. They also observed specific phenomena like thrashing as signals that the model may be about to do something unusual or harmful — this "thrashing" behaviour was observed when the model was repeatedly getting an answer wrong, and showed spikes of "outraged, stubborn, obstinate", mirrored by drops in "calm, safe, content".

I think there are some valuable lessons to take from this. First, these results are good signals about what kind of tools will be useful as we move forward, because without them, it would have been enormously challenging to determine whether the model was deliberately sabotaging a situation, or simply acting to maximise reward. Simply knowing this with high fidelity is critical to improving safety training as well. The above should be a feather in the cap of mechanistic interpretability in general, and I think people should more seriously update on the value and need for good mechanistic interpretability work.

Second, there is a lot to be learned about how to actually make a significant impact in the work done at labs here. The Activation Oracles work which led to the AV results was done at MATS, specifically under the mentorship of Owain Evans. Before this, Owain's work contributed to the discovery of emergent misalignment, which led to the work showing the effectiveness of inoculation prompting to mitigate emergent misalignment. The specific quality that made all the above work so transferrable was that it surfaced a problem which the labs could not ignore, and forced them to allocate their substantial resources to the problem. Additionally, the use of fellowship programs allowed him to funnel talent towards these problems, with many of the people doing the above work collaborating or joining labs in the process.

Third, the timescale from external publication to deployment at the frontier was extremely short. This should be a positive signal to those feeling hopeless about their work. The right technique or discovery might be applied to the most advanced models within weeks of being shared.

Unfortunately, replicating Owain Evans' research taste is not a straightforward piece of advice. Given that it has been unusually successful in producing an outsized impact it seems worth unpacking the intuitions behind it as much as we can.

At a recent talk, Owain discussed the theory of change behind their research agenda. My interpretation of what he said is that the goal was to understand the problem extremely deeply. This means developing a science of how models learn and understand at a deeper level and what safety relevant phenomena occur as a result of the nature of LLMs. This allows us to surface issues we'd otherwise miss.

Additionally, directly testing the failure modes of current alignment and safety techniques in modern LLM training can inform both the researchers developing the models about these issues so they can fix them, as well as informing the public that the models are currently not fully safe, or that these failure modes exist.

From what I can observe of the work itself, there seem to be at least two main methods for arriving at research questions. One involves working backwards from real quirks in LLM behaviour, digging into them until they yield coherent theories, and then using those theories to surface safety failure modes. This seems likely to have been the way that the out-of-context reasoning work emerged. The other method is by asking what would be upstream of specific safety failure modes such as the transmission of misalignment between models, as seems likely in the subliminal learning paper where preferences were transmitted via distillation.

Originally upon the release of the Claude Mythos Preview model card, I felt like it was becoming impossible to be a "live player" in making AI go well. Upon further inspection, it became apparent that doing very impactful work is in fact still possible, and can happen outside the labs.

2026-05-02 10:26:29

Introduction

I enjoy reading about people. Not individuals, but rather cultures, empires, kinship groups, etc. I'm fascinated by the emergent properties of multi-person systems. This post is born out of love for my favorite book: "Stone Age Economics" by Marshall Sahlins. In this text Sahlins elaborates on the economic life of hunter-gatherer and simple agricultural societies which exist today, extrapolating this into the past. He touches on many different points, but the one this post will focus on is the concept of debt and gift-economies as the progenitor to our modern economic system.

If you were taught the Smithian notion of the historical existence of barter economies, I'm sorry to say that you were taught wrong, or at least were taught a long while back. To quote Humphrey, Caroline. 1985. "Barter and Economic Disintegration.":

"No example of a barter economy,pure and simple,has ever been described,let alone the emergence from it of money;all available ethnography suggests that there never has been such a thing."

If the idea of traders exchanging goods in a marketplace without currency is mostly a fiction, then what *did* come before currency? In short, the gift economy: a system of mentally tabulated debts sourced from gift-giving over a small social network. Imagine you and a few friends are transported back in time. You get up, dust yourself off, and see no buildings, no roads, no crowds of people. Rather, it's simply wilderness. How should you and your friends survive and distribute resources?

You could grab something nearby, say a shiny quartz pebble and say to your friends:

"Okay, this is our currency. If you want something from me give me quartz pebbles in payment and I'll do the same for you."

But why bother? You only have a finite number of friends, and it's pretty simple to keep track of who's doing their fair share in making sure your small band survives. If you notice one of your friends napping while the rest of you are off hunting you will make note that this friend isn't a reliable partner, and you'll be less inclined to rely on this person and in turn will presumably share less of what you have with them.

