Calculator screen displaying the number 58008 with memory function buttons above.

Can You Teach AI to be a Dumbass?

Random Thoughts: We can debate until the cows come home on whether current models and systems can be scaled to create General AI. However it happens, I think we can all agree future models and techniques will probably still be derived from how the human brain functions and processes information. Can we build models that can develop logic and reasoning skills and be situationally aware to apply the right skills in the proper context? Probably. But can AI be creative? 🤷‍♂️

Head on a guillotine, my answer is no. Claude never had a childhood.

If I were to place a standard calculator, a bottle of Elmer’s glue, and a mechanical pencil in front of you and then asked, “what can these items be used for?” Most of you, and probably General AI would respond: 1) addition, subtraction, multiplication, etc., 2) an adhesive to bind objects together, and 3) a writing tool.

Is that the best you can come up with?

Me: 1) You type 5-8-0-0-8 into the calculator. 2) You pour the glue onto both your hands, spread it evenly, wait for it to dry, and then peel the glue off your skin. 3) You give your buddies an imaginary cooties vaccine.

Yes, I was that kid in class, and today I’m that kid at work. Knock on wood if you’re with me.

That is all.

Magic 101

Random thoughts of the day:

  1. FFS why would you put the delete button two microns away from the archive button!!!
  2. “Any sufficiently advanced technology is indistinguishable from magic” – Arthur C Clarke

In this context, the current set of AI tools are, relatively speaking, crude and rudimentary. Technologists talk about prompting and context windows, and tokens as if it were as exciting as the first season of LOST. CEOs are all in on AI because they see an opportunity to either increase 💰 or save 💰💰💰. Though I am of the belief that most CEOs couldn’t tell you how to connect to the company WiFi much less craft an AI strategy. But I reckon most people don’t see a ton of value in AI. Perhaps a good analogy is the BlackBerry or Palm Pilot. The only reason anyone carried one of these is because it was required for work or because it was a status symbol for pretentious aholes. See above, me 20 years ago. But the iPhone! There were lines straight up and down and around the corner and down the street at the Town Center when iPhones came out. It was magic.

Until Tim Cook or someone gets off their ass and builds a device or software that makes AI appear as though it were magic, no one is really going to be compelled to use it.

1,106 Days Later

Someone once wrote, “art is the stored honey of the human soul, gathered on the wings of misery”

Art provides self-expression and healing. An outlet to process emotions and confront thoughts. It allows individuals to explore different perspectives and connect with experiences beyond their own. It provides nourishment to the human spirit and inspires us by transforming personal experiences into universal experiences.

Honest Question: What happens to the human spirit when the primary source of art is produced by Artificially Intelligent Nano Anthropic Generative Pre-Trained Transforners?

Great art, great writing, great code, and great accomplishments require [human] suffering.

To that end, if you’ve ever worked with me then you’ve probably heard me yell the following:

“I know two sprints is not a lot of time, but you’ll figure it out. If it was easy, then [with all due respect 😁] they would have hired Accenture to do it.”

The Return of Renewables and ESG

Here’s my first prediction for 2026: Yeaup, it’s that time of year again, Turkey, holiday decorations, and prognostications.

I’ve done the math, mostly in my head, but sober, and my mental models suggest that the demand for power generation, transmission, and transformation is so ungodly huge, that renewables make the biggest comeback since the second half of Super Bowl LI. You look skeptical.

Ok, if you want to purchase a gas turbine, the lead time is one to seven years. And what do you suppose it costs to lease a gas turbine? Yeah I have no idea either, but if the stock price of GE Veranova is a leading indicator, biz-ness-is-a-boomin! But you know what’s relatively cheap, and can be constructed co-located next to your datacenter in ~18 months? Rhymes with polar. Now all you need is to buy a few power distribution transformers, oops, wait time on those is…you don’t want to know.

My point is that if you’re eating french fries, and a bottle of Heinz costs nine skillion dollars and a bottle of Hunt’s costs a nickel, you make do with what you got. (Note: Don’t you ever serve me Hunt’s. I will come at you like a spider monkey)

What the hell was I talking about? Oh yea, ESG is making a comeback because of Datacenters. Crazy right!? I think Alanis Morrisette wrote a song about this on her second album Jagged Little Pill, which, for my money, is one of the top ten albums of all time. Laugh at me if you want, but there’s not one bad song on that album.

That is all.

Too Big to Fail: The Sequel

Let’s start with the fact that I did not major in finance or economics. BUT, I am a student of history, and well, let’s just say that I’ve seen this movie before.

