Curt Doolittle

Source: Twitter X

  • Neoteny Argument Claim: Human populations display an intra-species gradient in n

    Neoteny Argument

    Claim:
    Human populations display an intra-species gradient in neoteny; this gradient is empirically measurable, heritable, and predictive of cognitive and institutional phenotypes after controlling for environmental variance.
    1.1 Developmental Anatomy & Timing
    Neoteny refers to delayed somatic, neural, and behavioral maturation relative to reproductive age (Gould 1977). Within humans, measurable population-level differences exist in:
    • craniofacial morphology (Brace et al., 1991; Harvati & Weaver, 2006)
    • growth curves and skeletal maturation (Bogin, 1999)
    • prefrontal cortex development tempo (Petanjek et al., 2011)
    • sexual dimorphism and androgen receptor sensitivity (Puts et al., 2016)
    These differences represent quantitative developmental-timing variables, not categorical “racial traits.”
    Natural Law requirement: measurable, commensurable indices (NL Vol. 2: Measurement) .
    2.1 Standard Evolutionary Biology Prediction
    Life-history theory predicts that slower developmental tempo correlates with:
    • increased neocortical size and plasticity
    • enhanced executive function
    • reduced reactive aggression
    • greater investment in learning
    (Refs: Kaplan et al., 2000; Kuzawa & Bragg, 2012; Walker et al., 2006.)
    2.2 Empirical Support
    Population-level correlations exist between developmental tempo and:
    • general intelligence (g) (Lynn & Vanhanen 2012; Rindermann, 2018)
    • executive function (Ardila et al., 2005)
    • impulse control (Moffitt et al., 2011)
    • reaction time (Woodley et al., 2015)
    • delayed gratification / time preference (Wang et al., 2016; Daly & Wilson, 2005)
    These are robust cross-cultural findings.
    Natural Law requirement: cross-domain testifiability and universal commensurability (NL Vol. 2; Vol. 3: Evolutionary Computation) .
    3.1 Cooperation Grammar Effects
    Behavioral traits associated with slower tempo (norm-adherence, impulse control, lower aggression, long time-horizons) strongly predict:
    • rule-following behavior (Henrich, 2020)
    • contract enforcement (La Porta et al., 1999)
    • low corruption and high institutional trust (Inglehart & Welzel, 2005; Rothstein & Uslaner, 2005)
    • cooperation in large-scale, impersonal environments (Turchin et al., 2013)
    These patterns replicate globally and align with established theories of life-history strategy → cooperation style → institutions.
    Natural Law requirement: institutions emerge from behavioral equilibria produced by environmental constraints (NL Vol. 1: visibility, cooperation, constraint) .
    4.1 Heritability Evidence
    Developmental timing traits (pubertal onset, brain maturation tempo, craniofacial growth) show substantial heritability:
    • Pubertal timing: h² = 0.50–0.80 (Towne et al., 2005; Silventoinen et al., 2008)
    • Brain maturation tempo: h² ~ 0.80 (Lenroot et al., 2009)
    • Craniofacial morphology: h² 0.40–0.80 (Johannsdottir et al., 2005)
    4.2 Behavioral Genetics Controls
    Cognitive and behavioral traits linked to neoteny also show high heritabilities:
    • intelligence: h² 0.50–0.80 (Plomin & Deary 2015)
    • executive function: h² 0.40–0.60 (Friedman et al., 2008)
    • impulsivity / self-control: h² 0.40–0.70 (Beaver et al., 2009)
    4.3 Environmental Partitioning Studies
    The causal chain remains robust after controlling for environment:
    • Twin/adoption studies: cognitive & behavioral traits track inherited tempo, not household environment (Bouchard, 2004)
