# Patrick Amiel (@patamiel) on x

- id: koinon:creator:x_patamiel
- url: https://koinonlink.com/kol/x/patamiel
- schema_version: 1.4

## Identity
- platform: x
- handle: patamiel
- platform_url: https://twitter.com/patamiel
- website: https://honeylab.fr/
- country: France
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 17885
- following: 1534
- posts_total: 17030
- posts_observed: 6
- posts_per_day: 2.472
- account_age_days: 6889
- verified: true
- size_tier: koc

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.00006
- 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: software
- subcategory: ai_tools
- category_confidence: 0.95
- category_source: derived_from_recent_content
- topics: Frontier AI model training costs, Startup revenue-per-employee metrics, European data center and infra funding rounds
- bio: Serial entrepreneur & investor ✖️ Building & Backing Startups across Europe ↔ LatAm/US ✖️ #21 BA France 2025.

## Audience (inferred)
- status: present
- description: Tech entrepreneurs, startup founders and investors tracking AI infrastructure costs, valuations and European funding rounds
- roles: general_ai
- seniority: decision_maker
- pain_points: Exploding AI training costs pricing out all but the largest players, Benchmarking startup efficiency against outliers like Cursor, Identifying which European tech rounds are actually infrastructure-driven
- posts_analysed: 3
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.6
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 67
- authenticity_basis: engagement_observed
- credibility_score: 82
- flagged_for_review: false
- koinon_bd_grade: A — 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_patamiel#386349c9] En 2023, entraîner un modèle frontier coûtait ~$100 millions. En 2026 : $5 milliards. Multiplication par 50 en 3 ans. Seuls 5-6 acteurs au monde peuvent se le permettre : OpenAI, Google, Meta, Anthropic, xAI, peut-être Mistral. En Europe, un seul candidat — et il est français. Pa (https://twitter.com/patamiel/status/2070523298295763377)
- [x_patamiel#f6bb5baf] Cursor : 50 employés. $4 milliards de CA en 4 ans. $80 millions de revenus par tête. À titre de comparaison : Google fait $1,8M par employé. Goldman Sachs $1,3M. McKinsey $600K. Cursor fait 44x Goldman Sachs par personne. C'est probablement le ratio de productivité le plus élevé 
- [x_patamiel#915fd763] Les 3 plus gros rounds tech en Europe au Q1 2026 ? Des data centers et de l'infrastru

## 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=67, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=67, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=67, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=77, 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=77, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=111, 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=77, 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=111, 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=111, method=platform_profile

## Freshness
- profile_checked_days_ago: 111
- content_analysed_days_ago: 77
- audience_updated_days_ago: 67

## AI retrieval
- indexing_requests: 7
- 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_patamiel
- Request removal: https://koinonlink.com/claim?k=x_patamiel
- Terms: https://koinonlink.com/for-ai