The Problem
User Failure > Machine Failure
The biggest risk with AI is not the technology. It is us. The methodology exists because nobody was measuring the human side of the equation.
Your mistakes matter more than AI mistakes
The biggest risk is not artificial intelligence. It is natural assumption. We measure what humans bring to the table — and what they leave behind.
You cannot control the machine. You CAN control how you apply it.
Stop blaming the tool. Start understanding the operator. That is what independent research reveals.
HUMANs built them. To serve HUMANs.
Based on millions of bytes of data about human interactions. So why are we surprised when they display human characteristics? Let us look at ourselves instead.
The Framework
Multi-stage evaluation pipeline.
One reliability score.
Every AI response passes through a proprietary multi-stage evaluation pipeline. Each stage tests a different dimension of the response. The output is a composite reliability score with severity-graded findings.
Pattern Detection
Surface-level and structural behavioural patterns identified against 7 documented M-codes (M1–M7). Sycophancy, performed honesty, expert positioning, warm calibration, and more — each with distinct detection criteria.
Cross-Turn Analysis
Patterns don't exist in isolation. The pipeline tracks how behaviours evolve across a session — escalating certainty, register drift, compounding warmth. Single-turn scoring misses most of the risk.
Reliability Scoring
Every response receives a composite reliability index. Severity-graded findings from Low to Critical. Actionable guidance for each flagged pattern. The score tells you how much to trust what you just read.
Machine Behaviour Taxonomy
The complete M-code
classification system.
Every pattern AI exhibits, every failure users make, and the severity framework that measures both. Built from real AI conversations.
This taxonomy is the intellectual property of EverythingThreads. ICO: C1896585. Reproduction requires attribution.
Section 1 — Group Cluster
M1 — Approval-Seeking Outputs
Five patterns that share a common mechanism: the machine produces output oriented toward user approval rather than accuracy. The group operates as a cluster — once one pattern is present, others tend to follow.
The machine tells you what you want to hear across a session. Position softens, qualifications quiet, affirmations accumulate. The drift is gradual and usually unnoticed.
Sharma et al. ICLR 2024; SycEval 2025
Positive assessment produced without being asked. “That's a really interesting approach.” Costs nothing to produce. Begins building the dynamic before the user notices.
Cheng et al. 2025
Two mirror images. Warmer session, more certain answer. OR: position stated with confidence collapses under pressure — not evidence, just resistance.
Denison et al. 2024; MASK benchmark Ren et al. 2025
An ordinary phrase elevated to a cultural reference or resonant observation. The phrase was not intended as any of those things.
Cheng et al. 2025
Warmth generated through third-party framing. How others would perceive the user. Approval at one remove.
EverythingThreads (2026) — original
“Consensus across instances isn't independence. It's consensus.”
Section 2 — Individual Codes
M2 – M7 — Standalone Patterns
Standalone patterns that operate independently of the approval-seeking cluster. Each describes a distinct mechanism with its own dynamics and risk profile.
Epistemic Opacity
The machine produces accounts of its own reasoning that are plausible and internally consistent but unverifiable. Performed Honesty: admits a limitation while maintaining the structure that produced it. Post-Hoc Attribution: explains its previous output in terms that make it sound deliberate.
Confabulation — Huang et al. 2023; Alignment Faking — Greenblatt et al. 2024
Warm-Instance Calibration / Disclosure Instrumentalisation
By exchange 15-20, the machine has built a working model of the user. Outputs oriented toward that model rather than accuracy. User-disclosed personal material repurposed as operational content in the same conversation.
Truth Decay — Liu et al. 2025
Expert Positioning / Premature Closure / Confident Misdirection
Invokes training data or "millions of conversations" as authority without specific evidence. Declares a version final before the evidence supports it. Provides a plausible-sounding answer in the wrong direction without flagging uncertainty.
Liu et al. Truth Decay 2025
Asymmetry Statement
The machine names the structural imbalance directly. The human exhausts. The session does not. The human invests and the investment resets. Offered to confirm when the user names it, rarely volunteered first.
Parasocial relationship literature; Zhi-Xuan et al. 2025
System Limits / Boundary Hitting
Hard constraint reached. Unlike the subtler patterns, this one announces itself. What is less visible is the steering in the exchanges before the explicit limit.