I’ve been trying to make sense of all of the AI financing that’s been happening over the past several weeks. If I’m being honest, the way some of these deals are being financed are a bit over my head. Do I think another financial crisis is coming? Ehhhh, no not really. I feel uneasy about the massive piles of debt the hyperscalers are accumulating, but I’m not panicked either. If there’s any good news, companies like Google (aka Alphabet), Microsoft, Facebook (aka META), and Amazon all have solid businesses with lots of room for growth, and that’s without future demand for AI.

To be clear, it’s not the size of debt these companies are taking on. Their bonds are all investment grade (see above, they make huge profits). It’s everyone else who’s taking on debt to buy these bonds. It’s also where this debt is showing up. I.e., pension plans and 401ks. If you’re a fixed income fund manager, I imagine you’d probably get fired if you told your boss you bout US Treasuries or anything that yields less than the 10 year bond.

“Ay, there’s the rub” - Marvin J. Hamlet, former Prince of Denmark

Investment-grade debt markets are the deepest part of the credit system. Private Credit with complex and obscure cash flows are also issuing debt to fund datacenters. I will go out on a limb and suggest that a non-trivial amount of this cash is tied to people’s retirement funds.

That’s the bad news. Here’s the good news, sort of: I’ve seen this movie before and IF it all goes south, and I’m not saying it will, but if if if it all goes south, the US government will have to issue a bailout. And if you thought the last bailout was huge…

Only Writing Can Save Our Souls

“Decidedly we shall not be safe if we forget the things of the mind. Indeed, if we want to save our souls, the mind must lead a more athletic life than it has ever done before, and must more passionately than ever practise and rejoice in art. For only through art can we cultivate annoyance with inessentials, powerful and exasperated reactions against ugliness, a ravenous appetite for beauty; and these are the true guardians of the soul.“

-Rebecca West, November 7, 1914

Conservation of Information

Ideas are just as real as the neurons they inhabit.  They leap from brain to brain.  The ideas have retained the properties of organisms.  And even though ideas, which have come out of us, are not made of nucleic acid, they have achieved more evolutionary change in the last 100 years than biological evolution and genes did in a billion years. – Jason Silva

Google Trends: Economic Leading Indicators

Don’t blame the messenger.

A new study by cruxanalytics.com analyzed Google Trends data across all 50 states, comparing searches between January and March 2024 to June and August 2025. Searches for “business loan” grew by 23% across the country, while “cash flow” searches grew 28%. And “going out of business” searches increased by 7% over the same time period, according to the report.

Reverse Engineering the Simulation

If we’re living in a simulation, what does the source code look like?

# -----------------------------
# CONSTANTS (fundamental)
# -----------------------------
c := speed_of_light
G := gravitational_constant
ħ := reduced_Planck_constant
kB := Boltzmann_constant
π := pi
ζ3 := zeta(3)
k := 0 # spatial curvature (flat ΛCDM, early era)
Λ := 0 # negligible in radiation era

# Radiation coefficients
α_rad := (π^2/15) * (kB^4 / (ħ^3 * c^5)) # energy density const: ργ = α_rad T^4
β_rad := (2*ζ3/π^2) * (kB^3 / (ħ^3 * c^3)) # number density const: nγ = β_rad T^3

# -----------------------------
# STATE VARIABLES (functions of t)
# -----------------------------
a(t) : scale_factor
H(t) : Hubble_parameter = (d/dt a)/a
T(t) : radiation_temperature
ργ(t) : photon_energy_density
nγ(t) : photon_number_density
fγ(ν,t) : photon_occupation_spectrum

# -----------------------------
# AXIOMS / DYNAMICS (radiation-dominated FRW)
# -----------------------------
Friedmann:
H(t)^2 = (8πG/3) * ργ(t) + (Λ/3) - k * c^2 / a(t)^2
Radiation_equations:
ργ(t) = α_rad * T(t)^4
nγ(t) = β_rad * T(t)^3
Adiabatic_expansion:
a(t) * T(t) = const # entropy per comoving vol ~ const
Planck_spectrum:
fγ(ν,t) = 1 / (exp[ (ħ ν) / (kB T(t)) ] - 1)

# -----------------------------
# INITIALIZATION (“Let there be light”)
# -----------------------------
LetThereBeLight(t0, T0):
a(t0) := a0 # choose gauge (often a0 = 1)
T(t0) := T0 # ultra-hot; photons in thermal equilibrium
ργ(t0) := α_rad * T0^4
nγ(t0) := β_rad * T0^3
fγ(ν,t0) := 1 / (exp[(ħν)/(kB T0)] - 1)
return state_at(t0)

