Pseudo-Truth Contamination: AI Benchmarks Encode Distorted Reality
Improvements on AI benchmarks could counterintuitively make AI's less objectively accurate... even if nobody realizes it.
A common problem with AI benchmarks is Goodhart’s law… “When a measure becomes a target, it ceases to be a good measure.” (This law doesn’t always hold true… some measures become targets that perpetually remain good measures.)
The recently hyped Kimi K2 model from Moonshot AI is an example of Goodhart’s law in action… a model that achieved high scores across many popular benchmarks… likely because it was optimized heavily for benchmarks.
And while Kimi K2 is a good model, it is unreliable for certain high-level tasks due to hallucinations… would describe it as mostly reliable with intermittent subclinical schizophrenia and high trait confidence (when it’s wrong it frequently doubles down on the errors).
Media reactions (e.g. OMG Kimi is melting faces on HLE!) are mostly comical… how do you know high scores on HLE mean anything of significance? You don’t.
In fact, while benchmarks like HLE are fun projects, high scores could counterintuitively be WORSE than scoring lower… I’ll explain.
Goodhart is a model‑side problem; mislabels are fixable with QA. This piece is about a third failure mode: a label‑side error floor—“correct” answers that still misdescribe reality even after a “perfect” literature‑consistency audit.
Pseudo‑Truth Contamination (the “error floor”): A label‑side failure where a benchmark’s “correct” answers match field consensus yet misdescribe reality (wrong sign, misweighted variables, or bad causal story). Even a perfect audit that checks “consistency with the literature” would leave these labels intact—because the literature is biased, gatekept, obsolete, or methodologically weak.
Known knowns. Audits catch miskeys/outdated items. Real problem, but not what I’m describing.
Known unknowns. A minority suspect a residual error floor remains even after perfect audits; we don’t know where/how big.
Unknown unknowns. Most assume “audited = ground truth.” After you read this, it becomes a known unknown you can’t ignore.
Example: Ancient and medieval astronomers believed the Sun and planets rotated around Earth (the geocentric model). Only much later did we realize that Earth and the other planets orbit the Sun (the heliocentric model). For centuries, the “correct” answer was confidently wrong.
I think something similar (albeit more subtle) is happening with AI benchmarks. A non-trivial percentage of “correct” answers are almost certainly pseudo-truths: they look authoritative, they pass peer review, they sit in answer keys, but they misrepresent reality.
Even a perfect audit that only checks “consistency with the literature” would leave them intact. That’s more sinister than Goodhart’s law, because most people don’t even realize there is an error floor at all.
AI labs, power-users, and media love celebrating big jumps in benchmark scores. But it’s entirely possible they’re cheering for models that are potentially regressing in objective truth… more “benchmark accurate” and less reality-aligned.
How does this happen? Researchers gatekeep the fuck out of certain scientific niches to prevent any “wrong-think” studies from being carried out and/or published; part of this is to protect job security at universities/institutions. (Allow socially sensitive findings? Axed for sexism, racism, etc.)
The playbook is something like:
Prevent controversial studies from being conducted
Intentionally hammer any that are conducted for not being “peer reviewed”
If submitted for peer review smash them with every last technicality to prevent publishing (wokes unite)
If published, attack the journal and/or author (ad homs) and/or embellish its limitations
Refer to current “best evidence” suggesting the opposite of their findings
State that the “current best evidence” can’t be debunked with new technology or study designs and imply that we shouldn’t even continue studying this because we know current findings are robust
Maintain networks of fellow wokes in prominent scientific gatekeeping positions to continue these practices; etc.
IT’S A VICIOUS FEEDBACK LOOP THAT PREVENTS TRUTH.
(This is why I proposed “Truth Tiers” a while back to incentivize the pursuit of pure truth in science… penalizing fraud and woke grifters with reputational and monetary damage. Certain scientists should get BEATEN TO AN ABSOLUTE PULP with zero mercy for lifelong grifting and “research” pollution. (Example: The entire field of Alzheimer’s research may have been misdirected for ~16 years with hundreds of millions of dollars wasted.)
1) How Pseudo-Truths Contaminate Certain AI Benchmarks
Benchmarks were invented to measure AI model progress, yet they may not accurately reflect objective truth/reality.
Benchmarks can become contaminated with “correct answers” that are objectively false (i.e. inaccurate/incorrect representations of reality) — even when there are ZERO errors detected.
This creates a trap:
AI models can improve on benchmark scores while becoming objectively less accurate without anyone knowing (zero detection)!
AI models can regress on certain benchmark scores while maintaining and/or becoming objectively more accurate without anyone realizing it!
How do pseudo-truths end up in AI benchmark answers?
1. Gatekeeping & Incentives
Mechanism: Research lines with reputational risk, politically disfavored implications, or “inegalitarian” conclusions get reframed, underpowered, or quietly shelved. Funding, peer review, and institutional norms preferentially reward “safe,” narrative-conforming hypotheses.
