How AI Acts as a Transformative Intermediary, Disrupting IP Flows
Hypothetical Framework — Prepared by Adservio Innovation Lab Olivier Vitrac (former Research Director, Université Paris-Saclay) For internal discussion — November 2025
This memo extends the corporate landscape analysis (Memo 1) by examining how AI models function as intermediaries that transform creative artifacts (music, video, text) in ways that challenge traditional rights detection and royalty flows. The analysis is built on publicly available research and industry reports, but specific impacts on Vivendi's operations remain hypothetical until validated.
In the traditional media ecosystem, rights flow along a relatively linear path:
Traceability: Each node maintains metadata (ISRC, ISWC, catalog numbers)
Signal Integrity: Audio/video files remain largely unaltered (compression notwithstanding)
Detection Reliability: Fingerprinting (Shazam, Content ID) works because signal structure is preserved
Reporting Clarity: Platforms know which work was consumed and how many times
AI models introduce a non-linear, transformative layer between creation and consumption:
| Aspect | Classical Flow | AI-Mediated Flow |
|---|---|---|
| Signal preservation | High (compression only) | Low (pitch, tempo, timbre altered) |
| Metadata continuity | Maintained (ISRC tags) | Often stripped or synthetic |
| Fingerprint matching | Reliable | Unreliable (feature drift) |
| Attribution clarity | 1:1 (work → creator) | N:M (many sources → many outputs) |
| Legal framework | Established (licensing) | Ambiguous (fair use? training rights?) |
Description: AI models take existing recordings and apply transformations:
Pitch shifting (transpose key)
Time stretching (change tempo without pitch)
Stem separation + recombination (vocals from Track A, drums from Track B)
Example Tools: iZotope RX, Moises.ai, Lalal.ai
Impact on Detection:
Fingerprint survival: Low (~30–50% match rate after pitch shift >2 semitones)
Metadata survival: Depends on user diligence (often lost)
SACEM challenge: If a remix goes viral on TikTok, SACEM may not detect the original composition
Description: Apply the "style" of one artist to the content of another:
Train model on Artist A's catalog
Generate new track that "sounds like" Artist A but uses harmonic/melodic ideas from Artist B
Example Tools: Jukebox (OpenAI), MusicLM (Google), Suno, Udio
Impact on Detection:
Fingerprint survival: Zero (entirely new waveform)
Compositional similarity: High (melody, chords may be recognizable)
Legal ambiguity: Is this a derivative work? A cover? Neither?
SACEM challenge: Even if melody is identical, acoustic fingerprint won't match → no royalty flow
Description: Model trained on large dataset (potentially including UMG catalog) generates novel works:
No single "source" track
Output is statistically derived from training set
May inadvertently reproduce melodic or harmonic patterns from training data
Example: Suno generates a "jazz ballad" that happens to resemble a Duke Ellington composition (but not intentionally)
Impact on Detection:
Fingerprint survival: Zero
Metadata survival: None (synthetic output)
Attribution: Impossible with current tools
SACEM challenge: If training data included SACEM-registered works, should model outputs trigger royalties?