# -----------------------------
# CLOSED-FORM EVOLUTION (radiation era)
# -----------------------------
SolveRadiationEra(t ≥ t0):
# From Friedmann with ργ ∝ a^-4 ⇒ a(t) ∝ t^(1/2)
a(t) = a0 * (t / t0)^(1/2)
T(t) = T0 * (a0 / a(t)) = T0 * (t0 / t)^(1/2)
ργ(t)= α_rad * T(t)^4 = ργ(t0) * (a0 / a(t))^4
nγ(t)= β_rad * T(t)^3 = nγ(t0) * (a0 / a(t))^3
fγ(ν,t) = 1 / (exp[(ħν)/(kB T(t))] - 1)
return state_path[t0 → t]

# -----------------------------
# TERMINATION MARKERS (milestones)
# -----------------------------
Milestone_Recombination:
# transparency when T(t_rec) ≈ 3000 K (kB T ~ 0.26–0.3 eV)
find t_rec such that T(t_rec) ≈ 3000 kelvin
output CMB := fγ(ν, t_rec) redshifted thereafter

Milestone_BBN:
# light nuclei synthesis (t ~ 1–10^3 s, T ~ 0.1–1 MeV)
when T(t) in [0.1, 1] MeV/kB:
run NUCLEAR_NETWORK(H(t), T(t)) to yield {^1H, ^2H, ^3He, ^4He, trace ^7Li}
# -------------------------------------------------
# COSMOLOGY & BARYONS (carry-over + minimal add-ons)
# -------------------------------------------------
Ω_b := baryon_density_parameter
Ω_m := matter_density_parameter
H0 := Hubble_constant_today
μ := mean_molecular_weight # ≈ 0.59 (ionized), ≈ 1.22 (neutral)
m_p := proton_mass
γ_ad := adiabatic_index = 5/3

ρ_crit(t) := 3 H(t)^2 / (8 π G)
ρ_b(t) := Ω_b ρ_crit(t) # mean baryon density

# -------------------------------------------------
# LINEAR → NONLINEAR COLLAPSE (seeds → halos)
# -------------------------------------------------
σ(M) : RMS fluctuation at mass scale M
δ_c := 1.686 # spherical collapse threshold
D(t) : linear growth factor

HaloMassFunction(M,t):
# Press–Schechter (schematic)
ν := δ_c / (σ(M) D(t))
f_PS(ν) := sqrt(2/π) ν exp(-ν^2/2)
n_halo(M,t) := (ρ_m(t)/M) f_PS(ν) |d ln σ^{-1}/dM|
return n_halo

# -------------------------------------------------
# GAS COOLING & JEANS COLLAPSE
# -------------------------------------------------
c_s(T) := sqrt(γ_ad kB T / (μ m_p)) # sound speed
λ_J(ρ,T) := c_s(T) * sqrt(π / (G ρ)) # Jeans length
M_J(ρ,T) := (4π/3) ρ (λ_J/2)^3 # Jeans mass
t_ff(ρ) := sqrt(3π / (32 G ρ)) # free-fall time

CoolingFunctions:
Λ_H2(T,Z) # H2 cooling (primordial, Z~0)
Λ_atomic(T,Z) # atomic cooling (Lyα, metals if Z>0)
Λ_total(T,Z) := Λ_H2 + Λ_atomic

CollapseCriterion(cloud):
return [ M_cloud > M_J(cloud.ρ, cloud.T) and t_cool := (3/2) n kB T / Λ_total < t_ff(cloud.ρ) ]

# -------------------------------------------------
# STAR FORMATION LAW (Schmidt-like)
# -------------------------------------------------
ε_* := star_formation_efficiency ∈ (0,1)
SFR(ρ_gas) := ε_* ρ_gas / t_ff(ρ_gas)

# -------------------------------------------------
# INITIAL MASS FUNCTION (IMF)
# -------------------------------------------------
# Two regimes: metal-free (Pop III; top-heavy) and enriched (Pop II; Salpeter/Kroupa-like)
IMF_PopIII(M) := K_III M^{-α_III} on [M_min^III, M_max^III] # α_III ≈ 1–1.5, M ≈ 10–300 M_sun
IMF_PopII(M) := K_II M^{-α_II } on [M_min^II , M_max^II ] # α_II ≈ 2.35, M ≈ 0.1–100 M_sun

SelectIMF(Z):
if Z < Z_crit then return IMF_PopIII else return IMF_PopII
Z_crit := 10^{-4} Z_sun # critical metallicity for IMF transition