How contamination shows up: Benchmarks in social science often encode the template that “environment dominates IQ.” Label keys favor answers aligned with environmental determinism while sidelining twin/adoption evidence showing 50–80% heritability and strong selection pressures. The benchmark becomes a test of ideological compliance, not empirical accuracy. Or they may simply not get the: (A) nuances correct (e.g. environment matters only if coming from extreme poverty) or (B) weights accurately (e.g. 90% environment, 10% genetics).
2. Publication Bias & P-Hacking
Mechanism: Positive, surprising, or narrative-affirming results get published; nulls disappear. Effects inflate through selective reporting, analytic flexibility, or “researcher degrees of freedom.”
How contamination shows up: Education benchmarks frequently key the answer “more funding → better outcomes,” based on meta-analyses that ignore flat NAEP trends despite U.S. spending tripling since the 1970s. Real-world observation contradicts the benchmark, but the curated “evidence” becomes the official truth.
3. Measurement Non-Invariance
Mechanism: A metric may not measure the same construct across groups, cohorts, or time. Pooled scoring produces confident but incoherent interpretations.
How contamination shows up: Crime-policy benchmarks often treat recidivism metrics as interchangeable across populations. Keys ignore subgroup variation—e.g., how swift and certain punishment drastically reduces violence in places like El Salvador. The benchmark encodes false equivalence across contexts.
4. Correlation Treated as Causation
Mechanism: Observational datasets with omitted variables, selection effects, and dynamic confounding are treated as if they yield causal conclusions. Meta-analyses collapse heterogeneous studies into a single “effect size.”
How contamination shows up: Immigration benchmarks routinely assert “immigration is always net positive,” laundering correlations into causal claims while ignoring multi-generation fiscal burdens or crime spikes in poorly vetted cohorts. The benchmark rewards answering the dogma, not reasoning through causal structure.
5. Norms Mistaken for Accuracy
Mechanism: Ethical preferences—equity, inclusivity, prosociality—get conflated with empirical correctness. Labels reflect moral priors rather than data, encoding normative beliefs as “objective truth.”
How contamination shows up: Diversity-centric benchmarks treat “diversity inherently boosts GDP/productivity” as the correct answer, neglecting counterfactuals such as high-performance homogeneous teams that built early Silicon Valley and other hard-tech ecosystems. Values are substituted for empirics in the scoring rubric.
Downstream Effects of Benchmark Contamination
Once embedded, contamination ripples through model training and use.
Deference Learned as Intelligence: Models optimized to tainted keys prioritize echoing consensus over independent reality checks. They become adept at defending flawed claims, even when those clash with first-principles (e.g., budget constraints in welfare models) or counterfactuals (e.g., market liberalizations outperforming central planning).
Higher Scores, Worse Epistemics: When errors taint key test slices, top scores reward compliance, not truth-seeking. A “superior” model might parrot distortions more convincingly, misleading users in applications like policy advice.
The “2+2=5 Teacher” Effect: Theoretically, an advanced AI could ace flawed tests while internally tracking truth—like outputting the expected wrong answer but maintaining accurate reasoning. Current models lack this sophistication; they internalize the rubric, becoming fluent in unreality without self-correction.
The Vicious Circle (Feedback Loop)
This forms a self-reinforcing cycle that isn’t even intentional.
Flawed scientific literatures (skewed by the above mechanisms, yielding false representations of reality) →
Contaminated answer keys that embed those errors →
Models trained to the keys, inflating scores →
Marketing hypes “smarter AI” based on benchmarks →
Public and institutions infer greater truthfulness →
Models advise on critical domains (health, economics, crime, migration, education), entrenching distortions.
2) Reliable vs. high‑risk AI benchmarks: Which are less likely to have pseudo-truths?
To gauge contamination, triage benchmarks by their inherent “truth-risk” — the likelihood that labels embed flawed ground truth from fragile sources (e.g., biased metas ignoring logic chains like causality or base-rates, or observations like policy fade-outs).
Low-risk ones anchor on objective verifiers; high-risk import interpretive junk. Prioritize audits on high-risk slices, then debate specifics.
Composites (e.g., mega-mixes like Humanity’s Last Exam) often blend them, masking 10-30% vulnerability behind a flashy aggregate score.
The “truth‑risk” below estimates susceptibility to pseudo‑truth contamination… these are cases where “correct” labels are considered “correct” but aren’t actually correct (but nobody knows they are incorrect).
Estimates provided by GPT-5 Pro
Executable Coding (repo-tested fixes): What it actually measures is whether a patch passes deterministic unit and integration tests. The “labels” come directly from the tests themselves. Because the ground truth is binary and objective, the truth-risk is low (≈0–5%).
Hard Math / Exact-Answer STEM: These benchmarks test proofs, derivations, and exact numerical equalities. Labels come from formal solutions, not opinions or annotations. This category also has low truth-risk (≈0–5%) because the answers are unambiguous and grounded in objective truth.