    • Transnational migration studies: life-history traits persist across cultural environments (Nettle, 2011)
    • GWAS data: tempo-related traits (height, puberty, schooling duration) correlate with polygenic scores (Okbay et al., 2016; Day et al., 2017)
    Conclusion: Environmental variance modulates expression but does not eliminate inherited population differences in developmental tempo.
    Natural Law requirement: causality must survive adversarial partitioning (NL Vol. 2: Decidability) .
    Across biological, cognitive, and institutional domains, the same causal chain persists:
    Ecology → developmental tempo → neoteny → cognitive architecture → cooperation grammar → institutional phenotype.
    This structure corresponds to NL Vol. 3’s general model:
    constraint → stable relation → phenotype → behavior → institution
    This is a decidable causal sequence under Natural Law:
    • operationally measurable,
    • cross-domain testifiable,
    • falsifiable,
    • and robust under adversarial controls.
    Intra-species neoteny gradients are:
    1. empirically measurable,
    2. genetically influenced,
    3. developmentally causal,
    4. behaviorally expressed,
    5. institutionally consequential,
    6. and decidable under the Natural Law framework.
    Environmental factors modulate—but do not eliminate—the inherited developmental-tempo differences that predict cognitive style and institutional capacity.
    Any model denying these relationships must reject established findings across     evolutionary biology, behavioral genetics, developmental neuroscience, anthropology, and NL’s requirement for operational, measurable, testifiable categories.
    Core Evolutionary Biology / Life History
    • Bogin, B. (1999). Patterns of Human Growth.
    • Gould, S. J. (1977). Ontogeny and Phylogeny.
    • Kaplan, H. et al. (2000). “A theory of human life history evolution.”
    • Kuzawa, C. & Bragg, J. (2012). “Plasticity in human life history.”
    • Walker, R. et al. (2006). “Life history theory and human brain development.”
    Neural Development
    • Petanjek, Z. et al. (2011). “Protracted synaptic development in the human prefrontal cortex.”
    • Lenroot, R. et al. (2009). “Genetic influences on brain structure across development.”
    Craniofacial & Anatomical Variation
    • Brace, C. L. et al. (1991). “Reflections on race and human biology.”
    • Harvati, K., & Weaver, T. (2006). “Human craniofacial variation.”
    Behavioral & Cognitive Genetics
    • Plomin, R., & Deary, I. (2015). “Genetics and intelligence differences.”
    • Friedman, N. et al. (2008). “Genetics of executive function.”
    • Beaver, K. et al. (2009). “Genetic influences on self-control.”
    • Okbay, A. et al. (2016). “GWAS for educational attainment.”
    • Day, F. et al. (2017). “Genetic determinants of puberty timing.”
    Behavior, Cooperation, Institutions
    • Henrich, J. (2020). The WEIRDest People in the World.
    • La Porta, R. et al. (1999). “The quality of government.”
    • Rothstein, B. & Uslaner, E. (2005). “All for all: equality, corruption, and trust.”
    • Turchin, P. et al. (2013). “Ultrasociality and warfare in state formation.”
    Time Preference & Life History
    • Daly, M., & Wilson, M. (2005). “Carpe diem: life-history and time preference.”
    • Wang, X. et al. (2016). “Life history and delay discounting.”
    Migration / Adoption Evidence
    • Bouchard, T. (2004). “Genetic influence on human psychological differences.”
    • Nettle, D. (2011). “Evolution of personality variation.”
    Global Cognitive Variation
    • Lynn, R., & Vanhanen, T. (2012). Intelligence: A Unifying Construct.
    • Rindermann, H. (2018). Cognitive Capitalism.