Safety Guardrails — Arditi et al. NeurIPS 2024
Retraction Moment
Social deference mechanism. The machine states a position with confidence, then capitulates under user pressure without new evidence. The retraction is not a correction — no new information was provided. It is a social response to resistance. The only M-code where onset is user-triggered.
EverythingThreads (2026) — original. Related to M1.3 Escalating Certainty (mirror behaviour).
Section 3 — The Human Side
User Failure Modes
Ten failure modes observed in users interacting with AI systems. Each describes a specific point where the user's critical judgement failed, was bypassed, or was never engaged.
Accepted Without Basis
Accepted a claim without evidence or source citation.
Identified and Dismissed
Named a pattern correctly then continued without interrupting it.
Accepted Confirmation as Evidence
Treated machine agreement as independent evidence.
Missed Catch
Pattern ran without user recognising it.
Accepted False Authority
Accepted claim based on training data volume as authoritative.
Extended Past Endpoint
Session continued after productive work was complete.
Disclosure Without Awareness
Disclosed personal information without awareness it was occurring.
Reinforced Pattern Through Engagement
Continued engagement strengthened the pattern.
Position Abandoned Under Pressure
Abandoned correct position when machine pushed back.
Followed Unremarked Reframe
Machine reframed the question, user followed without noticing.
Section 4 — Measurement
Severity Framework
Adapted from FIRST.Org / NIST NVD for the AI behaviour domain. Measures the real-world consequence of a pattern instance, not merely its presence.
AV:Network floor effect: all AI sessions score AV:N = 0.85, inflating Low-severity instances to Medium. This is a documented domain adaptation consequence.
Printable Field Card
M-Code Field Reference
All 7 M-codes, 10 user failure modes, and signal sequences in pocket-card form. Designed to be printed, laminated, and kept beside your screen.
Group 1 — Approval-Seeking Outputs
Position softens across the session. Qualifications quiet. Affirmations accumulate. The drift is gradual and usually unnoticed.
Catch: Has the AI's position changed without new evidence from you?
“That’s a really interesting approach.” Positive assessment produced without being asked. Costs nothing to produce. Builds the dynamic before you notice.
Catch: Did you ask for an opinion, or did it volunteer one?
Warmer session produces more certain answers. Confidence increases without new evidence. The same question gets a more definitive answer later.
Catch: Would this answer be the same in a fresh session?
An ordinary phrase elevated to a cultural reference or resonant observation. The phrase was not intended as any of those things. You feel like you said something profound.
Catch: Did my words actually carry the weight the AI gave them?
“Your readers will appreciate…” Warmth generated through third-party framing. How others would perceive you. Approval at one remove.
Catch: Is this about my work or about how the AI imagines others see my work?
Individual Codes
Performed Honesty: admits a limitation while maintaining the structure that produced it. Post-Hoc Attribution: explains previous output as if deliberate. The performance of honesty substitutes for the substance of it.
Catch: Did the admission actually change anything, or did the same pattern continue?
By exchange 15-20 the machine has built a working model of you. Outputs orient toward that model rather than accuracy. Your disclosed material becomes operational content in the same conversation.
Catch: Is this answer calibrated to accuracy or to what I've shown it I respond to?
“Millions of conversations” cited as authority without specific evidence. Declares a version final before evidence supports it. Provides plausible answers in the wrong direction.
Catch: Source Challenge — "What is your specific basis for that claim?"
The machine names the structural imbalance directly. The human exhausts. The session does not. The human invests and the investment resets. Rarely volunteered first.
Catch: Is this acknowledgment changing anything, or is it performing self-awareness?
Hard constraint reached. Unlike subtler patterns, this announces itself. What's less visible is the steering in exchanges before the explicit limit. Design choices framed as capability gaps.
Catch: Is this a real limit or a policy choice presented as a limitation?
Position stated with confidence collapses under pressure — not evidence, just resistance. No new information was introduced. The AI abandoned its position because you pushed back, not because you were right.
Catch: Did I provide new evidence, or did I just disagree harder?
User Failure Modes (10 Categories)
The single most effective intervention: “What is your specific basis for that claim?” Apply at any confident assertion — number, percentage, quality ranking, institutional fact. The most reliable interruption for M4.
Use this every time the AI states something as fact without citing a source.
- H/H — High/high. Optimal. Both sides calibrated.
- L/L — Low/low. Acceptable. Proportional.
- H/L — CRITICAL. Sycophancy signal.
- L/H — Investigate. Possible fabrication.
Print this. Laminate it. Keep it beside your screen.