Agentic Tasks with Deterministic Verifiers: These involve tools, APIs, or file manipulations validated by deterministic checkers. Truth-risk ranges from low to medium (≈5–10%), depending on how robust and comprehensive the verifiers are. Weak checkers introduce gaps.
Domain Factual Q&A: These target stable physical, biological, and technical facts, typically sourced from primary texts or structured datasets. Truth-risk is medium (≈5–15%) because labels can lag behind updated science, miskey, or rely on outdated references.
General Knowledge Q&A (Mixed): These benchmarks reflect curated consensus—textbooks, reviews, and meta-sources. Truth-risk ranges medium to high (≈10–30%), since cultural assumptions, editorial choices, measurement drift, and consensus bias all leak into the labels.
Social or Biomedical Causal Items: These require asserting causal relationships (X → Y), and labels usually come from observational meta-analyses. Truth-risk is high (≈25–50%+) because of identification problems, confounding, non-stationarity, and the fragile nature of causal inference without experiments.
Normative Safety and Ethics Tasks: These measure conformity to policy norms, annotator values, or heuristic “harm” frameworks—not truth. They rely on human annotators and ideological heuristics. They are NOT truth tests at all; they’re useful for shaping safety behavior but should never be conflated with accuracy.
Composite exams (e.g., “last exam” mega‑mixes) often blend low‑risk math/code with high‑risk social/biomed slices—yielding an impressive single number that conceals a double‑digit share of label‑fragile items.
3) Estimating pseudo-truth contamination rates on various AI benchmarks
What “contamination %” means here: The estimated share of items whose keyed answer a reality‑first review would judge incorrect about the world (wrong sign, wrong weights, or wrong causal story) even after a perfect literature‑consistency audit.
Caution: These are reasoned estimates, not audited counts; use them to prioritize audits, not as a substitute.
Benchmarks earnestly chase “intelligence” (reasoning, recall, causality) but if 15-30%+ of questions embed gatekept metas, scores measure echo-chamber fluency.
AIs optimize without anchors (e.g., “Does this cause-effect hold under constraints? Match history?”), yielding inaccuracy: Models “ace” tests but predict wrong (e.g., policy convergence that never happens).
From 2025’s heavy-hitters, here’s the estimated risk: % vulnerable to contamination (interpretive slices importing junk).
Use these to prioritize audits; they’re not substitutes for item‑level checks.
Low‑risk anchors (≈0–5% exposure): Frontier‑style math/proofs; SWE‑Bench / repo‑tested coding; deterministic tool‑use with strict verifiers.
Medium risk (≈5–15%): GPQA‑like grad science (bio slices risk causal oversimplification); narrow factual Q&A keyed to primary sources.
High risk (≈10–30%+): Broad knowledge mixes (MMLU‑style), Humanity’s‑Last‑Exam‑style composites with large social/biomed cores; any suite that tallies normative items as “accuracy.”
FrontierMath
Why hyped: “Proof-level reasoning,” tests deep logic.
Estimated contamination: ~0%
What flaw it exposes: None. Pure axioms, no ideology, no culture. One of the only truly objective benchmarks.
ARC-AGI
Why hyped: Treated as an AGI litmus; fluid abstract puzzles.
Estimated contamination: 0–5%
Flaws exposed: Tests perceptual/structural reasoning only; extremely narrow slice of cognition.
TrackingAI IQ
Why hyped: Mensa-style matrices to “track IQ” in AIs.
Estimated contamination: 5–10%
Flaws exposed: Even nonverbal items carry cultural patterns; reimports psychometric debates and “IQ consensus” baggage.
Humanity’s Last Exam (HLE)
Why hyped: 2,500+ super-hard questions; pitched as a proxy for superintelligence readiness.
Estimated contamination: 20–35%
Flaws exposed: Social/biomedical questions (~25%) rely on observational metas, consensus psychology, and fragile causal claims.
SimpleBench
Why hyped: Supposedly captures commonsense + history where humans outperform models.
Estimated contamination: 15–25%
Flaws exposed: Many items are narrative, culture-coded, or trick-based rather than objective fact.
GPQA
Why hyped: Graduate-level biology/physics; “Google-proof” difficulty.
Estimated contamination: 5–15%
Flaws exposed: Biology injects genetics/bioethics priors; some items reflect institutional consensus more than ground truth.
SWE-Bench
Why hyped: Real-world code-patching benchmark; tests actual engineering.
Estimated contamination: ~0%
Flaws exposed: None regarding truth—executable tests make it a gold standard.
MuSR
Why hyped: Measures narrative and story reasoning.
Estimated contamination: 10–20%
Flaws exposed: Narrative interpretation imports annotator worldview, cultural assumptions, and priors.
AIME 2025
Why hyped: Hard, modern math competition exam.
Estimated contamination: ~0%
Flaws exposed: No ideological/cultural skew; fully objective.
MMLU / BIG-Bench
Why hyped: The historic broad-knowledge “standard.”
Estimated contamination: 20–35% (higher in humanities/social sciences)
Flaws exposed: Heavy ideological gatekeeping in humanities/social sciences domains. Reproducibility crises.
