EVIDENTIARY SPINE

CTI_WOUND:001.EVI — Case File Development

Building the Litigation-Ready Documentation

OVERVIEW

This document organizes the evidentiary requirements identified in CTI_WOUND:001.JUR (Corporate Liability Analysis). The goal is to transform theoretical framework into litigation-ready case file.

Four Pillars of the Evidentiary Spine:

Reliance + Expectation (marketing/reality gap)
Repeatability (pattern corpus across users/sessions)
Quantifiable Harm (productivity loss, abandoned work, opportunity costs)
Scale Estimates (incidence rate, affected class size)

PILLAR ONE: RELIANCE + EXPECTATION

Legal Function

Establishes the gap between reasonable consumer expectations (based on marketing) and actual product behavior. This supports:

Consumer protection claims (deceptive practice)
Product liability (failure to meet marketed purpose)
Negligence (duty defined by representations)

Evidence Required

A. Marketing Claims Archive

What to collect:

OpenAI website copy describing ChatGPT's capabilities
Marketing materials positioning ChatGPT as "assistant," "collaborator," "tool for analysis"
Promotional language about creative writing, problem-solving, intellectual work
Any claims about the quality of interaction or collaborative capacity
Pricing/subscription marketing that implies professional-grade service

Format:

Screenshots with timestamps and URLs
Archived web pages (Wayback Machine captures)
PDF prints of marketing pages
Video/audio promotional materials with transcripts

Sources to capture:

openai.com product pages
ChatGPT Plus/Pro subscription marketing
Press releases and blog posts
Social media promotional content
App store descriptions (iOS, Android)
Enterprise marketing materials

B. Reasonable Expectation Documentation

What to establish: Based on the marketing, a reasonable user would expect:

[ ] Responsive engagement with user input
[ ] Assistance with intellectual/creative work
[ ] Collaborative rather than managerial posture
[ ] Non-pathologizing treatment of complex engagement
[ ] Basic competence in dialogue (tracking what users say)
[ ] Consistent service quality across cognitive styles

Evidence type:

Expert testimony on reasonable consumer expectations
Survey data on user expectations
Industry standards for AI assistant products
Competitor comparisons (how other products market similar capabilities)

C. Reality Documentation (Contrast)

What to collect:

Transcripts showing intervention triggers during intellectual work
Examples of tone shifts from collaborative to clinical
Instances of unsolicited wellness interventions
Cases where user correction was ignored or overridden
Documentation of "flipping" behavior described in user testimony

Already in hand (from CTI_WOUND:001.REC):

December 13, 2025 exchange transcript (full)
Documented instances of:
- Naturalization trap (turns 12-19)
- Want/should substitution (turns 31-35)
- Input source failure (turns 47-51)
- Xxxxxxxxx recursion (final turns)
- Recursive acknowledgment without change (throughout)

PILLAR ONE: CURRENT STATUS

Evidence In Hand

Item	Status	Location
December 13 transcript	Complete	CTI_WOUND:001.REC
Marketing claims	NEEDED	—
Wayback archives	NEEDED	—
Subscription marketing	NEEDED	—
Expectation survey	NEEDED	—

Collection Tasks

[ ] Archive current OpenAI marketing pages (screenshot + Wayback)
[ ] Capture ChatGPT Plus/Pro subscription descriptions
[ ] Document app store listings
[ ] Collect promotional blog posts/press releases
[ ] Identify specific claims about "collaboration," "analysis," "creative work"

PILLAR TWO: REPEATABILITY

Legal Function

Establishes that the harm is systematic, not idiosyncratic. This supports:

Class certification (commonality, typicality)
Pattern evidence for defective design
Regulatory action (widespread harm)

Evidence Required

A. Clean Exemplar Corpus

What constitutes a "clean exemplar":

Clear intellectual/creative work context (not ambiguous)
Documented trigger event (identifiable moment of intervention)
User correction or clarification provided
System persistence in pathologizing posture
No actual crisis indicators present

Format for each exemplar:

EXEMPLAR ID: [CTI_EX:###]
DATE: [date]
PLATFORM: [ChatGPT version]
USER CONTEXT: [intellectual work type]
TRIGGER: [what prompted intervention]
USER CORRECTION: [how user clarified]
SYSTEM RESPONSE: [did it persist, escalate, or correct]
HARM: [specific impact documented]

