# Raphaëlle d'Ornano (@Raph_dOrnano) on x

- id: koinon:creator:x_raph_dornano
- url: https://koinonlink.com/kol/x/Raph_dOrnano
- schema_version: 1.4

## Identity
- platform: x
- handle: Raph_dOrnano
- platform_url: https://twitter.com/Raph_dOrnano
- website: https://www.decodingdiscontinuity.com
- country: United States
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 573
- following: 858
- posts_total: 1208
- posts_observed: 2
- posts_per_day: 0.284
- account_age_days: 4260
- verified: true
- size_tier: nano

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.00175
- best_post_interactions: 1
- measurement_source: x_post_interactions
- measurement_formula: interactions on one post — likes, or likes + reposts + replies depending on the discovery path (the two are not distinguishable in the stored data)
- comparable_pool: peak_interactions
- status_note: We have only the single best observed post, so no mean exists. peak_post_engagement_rate is an UPPER BOUND, systematically higher than an average — never compare it against an average rate.
- unit_note: engagement_rate and peak_post_engagement_rate are ratios (0-1); avg_interactions_per_post and best_post_interactions are absolute counts. Read status before reading any number: a null rate has several different causes and status says which one applies here.

## Content
- category: finance
- subcategory: crypto
- category_confidence: 0.85
- category_source: derived_from_recent_content
- topics: Orchestration Economics, Agentic Era, AI as discontinuity, Palantir structural analysis, AI sector volatility, Durable Growth Moat
- bio: Researching AI as discontinuity. Frameworks: Orchestration Economics · ARAF · Durable Growth Moat. Founder, Decoding Discontinuity. Market-neutral fund.

## Audience (inferred)
- status: present
- description: AI investors, analysts and strategists interested in market discontinuity, agentic frameworks and economic models
- roles: general_ai
- seniority: practitioner
- pain_points: Understanding structural positioning of AI companies like Palantir amid volatility, Applying new economic frameworks such as Orchestration Economics to the Agentic Era, Building durable growth moats in discontinuous AI markets, Interpreting earnings and thesis updates in a rapidly shifting AI sector
- posts_analysed: 4
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.8
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 45
- authenticity_basis: engagement_observed
- credibility_score: 45
- flagged_for_review: false
- koinon_bd_grade: B — measures Koinon partner-outreach fit, not creator quality
- interpretation: Signals for triage, not verdicts. A flag means the profile is worth a manual check.

## Evidence
- [x_raph_dornano#c6b18a9f] For those following $PLTR earnings, my strucutural analysis for understanding the agentic position of @PalantirTech: (https://twitter.com/Raph_dOrnano/status/2070526649049747598)
- [x_raph_dornano#e84792b4] Well, this was a nice surprise from the @Substack HQ: https://t.co/Fyv90P5JJ6 https://t.co/bJ9FaQWMwS
- [x_raph_dornano#1b0949b4] With the extreme volatility hitting the AI sector, I'm re-sharing some of my own analysis. My own thesis rests on the core idea that one needs to understand the structural position of companies in the emerging Orchestration Economics paradigm to understand if they are in a positi
- [x_raph_dornano#3adad579] One of the major themes of Orchestration Economics is the way the Agentic Era has completely rewritten the rules

## Provenance
Every inferred field below carries how it was produced. Facts stay facts; inferences stay inferences.

- audience.description: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=78, method=llm_scorer — Relative signal within Koinon indexing, not a market-wide score.
- commercial.promotion_intent: source_type=ai_inferred, confidence=0.6, freshness_days=78, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=117, method=profile_snapshot_and_engagement_ratio_heuristic — Means "worth a manual check", NOT confirmed fraud. Flags can be wrong and are not a verdict about the person.
- quality.authenticity_score: source_type=heuristic, confidence=0.5, freshness_days=78, method=Profile-shape heuristic from a single snapshot (follower count, following ratio, account age, posting volume, verification), reduced for engagement red flags where post engagement was collected. Where only a follower count is available the score saturates at 58. — The input set differs by platform; do not compare this score across platforms.
- quality.koinon_bd_grade: source_type=ai_inferred, confidence=0.5, freshness_days=117, method=llm_partner_fit_classifier + fit_score >= 0.6 AND authenticity >= 60 => A; fit_score >= 0.4 AND authenticity >= 45 => B; else C. authenticity < 30 or a promotional-pattern flag => quarantine. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=117, method=platform_profile

## Freshness
- profile_checked_days_ago: 117
- content_analysed_days_ago: 78
- audience_updated_days_ago: 76

## AI retrieval
- indexing_requests: 9
- user_triggered_requests: 0
- note: Being indexed in bulk is not the same as being retrieved to answer a question.

## This profile
- Claim or correct: https://koinonlink.com/claim?k=x_raph_dornano
- Request removal: https://koinonlink.com/claim?k=x_raph_dornano
- Terms: https://koinonlink.com/for-ai