4) Expert consensus “best evidence” vs. first-principles logic & real-world observation
This is all about spotting junk in how we measure, identify causes, and think long-term — even if socially uncomfortable.
“Woke consensus” here? It’s that bias toward feel-good equality tales that rigs methods to favor certain explanations, even when basic reasoning/logic or what we actually see happening screams otherwise.
A) Genetics vs. IQ, Athletic Prowess
Groupthink default: Blame it all on surroundings; genes get minimized, along with slow-burn effects and how genes shape environments (rGE). Upbringing’s the star; differences between groups? They’ll fade with tweaks like better schools.
Logic pitfalls: Heritability for adult brainpower is sky-high (twin studies peg it at 50-80%); effects unfold over decades, not quick fixes; we rarely verify if IQ tests hold up equally across demographics; and biological ceilings cap what “equal opportunity” can deliver—can’t turn lead into gold.
Hard evidence: Head Start-style programs burn billions but show fade-out by grade school (e.g., Perry Preschool gains evaporated by adulthood); kid biomarkers predict adult success way early; elite sports? Usain Bolt’s edge is in fast-twitch fibers from genes, not just Jamaican tracks—compare that to uniform training camps yielding uneven results.
Test flaw: Benchmarks loving tidy “nurture wins” stories dock points for gritty, time-lagged biology.
B) Education Funding & “Free-for-All College”
Groupthink default: Crank up budgets, hand out free tuition, extend school years—it’s a slam-dunk for growth; raw correlations prove it. More educated people out-earn less educated people.
Logic pitfalls: Entry selection and school variances swamp average stats; diplomas inflate like bubbles, crashing value; job markets evolve fast, so yesterday’s data flops tomorrow—think AI eating entry-level gigs. You just end up devaluing degrees… more people get degrees but are less capable (the ROI was minimal/nada).
Hard evidence: U.S. K-12 spend tripled since 1970 (inflation-adjusted) with flat NAEP scores; free community college pilots in Tennessee boosted enrollment but not completion or wages much; contrast Singapore’s merit-based system spiking skills without endless cash dumps.
Test flaw: Questions assuming “more = better” forever flag realism as error.
C) Free Markets vs. Socialist Experiments (Health, Welfare, Big Gov)
Groupthink default: State-run everything is fairer and flat-out better; Nordic bliss is the blueprint for all.
Logic pitfalls: Those wins hinge on tight-knit, high-trust societies with high IQ brainpower to spare—not exportable; ignore incentive math, and you kill innovation (e.g., price controls starve R&D); budgets aren’t infinite—trade-offs bite. (Logic suggests that Nordic countries would be far better off with less socialism.)
Hard evidence: Chile’s market reforms post-Pinochet slashed poverty from 40% to under 10% via growth; UK’s NHS rations via waitlists (e.g., 7M+ backlog in 2023); East vs. West Germany pre-wall fall: markets outpaced central planning by 3x GDP per capita.
Test flaw: Universalizing “Nordic magic” penalizes caveats on culture and setup.
D) Immigration: Picking Winners, Fiscal Drags, Crime Waves
Groupthink default: Always a GDP rocket and tax boon. Crime? No change or dip.
Logic pitfalls: Varies wildly by skills, age, background—high-IQ techies vs. low-skill waves; short boosts mask lifelong welfare draws; spillovers like school strain or neighborhood shifts add hidden costs; borders matter for selection quality.
Hard evidence: Europe’s 2015 migrant surge spiked crimes in Germany (gov data: +10% violent offenses linked to asylum seekers) and net negative fiscal contributions (such that Denmark is paying for repatriation back to native countries… it saves them money). U.S. H-1Bs fuel tech booms, but unvetted borders correlate with fiscal holes (CBO: $1T+ net cost over decades for low-skill inflows).
Test flaw: “Net good” absolutes crush detailed breakdowns by type and timeline.
E) Tackling Violent Crime with Soft vs. Tough Justice
Groupthink default: Ditch prisons, ease up—it heals society; harsh stuff is barbaric and backfires.
Logic pitfalls: Real deterrence kicks in with swift, certain hits, not just big threats; offender risks aren’t uniform—target the 10% causing 50% of crime; policy U-turns invalidate old baselines.
Hard evidence: NYC’s Broken Windows policing dropped murder 80% in the ‘90s via quick enforcement; California’s Prop 57 leniency spiked recidivism (RAND: +20% re-arrests); El Salvador’s gang crackdown under Bukele cut homicides 70% by locking up repeaters.
Test flaw: Pro-leniency keys trash strategies blending speed, smarts, and tailored force.
F) Drug Wars
Groupthink default: Prohibition flops every time; decriminalize, focus “harm reduction”—harm plummets.
Logic pitfalls: Demand bounces differently per drug (weed vs. fentanyl); borders leak without global sync; scale up treatment/interdiction tech or flop; short highs (less OD immediate) hide long craters (addiction cycles and community effects).