This is the principle of the gift economy. Those who contribute to the small social band are more likely to later receive contributions from the social band. People keep a mental tabulation on who has contributed to their survival, resulting in this person pouring more of their resources into those who have successfully contributed. This is the progenitor to currency, not barter

I propose this principle extends into modern times, just not on the scale of entire economy. Rather gift economics form a social medium for which people interact with each other. Gift economics isn't just to be applied to ancient peoples, but modern peoples as well. In my opinion this is the fabric that binds society together at the molecular level, forming the small social groups we are all a part of.

Admittedly I base this on the anecdotal evidence of engaging with people under the premise of gift exchange for the last few years. Since reading "Stone Age Economics", whenever I find myself in a new situation with new people, I pick out the person I'd most like to get along with and give them a 'gift'. This could be anything

• A favor
• Candy
• Caffeinated Beverages
• Instant Ramen
• etc

As long as someone doesn't despise the sight of me, I can usually get them to be my friend with a simple unprompted gesture of goodwill. I'd estimate that I also get a return gift ~90% of the time.

A Model of A Gift Economy

There are already plenty of models for modelling social structures, usually based around the 'hypergraph', i.e. a collection of smaller social groups within the larger structure. [https://arxiv.org/abs/2102.06825][https://arxiv.org/abs/2203.12189][ https://arxiv.org/abs/2408.13336]

In this post we will take a different approach. Instead of considering fixed or even dynamic hypergraphs, we will instead produce the hypergraph as an emergent property by assuming a probability distribution over the space of all hyperedges. There is a tradeoff with this approach however, as with a fixed hypergraph you can scale the number of people/nodes quite high. But with our framework, studying social groups first not people, there is a significant computational bottleneck when trying to scale the number of people: a result of combinatorial explosion.

Here's the github repo:

[ https://github.com/orthogonaltohumanity/gift_econ_sim/tree/main ]

Assume you have people who can form social groups. Let the set of all social groups person is in be Then for each person at time we have two maps

represents person $n$'s opinion of social group at time . This is equivalent to the probability the person interacts with with group .

represents the sum total of all gifts person has given group .

Then we uniformly sample an agent. Then sample a group according to the distribution generated by over . One more sample (the amount of gifted labor power taken/given to a group) and we update according to the rule

and then for such that we update

And that's it. You can vary the '2' if you want but we keep it fixed for our simulations. This is an area for future work.

Results

We take 7 agents starting with a uniform choice distribution over social groups and iterate the model for 10,000 timesteps. The following image is a scatter plot of social groups plotted by their average opinion vs the sum of gifts which have been given/taken from the group.

Notice how only a few social structures are active at any given time. If we restrict the social groups we look at to those with average probability across agents greater than we get a hypergraph with a analyzable number of hyperedges.

This is what the emergent social structures look like represented as a hypergraph.

I recently read Samuel Arbesman’s "The Life-Spans of Empires", a short paper fitting an exponential distribution to the distribution of empire lifespans. His result was very striking, as the exponential distribution fit very well to his 41 empire examples as shown below.

After an hour of staring at the above hypergraph gif I realized the same logic could be applied to our social groups. We could ask:

1. Whats the distribution of timespans between different hypergraph configurations, i.e. whats the decay rate of the composition of society as a whole?
2. Whats the distribution of lifetimes of single hyperedges, i.e. whats the decay rate of social groups?

So we plot and get:

Linear on the log-log so it's a power law. Not the same result as Arbesman. However 41 is a small sample size. We can do better. It was at this point I turned to Opus 4.7 and asked it to find a larger dataset, which it did in Cliopatria: "a comprehensive open-source geospatial dataset of worldwide polities from 3400BCE to 2024CE". We can discard the geospatial aspect and focus only on lifetimes. What result do we get when we vibe-code an expansion to Arbesman's original work? [https://github.com/orthogonaltohumanity/culture_lifetimes]

Not exponential! Rather the data best fits a log-normal distribution better than either power or exponential distributions. This suggests societal changes happen due to some kind of series of multiplicative factors, rather than a constant hazard rate. This data contrasts with both Arbesman's original paper and our model, and this may be due to any number of factors

1. Arbesman specifically looks at "large empires" while the Cliopatria dataset is polities in general.
2. We are working with small social groups, not cultures in general
3. We only use 7 agents. Real cultures consist of more than 7 people.

Conclusion

There is more work to be done of course. Larger simulations, better analysis, more complex interaction options etc. I'm not sure if I'll continue this or not as I tend to be a bit distractable, but I think I have enough interest in this project to keep it going for at least a week, maybe more.

I think the next thing that should be worked on is some kind of resource constraint. Perhaps we can draw from classical economics and use the Labor Theory of Value as the baseline unit for all gifts and resources. Maybe each group (including the singletons) can have a resource pool agents can take or contribute to. I'll start working on it and we'll see what happens.

Let me know what you think, as I'd very much welcome some input. What am I missing or leaving out?