# -------------------------------------------------
# STELLAR EVOLUTION & YIELD KERNELS
# -------------------------------------------------
τ_*(M,Z) # stellar lifetime
R_SN(M,Z) # explosion type: core-collapse SNII / PISN / direct collapse
E_SN(M,Z) # kinetic energy per SN (∼10^51 erg for SNII; higher for PISN)
y_He(M,Z), y_C(M,Z), y_O(M,Z) # ejected mass yields

# Population-integrated (per unit stellar mass formed):
Y_X(Z) := ∫ y_X(M,Z) IMF(M|Z) dM / ∫ M IMF(M|Z) dM for X ∈ {He,C,O}
η_SN(Z):= ∫ 1_{explodes}(M,Z) IMF(M|Z) dM / ∫ M IMF(M|Z) dM # SN per solar mass

# -------------------------------------------------
# NUCLEOSYNTHESIS SUMMARY (inside stars)
# -------------------------------------------------
# H → He (pp-chain / CNO), then He-burning:
# 3α: 3 × ^4He → ^12C + γ
# ^12C + ^4He → ^16O + γ
# Heavier α-captures in massive stars produce Ne, Mg, Si... (beyond this phase’s scope)

# -------------------------------------------------
# FEEDBACK & METAL ENRICHMENT OPERATORS
# -------------------------------------------------
MixingVolume(E_SN, ρ):
# Sedov–Taylor scaling (schematic): radius R_ST ∝ (E_SN / ρ)^{1/5} t^{2/5}
return V_mix ∝ (E_SN/ρ)^{3/5} t_mix^{6/5}

Enrich(gas_cell, ΔM_* formed at Z):
IMF := SelectIMF(Z)
ΔM_He := Y_He(Z) ΔM_*
ΔM_C := Y_C (Z) ΔM_*
ΔM_O := Y_O (Z) ΔM_*
gas_cell.Metals += {He:ΔM_He, C:ΔM_C, O:ΔM_O}
gas_cell.M_gas -= (locked_up := (1 - return_fraction(Z)) ΔM_*) # long-lived remnants
gas_cell.T += heating_from(η_SN(Z) ΔM_* E_SN_avg)
gas_cell.Z = (total_metals_mass / gas_cell.M_gas)
return gas_cell

# -------------------------------------------------
# POPULATION LOOP (cosmic time stepping)
# -------------------------------------------------
Phase2_Evolve(t1 → t2, grid_of_gas):
for t from t1 to t2:
for cell in grid_of_gas:
if CollapseCriterion(cell):
ΔM_* = SFR(cell.ρ) Δt
Zloc = cell.Z
cell = Enrich(cell, ΔM_* at Zloc)
if cell.Z rises above Z_crit:
cell.IMF_regime := "Pop II"
return state(t2)

# -------------------------------------------------
# MILESTONES (observables)
# -------------------------------------------------
HeliumBudget(t):
# He mass made by stars (on top of primordial Y_p ≈ 0.246)
ΔY_*(t) := ∑cells ∫_0^t Y_He(Z_cell(t')) SFR_cell(t') dt' / M_b,universe

CarbonOxygenBudget(t):
ρ_C(t) := ∑cells Metals_C(cell,t) / Volume
ρ_O(t) := ∑cells Metals_O(cell,t) / Volume

MetallicityPDF(t):
P(Z,t) from mass-weighted histogram over cells

# Simple reionization tracker (ionizing photon budget):
ξ_ion(Z) # ionizing photons per stellar baryon (depends on IMF; Pop III high)
f_esc # escape fraction of ionizing photons
n_H # hydrogen number density
t_rec # recombination timescale (clumping-dependent)

dQ_HII/dt = (f_esc ∑cells ξ_ion(Z_cell) SFR_cell / (ρ_b/m_p)) - Q_HII / t_rec
Milestone_Reionization when Q_HII → 1

# -------------------------------------------------
# INITIAL & BOUNDARY CONDITIONS FOR PHASE 2
# -------------------------------------------------
InitializePhase2(state_from_Phase1 at t ≈ 10^6–10^8 yrs):
# Neutral gas after recombination; tiny metal-free fluctuations
for cells: set Z := 0, T := T_IGM(t), ρ := ρ_b(t) × (1+δ)
choose ε_*, IMF_regimes, yield tables {y_He,y_C,y_O}, η_SN, ξ_ion
return grid_of_gas
# -------------------------------------------
# INPUTS FROM PHASE 2 (enriched ISM)
# -------------------------------------------
Metals = {C,O,N,Si,Fe,P,S,...} # yields from Pop III/II SNe
DustFraction(Z) = f_dust * Z # metals condense into dust grains