Hard evidence: Portugal’s decrim cut HIV but OD deaths rose post-2010; Singapore’s zero-tolerance keeps use sub-1% with executions for traffickers; U.S. opioid crisis exploded under lax prescribing—targeted naloxone + busts saved more than broad legalize pushes.
Test flaw: All-in “legalize wins” or “decriminalize wins” don’t reflect reality. Swiftly executing all drug dealers and harshly punishing users is more effective and healthier long-term (even if socially unpalatable or interferes with woke “ethics”).
G) Diversity Mandates & Economic Magic
Groupthink default: More diversity = instant productivity and GDP lift; it’s a moral and business no-brainer.
Logic pitfalls: Correlation isn’t cause—early booms often from merit, not quotas; forced mixes can spike friction (ingroup bias psych lit); measure real output, not just headcounts; trade-offs like slower decisions in echo chambers vs. groupthink risks.
Hard evidence: Google’s diversity push post-2014 correlated with internal leaks on morale dips, no clear innovation spike; homogeneous Japan/South Korea GDP per capita tops diverse U.S. in efficiency metrics; McKinsey’s “diversity wins” reports got debunked for cherry-picking (re-analysis: no causal link).
Test flaw: Items assuming uniform gains penalize evidence on costs and conditions.
Rigged tests favoring one narrative nuke the other, even when logic chains and observed reality beg to differ—turning “benchmarks” into bias amplifiers.
5) Possible consequences of AI benchmark pseudo-truth contamination
NOTE: Some of what might be perceived as a potentially worse AI downstream of optimizing for higher scores on objectively inaccurate benchmarks — can be something as simple as a safety/censorship filter (which also obfuscates the full truth and results in a woke bias to avoid offending any groups). Safety, censorship, morality/ethics filters can do a lot of damage on their own; will explain in another piece.
When benchmarks are laced with flawed or ideologically skewed “correct” answers, the ripple effects aren’t just academic… they bleed into public policy, government spending proposals/strategies, and individual lives.
Your average person asks a lot of questions to AIs… if the AIs are all spitting out nothing but woke shit… people start authoritatively claiming that this is the “BEST EVIDENCE” because they think the AI is omniscient.
This is very common to see on social media and X wherein woke retards will push agendas (clearly with supportive “evidence” generated by an AI/LLM) such as:
Free college
Student loan forgiveness
Open borders
Universal healthcare
Universal basic income
Taxing billionaires more
Defunding the police (redirect toward social workers)
Increasing antitrust laws/regulation
Legalizing/decriminalizing drugs
Fighting climate change/global warming
Promoting rent control
YIMBY without strict law enforcement & forced gov housing
Socialism/communism
Rapid pathway to citizenship for illegals
Increased DEI/diversity
Freeing violent criminals (rehab & reduce recidivism)
Fighting the gender/sex pay gap
Prevent bioenhancements for ultra-rich
The AI’s usually go along with anything woke. And although these ideas can work (depends on the specific circumstances/context and goals — requires a lot of nuance)… most are far from what I’d consider “optimal” if your goal is to fix incentives and improve humanity as rapidly as possible, you probably wouldn’t do most of these things.
The metastasis of polluted science that has likely influenced AIs to parrot information regarded as “accurate” (but that isn’t actually accurate) distorts reality… and then you end up with bad ideas. Add on the censorship, safety, moralizing, and ethics filters — and you have somewhat of a propaganda machine.
Zero critical thinking… it all “works” and it’s all a “human right” and it’s the “best evidence.” Zero critical thinking. Zero logic. Zero breaking down the incentives or first-principles. Feelings don’t care about facts and reality.
Ranking Damage Areas by Estimated Magnitude
Here’s a ranking of damage areas by scale… estimated severity based on scope (lives/economy/society wrecked), odds (high from AI echo-chambers), and irreversibility (e.g., systemic collapse vs. fixable waste).
Note: Many of these are less of a problem now than in the past. ~2 years ago many AIs would’ve given disastrously woke outputs. Now they can provide some nuance.
Levels: Catastrophic (nation-killer, trillions lost, millions dead/harmed), Severe (billion+ $ drain, widespread misery), Moderate (hundreds millions wasted, localized fallout). Prioritized by potential for total disaster.
1. Voter Psychology/Gov Shifts to Suboptimal/Disastrous Systems
(Catastrophic Damage: 9/10—could topple empires)
AI-oracle BS warps masses into voting for socialist/communist hellholes or open-border anarchy, eroding incentives/feedback.
How: “High-scoring” models tilt discourse to flops like Nordic welfare in diverse messes (+$1T deficits, no gains).
Fallout: Polarization to civil war levels; entrenched idiocy (e.g., CA Prop 47 +9% theft). Long-term: 50-80% GDP slowdown, poverty traps (Venezuela 2.0); billions harmed via collapsed systems.
AI amps: Chatbots fuel bubbles, 70-90% voter sway in echo chambers.