Setup:
User downloads a UMG track (e.g., Billie Eilish)
Pitch-shifts it +3 semitones using free AI tool
Uploads to TikTok → 10 million views in 48 hours
Current Outcome:
Content ID may flag it (low confidence match)
TikTok may mute or demonetize the video
But no clear royalty path to UMG or SACEM (match confidence too low)
Revenue Impact (hypothetical):
Standard TikTok royalty: ~€0.002 per stream
10M views → €20,000 potential revenue
Actual payout: €0 (not detected)
Setup:
User trains AI on Drake's voice
Generates Drake "cover" of a Beatles song
Uploads to Spotify (before detection)
Rights Tangle:
Composition: Owned by Sony/ATV (Beatles catalog)
Performance: Synthetic (no human performer)
Master: Entirely AI-generated (no traditional label)
Current Outcome:
Spotify may detect Beatles composition (via melody/lyrics)
But no royalty to Drake or UMG (voice style is not copyrightable... yet)
Sony/ATV may get composition royalty, but at mechanical rate (much lower than streaming master rate)
Setup:
Startup trains generative model on 1 million tracks (including 300k UMG tracks, scraped)
Releases API for users to generate "royalty-free" music
10,000 users generate 100,000 tracks → uploaded to YouTube, TikTok, Spotify
Current Outcome:
No fingerprint matches (all synthetic)
No metadata trail (AI-generated)
UMG and SACEM receive zero royalties
Startup may face lawsuit (but attribution is near-impossible)
Long-term Risk:
If this becomes normalized, SACEM's collection rate could drop 20–40% for composition rights (hypothetical estimate)
Training Data Opacity: Models rarely disclose what was in training set
Causal Ambiguity: Hard to prove Output X was "derived from" Input Y
Transformation Defense: AI companies argue outputs are "transformative" (potential fair use)
Scale: Millions of derivatives make individual enforcement impractical
Recall from Memo 1 that SACEM relies on:
Declarative registration (metadata, ISWC)
Automated detection (platform fingerprinting)
| SACEM Mechanism | Classical Robustness | AI-Era Vulnerability |
|---|---|---|
| Declarative (ISWC) | High (if metadata preserved) | Low (AI strips metadata, synthetic works have none) |
| Automated (fingerprint) | High (acoustic matching) | Low (signal transformation breaks hashes) |
| Platform reporting | Medium (depends on platform diligence) | Low (platforms don't report what they can't detect) |
| Cross-border coordination | Medium (via CISAC) | Very Low (AI-generated content is nationality-agnostic) |
If AI-mediated music grows to 20% of total streams by 2028:
SACEM's detection rate could drop from ~90% to ~70% (missed remixes, style transfers)
Revenue leakage for UMG/publishers: potentially €50–200M annually (across SACEM's €1.4B collection)
Composition royalties (via UMPG) depend on SACEM detection → direct revenue risk
Master royalties (via labels) depend on platform Content ID → same risk
Artist/producer relations: If creators see declining royalties, they may question UMG's ability to protect IP
Film/TV content can also be AI-transformed (deepfakes, re-edits, synthetic dubs)
Sports broadcasting: AI-generated highlights could bypass Canal+ licensing
No unified IP defense: Each Vivendi entity (UMG, Canal+, Gameloft) likely uses different detection vendors
Opportunity: Vivendi could build a group-wide AI traceability layer
While AI poses threats, it also creates strategic opportunities for Vivendi:
Champion industry standards for AI music attribution
Partner with SACEM and EU regulators on pilot programs
Rather than relying on SACEM alone, UMG could license catalog to Suno, Udio, etc. with per-generation fees
Fund research into phase-domain, perceptual hash, or blockchain-anchored fingerprints (Memo 4)
Propose that AI music platforms pay a flat percentage (e.g., 10% of revenue) into a pool distributed by SACEM/CMOs
AI models function as transformative intermediaries that:
Decouple content from metadata (no ISRC, ISWC propagation)
Alter acoustic signatures (breaking fingerprints)
Blend multiple sources (making attribution ambiguous)
Scale infinitely (millions of derivatives overwhelm manual enforcement)
Result: Traditional rights detection (SACEM's hybrid model) is increasingly ineffective, leading to revenue leakage for Vivendi and erosion of creator compensation.
Given that AI is fundamentally altering the topology of content creation and distribution, what organizational and technical measures should Vivendi prioritize to ensure IP remains monetizable over the next decade?
The following memos will explore:
Memo 3: Technical deep-dive into current detection mechanisms and their failure modes
Memo 4: Alternative architectures (phase-domain, watermarking, blockchain) that could restore traceability
Memo 5: Strategic roadmap and pilot concepts for Vivendi
End of Memo 2 Prepared by Adservio Innovation Lab — Hypothetical Framework Contact: olivier.vitrac@adservio.fr