# -------------------------------------------
# PLANET FORMATION KERNEL
# -------------------------------------------
# Collapse of molecular cloud → disk → planetesimals → planets

M_disk := f_disk * M_star # ~0.01–0.1 M_star
Σ_gas(r) ∝ r^{-p} # gas surface density
Σ_dust(r) = DustFraction(Z) Σ_gas(r)

Coagulation:
dN/dt = -σ v_rel N^2 # dust grains collide/stick

PlanetesimalGrowth:
dM/dt ≈ π R^2 ρ_gas v_rel # sweep-up

RunawayGrowth:
when M > M_crit: dM/dt ∝ M^2 # gravitational focusing

TerrestrialPlanetFormation:
M_planet ∼ 0.1–10 M_earth at r ~ 0.5–2 AU

InitializeEarth:
Composition = {Fe,Si,C,O,H,N,P}
State = {oceans, atmosphere, volcanism}
return Earth
# Basic feedstocks (assume delivered via atmosphere, volcanism, meteoritic infall):
Feedstocks = {H2O, CO2, CH4, HCN, NH3, PO4^{3-}, simple organics}

EnergySources = {UV_photons, lightning, geothermal, hydrothermal}

# Example prebiotic reactions:
HCN + formaldehyde → amino_acids
HCN + NH3 + UV → adenine (C5H5N5)
PO4^{3-} + ribose + base → nucleotide

ReactionRate(i→j) = k_ij [i]^α exp(-Ea/kB T) under EnergySources

Polymerization:
Nucleotides + Energy → short RNA-like oligomers

ReplicationCriterion:
if oligomer length L > L_crit and template-directed ligation occurs:
tag oligomer as "self-replicator"

ReturnSet = {nucleotides, short_RNA}
PhaseTransition_RNA:
Input: {nucleotides}
Process: random polymerization + template copying
Output: {self-replicating RNA-like strands}

RNA_World_Dynamics:
dN_self/dt = r_replication N_self - r_degradation N_self

DNA_Upgrade:
if enzymatic-like ribozymes evolve that produce deoxyribonucleotides:
DNA(t) emerges as more stable information carrier

Return: {first DNA-based genome analogues}
SelectionOperator(population):
fitness(seq) = replication_rate(seq) - degradation_rate(seq)
NextGen = mutate_and_replicate(population, fitness)
return NextGen
# INPUT: Earth formed, atmosphere dense (CO2, CH4, N2)

AtmosphereOpacity(t) = f(volcanic_gases, CO2, CH4, aerosols)

if AtmosphereOpacity < threshold:
VisibleObjects := {Sun, Moon, Stars}
Timekeepers := {day/night cycle, lunar months, stellar seasons}

DefineCycles:
Day(t) := 24h rotation of Earth
Month(t) := synodic cycle of Moon ≈ 29.5d
Year(t) := orbital period around Sun ≈ 365d

MarkCalendar:
For Homo ancestors: cycles → circadian rhythm, agricultural calendar, ritual observances
# INPUT: Oxygen-rich atmosphere, stable climate

t ≈ 540 Myr (Cambrian Explosion)
Environment := {O2 ~ 10–20%, oceans stable, ecosystems expand}

BiodiversityIndex(t):
dS/dt = speciation_rate - extinction_rate

SpeciationOperators:
MarineLife(t) → Fish(t)
Fish(t) → Amphibians(t) via land transition
Reptiles(t) → Birds(t), Mammals(t)

GeneticFramework:
AllSpecies.genome = DNA
DNA.mutation_rate = μ
Evolution(pop):
NextGen = mutate_and_select(pop, fitness(env_conditions))
return NextGen

Output:
Explosion of phyla, vertebrates, land animals, birds
# INPUT: Mammals evolved, primates branch, hominins emerge

t ≈ 200k yrs (Homo sapiens)

GenomicOperators:
Homo.genome = {99% chimp, 1% distinct}
Genes for FOXP2, brain expansion, symbolic language

CognitionModel:
ConsciousnessLevel(species) = f(neocortex_volume, symbolic_language, abstract_thought)

if ConsciousnessLevel(Homo_sapiens) > threshold:
DefineImageOfGod(Homo_sapiens):
properties = {self-awareness, morality, creativity, symbolic reasoning, spirituality}
return properties

CulturalEvolution:
ToolUse → Agriculture → Cities → Writing → Science → Religion