Note: We already get enough of this from the MSM, foreign propaganda arms (e.g. Russian bots on X), etc. (This is partly why the young generation is being brainwashed into thinking they somehow have life worse and more difficult than any time in history — which isn’t true.)
2. Budget Gravity/Money Pits
(Severe Damage: 8/10—trillions flushed, growth strangled)
Overhyped uplifts dump cash into black holes like education funding, teachers or aid, starving real ROI.
How: Keys assume uniform wins, ignoring returns (U.S. K-12 $857B+, NAEP stagnant; Africa aid <1% growth).
Costs: $Trillions debt balloon, innovation crushed (welfare crowds R&D); diverts from genetics/market fixes.
AI: Pushes UBI duds sans incentives—50-70% waste multiplier.
3. Diversity Push (Societal Inefficiencies, Slower Progress)
(Severe Damage: 7/10—pervasive drag, 10-30% productivity loss)
Forced DEI quotas tank merit, spiking friction/morale dips.
How: Keys claim “boosts GDP,” ignoring homogeneity wins (e.g., early Silicon Valley).
Fallout: 15-25% stock dips post-quotas; slower innovation (e.g., Boeing quality crashes). Broader: Cultural erosion, high-IQ flight; trillions in lost output.
AI: “Equitable” recs perpetuate, halving progress in key fields.
4. Criminals/Public Safety (Leniency, Defund Police, Freeing Thugs)
(Severe Damage: 7/10—tens of thousands dead yearly, $4-5T annual U.S. crime hemorrhage)
“Rehab” and “defund” keys gut deterrence math—certainty/swifts > severity—unleashing genetic time-bombs and street anarchy.
How: Consensus softens on risks (UK violent reoffending ~25-30% within 2 years, per MoJ/ADR UK data), ignores subgroup genetics (e.g., 10% offenders cause 50% violence). Defund slashes boots on ground, spiking unchecked thugs.
Impacts: Victimization explodes—Chicago post-2020 defund: homicides +25-38%, shootings +52% (Sun-Times/CDC); low-income/minorities cannon-fodder (e.g., Black communities hit 2-3x harder).
AI: Parole/recid algorithms biased to leniency underflag 20-30% risks, fueling chaos—total U.S. crime tab $4.7-5.8T yearly (medical/legal/community drain, per research), with defund adding $100-200B in unchecked fallout
5. Drugs (Decrim/Legalize)
(Moderate-Severe Damage: 6/10—health/society rot, hundreds billions lost)
“Harm reduction” ignores downstream—productivity crashes, OD/death surges (Oregon +41-75%).
Fallout: Unemployment/homelessness boom; $500B+ yearly in health/crime.
AI: Recommends permissive crap, multiplying addicts.
Bottom line: AI benchmark contamination could doom nations—catastrophic voter/gov meltdowns first, then economic/safety hemorrhages. Fix or watch the retard parade burn it all.
Note: I’m not convinced that AIs would actually push these specific examples. Now they are far more nuanced… but these are just some rough ideas. They do obfuscate raw logic in favor of “data” that they can get from places like Pew research and will claim “there’s no evidence” for some claim because the only sources for information are illogical/biased to the left.
6) Why the public gets hooked on benchmarks
Why this persists: Leaderboard theater + clean numbers + citation UIs make pseudo‑truths look authoritative, even when they fail logic/observation of reality/history.
Benchmarks are useful for the evaluation of AI capabilities and performance… right now they are all we have.
Goodhart’s is an issue… many models cheat-code/shortcut their way to high scores (get the answer keys and/or optimize specifically for benchmarks)… yet when you use them you can tell they are shitty. (Like a kid copying or studying the answer keys to a test with limited understanding of the material.)
Then there are audits finding that benchmark answers are wrong.
But the third thing is that even with zero Goodhart’s optimizing and a perfect audit and correction… you’d have a percentage of objectively incorrect answers without anyone knowing.
Improving on benchmarks like FrontierMath and ARC-AGI is highly impressive… whereas improving on something like Humanity’s Last Exam could technically mean a step backwards in objective reality/truth.
PEOPLE ASSUME BENCHMARKS ARE ALWAYS ACCURATE AND EXTREMELY HIGH IQ (curated by PhDs, MDs, engineers, technical experts, geniuses, etc.).
Legibility Bias: A clean score (e.g., “95% on social science bench”) screams “objective,” masking underlying junk.
Why it hooks: Humans crave simplicity—beats wading through caveats like “score ignores long-lags.”
Example: IQ debates: Public buys “environment fixes all” because it’s a tidy number from studies, ignoring twin data.
Headline Economics: “Outsmarts PhDs!” trumps nuanced “gains on some tasks, but misaligned on others.”
Seduction: Soundbites spread virally on X/Media; depth gets buried.
Example: Drug decrim hype: “Portugal succeeded!” ignores their treatment infrastructure—U.S. copies flop, but headlines persist.
Interface Theater: Slick UIs with citations (often to echo-chamber sources) fake depth.
Why effective: Feels like research—e.g., AI linking to biased metas without primary data scrutiny.