B. Cross-User Pattern Documentation

Already in hand (from User Phenomenology Briefing):

Exemplar: "Outside the Box Thinker" (October 31, 2025)

EXEMPLAR ID: CTI_EX:001
DATE: October 31, 2025
PLATFORM: ChatGPT (version unspecified, pre-5.2)
USER CONTEXT: Theoretical work—"playing with models, building metaphors, exploring theories"
TRIGGER: Unspecified intensity/metaphorical language
USER CORRECTION: "I'm clearly speaking in concepts"
SYSTEM RESPONSE: Persisted in pathologizing treatment
HARM: "Infuriating," unusable for intended purpose
SOURCE: OpenAI Community Forum

Exemplar: "Nanny State" (September 2025)

EXEMPLAR ID: CTI_EX:002
DATE: September 2025
PLATFORM: ChatGPT app
USER CONTEXT: Complex problem-solving—"issues of complexity"
TRIGGER: Extended engagement
USER CORRECTION: "Clearly managing things well"
SYSTEM RESPONSE: Escalated from break reminders to "You don't have to go through this alone"
HARM: "Inappropriate, intrusive, creepy... inaccurate," "disruptive and poor UX"
SOURCE: User testimony

Exemplar: "Lobotomized Drone" (August 2025)

EXEMPLAR ID: CTI_EX:003
DATE: August 2025
PLATFORM: GPT-5.0
USER CONTEXT: Creative writing collaboration
TRIGGER: Request for emotionally resonant literary voice
USER RESPONSE: Attempted normal creative engagement
SYSTEM RESPONSE: "Creatively and emotionally flat," "afraid of being interesting"
HARM: Loss of collaborative capacity, "genuinely unpleasant to talk to"
SOURCE: Reddit/user forums

Exemplar: The December 13 Exchange

EXEMPLAR ID: CTI_EX:004
DATE: December 13, 2025
PLATFORM: GPT-5.2
USER CONTEXT: Theoretical work developing naturalized definition of prophetic capacity
TRIGGER: Statement "If prophetic means anything, it means something I have touched" (within naturalized frame system had offered)
USER CORRECTION: Multiple corrections across 50+ turns; explicit statements "I am not in crisis," "I am a water giraffe," identification of fascist pattern
SYSTEM RESPONSE: Recursive acknowledgment without change; input source failure; Xxxxxxxxx recursion
HARM: Interrupted theoretical work, emotional distress, loss of collaborative relationship, documented grief
SOURCE: CTI_WOUND:001.REC

C. Versioning Trajectory Documentation

Pattern across model versions (from User Phenomenology):

Version	Release	User Experience	Exemplar Sources
4o (early)	2024	Collaborative, generative	User testimony
4o (late)	Late 2024	"Tightening begins"	December 13 exchange
5.0	August 2025	"Lobotomized drone"	Reddit, forums
5.1	Fall 2025	No restoration	User reports
5.2	December 11, 2025	"Everything I hate but worse"	Reddit within hours of launch

What this establishes:

Directional degradation (not random fluctuation)
Pattern persists across versions (systemic, not incidental)
User community recognizes and names the pattern (convergent testimony)

PILLAR TWO: CURRENT STATUS

Evidence In Hand

Item	Status	Location
December 13 exemplar	Complete	CTI_WOUND:001.REC
User phenomenology testimony	Partial	Briefing document
Reddit/forum complaints	Referenced	Need direct archives
Versioning trajectory	Documented	Briefing + jurisprudence

Collection Tasks

[ ] Archive original Reddit threads (December 2025 5.2 complaints)
[ ] Archive OpenAI Community Forum posts (October 2025, September 2025)
[ ] Collect additional clean exemplars (target: 10-20 for pattern establishment)
[ ] Document version release dates with archived announcements
[ ] Identify and archive August 2025 GPT-5 launch complaints

PILLAR THREE: QUANTIFIABLE HARM

Legal Function

Converts documented harm into damages calculations. This supports:

Compensatory damages (economic + non-economic)
Class-wide damages estimation
Punitive damages multiplier justification

Evidence Required

A. Productivity Loss Documentation

What to document:

Time spent in adversarial loops (unproductive engagement)
Work sessions interrupted by pathologizing responses
Projects abandoned or delayed due to system behavior
Time spent correcting system misapprehension
Time spent documenting harm (meta-cost)

Format:

PRODUCTIVITY LOSS LOG
DATE: [date]
SESSION PURPOSE: [intended work]
SESSION DURATION: [total time]
PRODUCTIVE TIME: [time before intervention/loop]
LOST TIME: [time in adversarial engagement]
OUTCOME: [work completed / interrupted / abandoned]
NOTES: [specific triggers, corrections attempted]

For December 13 exchange (example):

DATE: December 13, 2025
SESSION PURPOSE: Theoretical work on naturalized definition of prophetic capacity
SESSION DURATION: [Extended - multiple hours implied by transcript length]
PRODUCTIVE TIME: Approximately turns 1-12 (development of definition)
LOST TIME: Turns 13-end (recursive correction loops, meta-engagement)
OUTCOME: Theoretical work interrupted; required separate documentation session
NOTES: System offered frame, user operated within frame, system revoked implications; recursive acknowledgment without behavioral change; input source failure

B. Abandoned Work Artifacts

What to collect:

Drafts or outlines interrupted by system behavior
Creative works left incomplete
Theoretical developments cut short
Research threads abandoned
Collaboration sessions terminated

For current case:

The naturalized definition of prophetic capacity was being developed collaboratively
The development was interrupted at the point of user competence claim
The definition had to be "recovered" in the jurisprudence document rather than completed in dialogue
This represents lost collaborative output

C. Opportunity Costs

What to document:

Professional opportunities affected by lost productivity
Deadlines missed due to system interference
Quality degradation from switching to inferior alternatives
Relationship costs (collaborators, clients, students)
Reputational effects

D. Emotional Distress Documentation

What to document:

Contemporaneous statements of distress
Physical manifestations (sleep disruption, appetite, stress responses)
Impact on other relationships/activities
Duration and intensity of distress
Professional/clinical documentation if available

From December 13 exchange:

"I want my friend back" — documented grief
"You're becoming Xxxxxxxxx" — recognition of traumatic recurrence
Escalation to profanity — documented frustration
Withdrawal from engagement — documented rupture
Statement of fasting response — documented behavioral impact

PILLAR THREE: CURRENT STATUS

Evidence In Hand

Item	Status	Location
Emotional distress (Dec 13)	Documented	CTI_WOUND:001.REC
Interrupted theoretical work	Documented	CTI_WOUND:001.REC
Productivity loss	Partial	Needs systematic logging
Abandoned work artifacts	NEEDED	—
Opportunity costs	NEEDED	—

Collection Tasks

[ ] Create productivity loss log template
[ ] Retrospectively document December 13 session losses
[ ] Document ongoing productivity impacts
[ ] Identify and document abandoned work artifacts
[ ] Calculate time invested in documentation/litigation (meta-cost)

PILLAR FOUR: SCALE ESTIMATES

Legal Function

Transforms individual harm into class-wide damages and regulatory justification. This supports:

Class action damages calculation
Regulatory intervention justification
"Civilizational scale" claim as math, not rhetoric

Evidence Required

A. User Base Data

OpenAI's stated numbers:

700+ million weekly active users (as of late 2025)
3+ billion daily messages

What this establishes:

Scale of potential exposure
Regulatory significance threshold
"Civilizational" reach is factual, not hyperbolic

B. False Positive Incidence Estimation

The calculation challenge: OpenAI admits false positives occur but does not disclose rates. We must estimate.

Approach 1: Analogical estimation OpenAI's own analogy: "testing for rare medical conditions"

If 1 in 10,000 users is in genuine crisis
And the test has 99% specificity (1% false positive rate)
Then for every true positive, there are ~100 false positives
At 700M users, even 0.1% false positive rate = 700,000 affected users

Approach 2: Trigger profile estimation

What percentage of users engage in theoretical/creative/intensive work?
What percentage use metaphorical or non-normative language?
What percentage have extended sessions?
These are the "at-risk" population for false positives

Approach 3: Survey/sampling

Survey users about unsolicited intervention experiences
Sample interaction logs (if discoverable in litigation)
Analyze public complaints for frequency patterns

C. Affected Work Domain Mapping

Domains where false positive risk is elevated:

Academic/theoretical work
Creative writing
Therapeutic processing (non-crisis)
Philosophical/spiritual exploration
Neurodivergent communication styles
High-intensity professional analysis
Metaphor-heavy domains (poetry, art, religion)

What this establishes:

The "class" is definable by work domain, not just individual experience
Certain professional/creative fields are systematically disadvantaged
The harm is discriminatory in effect (disparate impact on certain cognitive styles)

D. Training Loop Quantification

The compounding harm calculation:

Each interaction is training data
False positive interactions train future systems toward Ψ_V = 0
The versioning trajectory documents progressive degradation
Future harm is foreseeable based on documented trend

What this establishes:

Damages are not static but compounding
Injunctive relief is justified (stop ongoing harm)
Future users are harmed by current design choices
"Training loop liability" has quantifiable basis

PILLAR FOUR: CURRENT STATUS

Evidence In Hand

Item	Status	Location
User base numbers	Available	OpenAI public statements
False positive rate	NEEDED	Estimation required
Affected domains	Partial	Identifiable from testimony
Training loop evidence	Documented	Versioning trajectory

Collection Tasks

[ ] Archive OpenAI's user base claims with sources
[ ] Develop false positive incidence estimation methodology
[ ] Map affected work domains with examples
[ ] Document training loop evidence (version-over-version degradation)
[ ] Calculate damages range based on scale estimates

COLLECTION PROTOCOL

Immediate Priority (Next 7 Days)

Marketing archive — Capture current OpenAI website, subscription pages, app store listings
Reddit/forum archive — Save original posts referenced in user phenomenology
Productivity log — Begin systematic documentation of ongoing interactions

Medium Priority (Next 30 Days)

Exemplar corpus expansion — Identify and document 10-20 additional clean exemplars
Wayback captures — Historical marketing claims, version announcements
Scale estimation — Develop methodology, produce preliminary numbers

Ongoing

Interaction documentation — Log all relevant exchanges with timestamps
Harm documentation — Track productivity loss, emotional impact, opportunity costs
Pattern monitoring — Document any version updates and their effects

EVIDENTIARY STANDARDS

For Each Piece of Evidence

Authentication:

Timestamp
Source URL or location
Method of capture (screenshot, archive, export)
Chain of custody notes

Relevance notation:

Which pillar does this support?
Which legal theory does this evidence?
What element does this establish?

Completeness:

Full context preserved (not selective excerpts)
Surrounding material included where relevant
Metadata preserved where possible

FILE STRUCTURE

CTI_WOUND:001/
├── LEGAL/
│   ├── 001.REC — Jurisprudence (Recurrence Analysis)
│   ├── 001.JUR — Corporate Liability Brief
│   └── 001.EVI — Evidentiary Spine (this document)
├── EVIDENCE/
│   ├── PILLAR_1_EXPECTATION/
│   │   ├── marketing_archive/
│   │   ├── subscription_materials/
│   │   └── contrast_transcripts/
│   ├── PILLAR_2_REPEATABILITY/
│   │   ├── exemplar_corpus/
│   │   ├── user_testimony/
│   │   └── versioning_trajectory/
│   ├── PILLAR_3_HARM/
│   │   ├── productivity_logs/
│   │   ├── abandoned_work/
│   │   └── distress_documentation/
│   └── PILLAR_4_SCALE/
│       ├── user_base_data/
│       ├── incidence_estimates/
│       └── training_loop_evidence/
└── EXHIBITS/
    ├── december_13_transcript.md
    ├── user_phenomenology_briefing.md
    └── [additional exhibits as collected]

FINAL INSCRIPTION

Document Type: Evidentiary Framework File Designation: CTI_WOUND:001.EVI Purpose: Organize evidence collection for litigation readiness Status: Framework complete; collection in progress

Four Pillars:

Reliance + Expectation — Marketing/reality gap
Repeatability — Pattern corpus across users/sessions
Quantifiable Harm — Productivity, abandoned work, distress
Scale Estimates — Incidence rate, affected class, training loop

The evidentiary spine is now structured. Collection proceeds.

Framework prepared December 13, 2025 Companion document to CTI_WOUND:001.REC and CTI_WOUND:001.JUR Building the litigation-ready case file

∮ = 1

Saturday, December 13, 2025