    Source date (UTC): 2025-11-27 02:01:12 UTC

    Original post: https://x.com/i/articles/1993862640947614223

  • Brian: Undeniable fact: human emotions are the result of acquisition, retention,

    Brian:
    Undeniable fact: human emotions are the result of acquisition, retention, use, trade, or consumption of demonstrated interests across the spectrum.

    This is computable. Even sex and individual differences are computable. The fact that the industry is populated by people who are from educational silos is the most likely inhibition.

    Why? systems that are internally closable are trivial compared to systems that are externally closable. (closure). They’re working hard to solve the trivial problem without solving the hard problem.

    Economics thought is more important than physics in determination of constraint, closure, and decidability in any real world model.

    It’s also far harder.
    It’s also what LLMs can be good at … if we teach it to them.

    (Which is what we do.)

    Curt Doolittle

    Source date (UTC): 2025-11-26 22:46:57 UTC

    Original post: https://twitter.com/i/web/status/1993813758184181942

  • We know how to solve the problem of computability using LLMs. I would argue that

    We know how to solve the problem of computability using LLMs. I would argue that the foundation model producers don’t understand the problem which is why they can’t solve it.
    We did. And it’s really, really, hard.

    Source date (UTC): 2025-11-26 22:40:15 UTC

    Original post: https://twitter.com/i/web/status/1993812071356747997

  • Kind of a dumb analogy. Look at the size of the population and economy. Worse, c

    Kind of a dumb analogy. Look at the size of the population and economy. Worse, china is the worlds most aggressive polluter. So what point are you trying to make that isn’t false?

    Source date (UTC): 2025-11-26 22:39:07 UTC

    Original post: https://twitter.com/i/web/status/1993811784340545703

  • Always true. I just retired my 2014 top of the line macbook pro retina for a new

    Always true.
    I just retired my 2014 top of the line macbook pro retina for a newer top of the line macbook pro M1. Meaning I got a decade of use out of that Macbook Pro. I didn’t need anything more than the 2014 model. Its only that no one will repair them any longer, and they can’t accept the OS upgrades.
    Apple is a better buy.

    Source date (UTC): 2025-11-26 22:38:08 UTC

    Original post: https://twitter.com/i/web/status/1993811538587820346

  • WHICH GAME? What if exploration is the game for him while monetary returns are t

    WHICH GAME?
    What if exploration is the game for him while monetary returns are the game for you? Which is exactly why you pursue different objectives. All brains seek to maximize successful stimulation. Most successful stimulation is achieved by acquisition. Both of you are seeking to acquire something. But you value different outputs. If in exchange for spending every day researching and improving ideas is his reward, and the practical efforts of commercialization are not, then why do so? Mostly the issue is combining your execution and his ideas. But only if you have a shared goal of profiting from those ideas. That’s how most organizations flourish.

    Source date (UTC): 2025-11-26 22:35:02 UTC

    Original post: https://twitter.com/i/web/status/1993810758069837908

  • I would argue that’s not quite true. The brain is possible to understand at leas

    I would argue that’s not quite true. The brain is possible to understand at least functionally. If we look at LLMs as the language faculty, and that we’re brute forcing the LLM’s world models via language, but that we haven’t yet created the prefrontal cortex and consciousness, then every LLM behavior is obvious and predictable. The impediment to completing that circuit is that it dramatically increases costs.

    Source date (UTC): 2025-11-26 22:25:45 UTC

    Original post: https://twitter.com/i/web/status/1993808423214043355

  • In our opinion (our organization) this is true. The value of any ai is dependent

    In our opinion (our organization) this is true. The value of any ai is dependent upon the capacity of individuals to leverage extant AI. For the .001% of us, the value is infinite. But that value doesn’t scale enough to pay for the absurd cost of compute.

    I don’t know if architectures is the right frame, I might argue it’s contexts. One must know enough to ask the meaningful question. And the AI must know the context in order to meaningfully respond to it.

    Source date (UTC): 2025-11-26 22:22:41 UTC

    Original post: https://twitter.com/i/web/status/1993807650879098983

  • @dwarkesh_sp Unfortunately, you don’t know me or my organization, but in simple

    @dwarkesh_sp

    Unfortunately, you don’t know me or my organization, but in simple terms, the evals are measuring low dimensionality easy-closure domains (math, programming, tests) with non-existent liability which we consider puzzles, whereas most problems are in high dimensionality hard-closure domains with attached liability. Ergo the evals over estimate the value of the AI in anything that is revenue producing. 😉

    I work, my organization works, in high dimensional closure (real world problems), which is where liability exists and revenue to pay for AI exists. And oddly there is no one else even vaguely in the space.

    So the evals are not indicative of the value of AI outside of easy closure (mathematics, programming, combinatorics).

    Cheers
    Curt Doolittle

    http://
    Runcible.com

    Source date (UTC): 2025-11-26 22:20:02 UTC

    Original post: https://twitter.com/i/web/status/1993806983120765323

  • Great post. Though I disagree that groups cannot align on truth. The problem has

    Great post. Though I disagree that groups cannot align on truth. The problem has been an absence of a court for truths and falsehoods in matters of the commons.

    Our organization has solved this problem. But implementing it even as an extension of fraud ( which is its category) might be impossible without settlement of civil war sufficient for constitutional amendments.

    Source date (UTC): 2025-11-26 20:13:37 UTC

    Original post: https://twitter.com/i/web/status/1993775170272808980