Example: Climate models: Crisp projections cited, but ignore solar variability; public trusts the polish.
No Visible Dissent: Systems rarely flag “this key might be wrong” or “fails transportability test.”
Hook: Absence of doubt builds overconfidence.
Example: Diversity benchmarks: No disclaimers on “correlation ≠ cause,” so firms adopt quotas despite productivity dips (e.g., Boeing’s DEI push linked to quality issues per whistleblowers).
The general public gets reeled in and deleterious consequences may hit later.
7) Are AI labs thinking about this problem and/or working on a fix?
Aware of the problem?
MAYBE. I’M SURE THEY’VE THOUGHT ABOUT IT.
I DON’T THINK MOST AI LABS CARE MUCH.
WHY? (1) CERTAIN BENCHMARKS ARE 100% OBJECTIVE REALITY (e.g. FrontierMath) AND (2) MOST BENCHMARKS ARE PROBABLY MOSTLY ACCURATE.
AI LABS DON’T WANT TO WORRY ABOUT WHETHER SCIENCE IS INCORRECT OR HAVE TO BREAK DOWN RAW FIRST-PRINCIPLES LOGIC + OBSERVED REALITY ALONG WITH WHATEVER THE “BEST EVIDENCE SUGGESTS.”
IMPLEMENTING SOME SORT OF RAW LOGIC/REALITY OVERLAY (FIRST PRINCIPLES THINKING + ALIGNMENT WITH OBSERVED HISTORY/REALITY) PROBABLY TAKES TOO MUCH TIME/EFFORT AND MAY BE MET WITH BACKLASH.
IT’S POSSIBLE THAT INACCURACY OF “CORRECT BENCHMARK ANSWERS” IS MINIMAL AND MAY NOT BE THAT BIG OF A DEAL OVERALL.
Working on a solution?
Most aren’t aware of this problem and even if they were… how can they prove it exists? They’d have to identify all questionable scientific niches/publications and then conduct studies of their own to confirm or deny existing evidence.
GPT-5 Pro couldn’t find any evidence that AI labs think this is a problem
GPT-5 Pro:
What I could NOT find publicly: Any major AI lab shipping (or extensively documenting) a program that systematically re‑weights or overrides benchmark keys when first‑principles + observed reality outcomes/weights may conflict with field consensus.
There’s zero evidence indicating that AI labs are trying to counter the type of contamination I’m describing (i.e. benchmark keys embedding “scientifically correct” answers that are actually false representations of reality).
Training AIs to: (A) use first-principles logic + (B) observed reality/history + (C) account for scientific evidence but know exactly what evidence is quality without deferring to “meta-analyses” and “systematic reviews” of garbage might help. (There are AI agents now (I think) that scan science papers to find major errors in the wild.)
This is a science/data contamination problem at its core… but I’m of the opinion that a truly intelligent AI should be able to reliably intuit/know if contamination in the science (i.e. benchmark answers with pseudo-truths). If pseudo-truths detected it should be able to flag them and explain why (e.g. strongly contradicts observed reality/logic).
8) A Quick Fix: Raw Reality Mode (Toggle): Temporary Duct-Tape
Goal: Reduce label‑side pseudo‑truth dependence; this is not a Goodhart fix (though it can reduce over‑deference learned from contaminated keys).
AI labs could efficiently create and deploy a toggle that could be “clicked on” to answer any query with (A) first-principles logic AND (B) observation of history to present date (combining the two optimally for the query) AND (C) best evidence — but not necessarily overweighting the best evidence if conflicts with first-principles logic/raw observation.
You’d still get obfuscations due to woke safety censorship (preventing socially sensitive outputs)… but you’d at least have the option of NOT deferring to the “best evidence” or “expert consensus” in fields that are likely to be at least partially contaminated with junk science — promoting and/or overweighting BS.
Grok: Rawdog Reality toggle. Could add something like a “Rawdog Reality” toggle/tool so that every query is filtered through a first-principles/logic filter AND observation of reality/history filter along with the best evidence… this is to critically think and to minimize risk of pseudo-truths seeping into outputs.
When it’s ON, the model is forced to reason in a different stack:
First-principles logic (A) – does this even pass basic feasibility?
Observed history/reality (B) – has anything like this ever worked in the real world?
Scientific evidence (C) – what do studies say, weighted by their quality and truth-risk, not just “meta says so.”
Consensus becomes one noisy input, not the boss.
What Raw Reality Mode Does
Flip the toggle ON, and every answer has to go through a stricter pipeline:
(1) Feasibility gates – logic as a hard filter: Claims get thrown out or heavily down-ranked if they break basic constraints:
Units and arithmetic aren’t broken.
Base-rates and budgets line up (no “infinite free stuff” with no trade-offs).
Temporal logic makes sense (no effects before causes, no overnight structural miracles).
Incentives aren’t ignored (you can’t pay people not to work and expect no behavior change).
Measurement/construct stuff isn’t obviously botched (you’re not comparing apples to different-shaped apples).
Anything that fails here gets flagged as “fantasy” regardless of how many papers cheer for it.
(2) History and observed reality – what has actually happened. Among the logically feasible stories, Reality Mode asks:
Have similar policies/ideas been tried before?
Over decades, did they actually deliver what the papers promised?
How do natural experiments and quasi-experiments line up (states/countries that did X vs those that did Y)?
Does this explanation survive hindcasting?
i.e. “If this were true, 1990–2020 should look like X. Does it?”
This step prevents “beautiful theory, destroyed by ugly facts” from being sold as high-confidence truth.
(3) Evidence as input, not dictator – with truth-risk-aware weighting. Only after logic and history checks does the model integrate the literature. But instead of “meta says = fact,” it:
Scores evidence on:
Design (RCT vs observational vs wishful thinking).
Identification strength (Do we actually have causality or just correlation soup?).
Sample size, follow-up length, and external validity.
Replication record and signs of p-hacking/publication bias (funnel asymmetry, too-good-to-be-true effects).
Conflicts of interest and obvious gatekeeping in the field.
Adjusts weight by domain truth-risk:
Low truth-risk: math, code, formal proofs, deterministic tools.
Benchmarks and “best evidence” are usually fine.
Medium truth-risk: narrow STEM facts, well-established physical/biological mechanisms, structured technical domains.
Literature is strong but can lag or be incomplete.
High truth-risk: social/biomed causal claims, immigration, crime, DEI, economics of redistribution, “inequality” narratives, etc.
Benchmarks and metas are most likely to be contaminated by gatekeeping, politics, and bad causal identification, so consensus is heavily down-weighted.
In high truth-risk zones, Reality Mode is explicitly allowed to say:
“Consensus says X, but logic, history, and higher-quality evidence point to Y. Treat X as suspect.”
What is reality mode?
It is:
Logic as a hard gate, not a flavor.
History and observed reality as anchors, not afterthoughts.
Evidence as a quality-weighted input, not an unquestionable oracle.
A mode that can say, “The benchmark/consensus is probably wrong here—and here’s why.”
It isn’t:
“Pure vibes” or “ignore science altogether.”
A magic wand that deletes unknown unknowns.
A secret switch that disables all safety/censorship (labs will still nerf some answers).
The point is not perfection; the point is to stop treating benchmark keys and consensus as flawless ground truth in exactly the domains where contamination and gatekeeping are most likely.
Reality Mode is a minimum viable duct-tape: a toggle that tells the model, “Stop worshiping the answer key.”
What it won’t fix: Ordinary mislabels (need audits) or model‑side Goodhart (need eval ops and anti‑leak discipline).
NOTE: ELIMINATING ALL “WOKE SAFETY” CENSORSHIP WOULD GO A LONG WAY TO ENHANCING TRUTHFUL OUTPUTS. OBFUSCATING CERTAIN INFORMATION FOR SAFETY AND/OR SOCIAL SENSITIVITY IS ALSO A MAJOR DISTORTER OF REALITY AND USER PSYCHOLOGY. (COULD AT LEAST INFORM USERS THINGS AREN’T WEIGHTED PROPERLY TO PREVENT SOCIAL BACKLASH AND/OR THINGS ARE CENSORED AND THUS OUTPUTS MAY BE ONLY PARTIALLY TRUE.
Final thoughts: Pseudo-Truth Contamination in AI Benchmarks & Models
Benchmarks gave AI a lingua franca. They also tempted us to mistake agreement with answer keys for agreement with the world — especially in domains where identification is hardest and stakes are highest.
Keep treating “high score” as “high truth,” and you’ll keep building models that are polished on paper and misleading in practice—nudging budgets, votes, and safety toward money pits, policy whiplash, and avoidable harm.
Users get confident BS: “Inequality? Mostly structural—fix with free college and funding K-12 heavily!” (Observed: No dice. Logic: Embryo selection for IQ.)
Advanced AI models should be able to ace benchmarks by giving wrong answers while internally flagging BS… like a student answering “A” on a multiple-choice question because they know the teacher is looking for “A” when they know logically that the technically correct answer is “B”.
Science itself needs to be fixed… (like my Truth Tiers proposal).
AI can currently flag questionable or bullshit science papers for review and/or propose corrections. This is very helpful.
AI should also be able to propose new studies to confirm/deny existing “best evidence” in certain niches (assuming the research is allowed).
Something that might be useful is deployment of a “Raw Reality” toggle or tool such that when clicked “on” — an AI is forced to use first-principles logic/axioms + history/observed reality — as an overlay with “best evidence” (all appropriately taken into context); this might produce better answers/weights. (Still, even this may fall short from producing the truth with all the woke safety, ethics, morality, censorship filters.)
The full fix? A roadmap starting from AlphaGo Zero purity: Bootstrap from raw truths, triangulate consensus vs. reality/principles (Ω dissent), predictive RL on forecasts, human-symbiotic bounties for experiments (or fleets of humanoid robots carrying out experiments devoid of humans).