What the “nearly 300 AI licensing” report actually says Multiple sources consistently describe the same underlying research commissioned by the British Phonographic Industry (BPI) .

orpmarketing
Jun 23
8 min read

1.

Number of deals:
- There are 274 commercial AI-related licensing agreements between rightsholders and AI developers across the creative industries as of early 2026. This is widely summarized as “nearly 300 AI licensing deals.”[1][2][3]
Scope and type of deals:
- Deals are spread across music and other creative sectors (film, images, publishing, etc.)[1][3].
- Most are for generative AI systems trained on creative works (music catalogs, lyrics, images, etc.).
- Some cover content identification and rights-management tools rather than pure “training” licenses[1].
Key industry players involved (examples):
- Major labels and publishers: Sony Music Entertainment, Universal Music Group, Warner Music Group, Kobalt, Merlin, Believe[1][3].
- AI / tech companies: Suno, Udio, ElevenLabs, Klay Vision, ProRata.ai, Vermillio, BandLab, Splice[1][2].
- Platforms/services: Spotify and others in related rights/tech deals[1][2].
Adoption patterns among music companies (from BPI member survey)[1][2]:
- Only 5% of small companies and 25% of medium-sized companies have completed an AI licensing agreement so far.
- 16% of surveyed independent companies are actively exploring AI licensing partnerships.
- Yet 77% say they would be open to licensing their music for what they consider “ethical” AI uses.

Fact check:The headline “nearly 300 AI licensing deals” is accurate. The true number is 274 documented, commercial AI-related licensing agreements worldwide by early 2026.

2. Why is this AI licensing boom happening?

2.1 Immediate drivers

Explosion of generative AI toolsMusic, voice, and image models (e.g., Suno, Udio, ElevenLabs, other text-to-music platforms) need large training datasets composed of copyrighted works. Using them without permission has triggered high‑stakes legal risk, pushing AI companies to seek licenses instead of scraping freely[1][2].
Lawsuits and legal pressure
- Major labels have sued Suno and Udio for allegedly copying recordings without permission to train their systems[2].
- Musician unions and rights organizations are now suing or challenging AI licensing deals if they are opaque or unfair[2].
  
  This legal pressure encourages formal, documented licensing agreements instead of “grey-area” use.
Industry fear and opportunity simultaneously
- Rightsholders fear a future where AI-generated music competes with human artists using their own catalog as fuel.
- At the same time, they see new revenue streams if training and AI outputs are properly licensed and monetised.
  
  Hence, the push towards structured licensing frameworks rather than an outright ban on AI.

2.2 Why now, structurally?

The streaming playbook is repeating

The trajectory looks similar to the early 2000s:
- Phase 1: Unlicensed use / piracy (Napster era → now AI-scraping era).
- Phase 2: Litigation, public outrage, regulatory debates.
- Phase 3: Mass licensing frameworks (Spotify/Apple Music then; AI platforms now).
Regulators and parliaments are focused on AI & copyright

Government reports and parliamentary committees are examining:
- Whether and how to allow text/data mining exceptions.
- How to ensure consent, compensation, and transparency for creators[3].
Commercial AI needs “clean” training data to attract investors

Large AI models that can prove their training data is properly licensed become more investable and safer for big clients (labels, film studios, broadcasters). This commercial pressure is a key driver of the nearly 300 licensing deals.

3. How does this affect upcoming African artists?

Your wording (“how does this after upcoming africa artist”) almost certainly meant “how does this affect upcoming African artists?” I’ll answer on that basis.

The BPI-linked report itself is global and not Africa-specific, but related African-focused articles and events make the implications clear for upcoming/emerging African artists.

3.1 Risks and threats

Unauthorised use of African music in AI training
- Generative AI models are being trained on huge internet scrapings which include African sound recordings, lyrics, and metadata often without permission or payment[4].
- South African and Nigerian artists have already discovered AI-cloned tracks, remixes, and voices resembling their work circulating online, with no clear licensing or royalties attached[4][5].
Weak IP protection in many African markets
- In practice, enforcement of copyright in much of Africa is weaker than in Europe/US.
- That makes African content a “soft target”: cheap to scrape, hard to defend.
- If global AI companies continue to sign deals mostly with major Western labels and publishers, African artists risk being heavily used but barely paid.
Royalty leakage and loss of bargaining power
- Estimates suggest that unregulated, unlicensed generative AI could divert up to 25% of global creator royalties every year (about €8.5bn / ~R165bn annually)[4].
- Most African artists already rely heavily on revenue earned outside the continent (streams and licences in Europe/US). If AI cannibalises these revenues, upcoming African artists face:
  - Less sync and licensing opportunity.
  - Lower streaming revenue.
  - More competition from “AI tracks” in their own genres.
Catalog flooding and visibility issues
- Catalogs on platforms like Spotify are already enormous. As AI-generated tracks flood these services, human-created songs—especially from newer, smaller African acts—may become:
  - Harder to discover by algorithms.
  - Crowded out in “soundalike” playlists by AI content with similar tags.

3.2 Opportunities (if Africa moves strategically)

New AI royalty stream
- If African rights-holders can participate in AI licensing, their works used in training or in AI-generated outputs could generate new recurring royalties:
  - Training-use fees (for inclusion in training sets).
  - Ongoing royalties where AI outputs draw measurably on their works (via attribution tech such as Creative Weight Attribution)[4][6].
Lower production and marketing barriers for emerging artists
- Properly used, AI tools can help upcoming African artists:
  - Produce pro‑level demos with AI-assisted mixing/mastering.
  - Generate visuals, marketing content, and multilingual subtitles cheaply.
  - Analyse audience data to target marketing more effectively.
- This can offset some structural disadvantages (smaller budgets, limited access to big studios or PR).
Africa-led AI models and cultural sovereignty
- Projects like the Wits AI & African Music pilot (artist–engineer teams from 7 African countries) explicitly focus on using AI to preserve and reimagine African musical traditions, with attention to ownership and licensing[7].
- Local projects such as Sona in Cape Verde are building AI tools that learn from local music and are governed locally, so that African artists control what is used and how[5].
- This can create Africa-centric AI infrastructure, rather than leaving African music to be processed and monetised solely by non-African companies.
Policy and negotiating window is still open
- Because only a minority of smaller companies have signed deals (5% of small, 25% medium)[1][2], there is still time for African policymakers, CMOs, and industry bodies to set conditions that protect upcoming artists before AI licensing becomes fully standardised on Western terms.

4. Practical “way forward” – what should be done?

Below is a concrete, role‑based roadmap tailored to upcoming African artists and stakeholders.

4.1 For upcoming / emerging African artists

A. Protect your rights in writing

When you sign any contract (label, publisher, distributor, management), explicitly address:
- AI training rights – can your recordings or compositions be used to train AI? On what terms?
- AI output rights – can your voice, likeness, or style be cloned or emulated?
- Revenue share from AI – specify that if your work is licensed for AI training or used in AI outputs:
  - You must give consent; and
  - You receive a defined royalty share (do not leave it vague).
Avoid blanket contract language that says your label can exploit your work “in any technology now known or hereafter devised” without additional compensation. Push for carve‑outs or additional payments for AI-related uses.

B. Join and use local CMOs and guilds

Register with your country’s collective management organisations (CMOs) (e.g., SAMRO, CAPASSO, COSON, etc.) so that:
- They can negotiate AI licenses collectively, including for upcoming artists.
- You are included when new AI royalty streams (training fees, usage royalties) begin to flow.
Participate in surveys, town halls, and conferences (e.g., Africa Rising Music Conference, Music Imbizo) where frameworks are being discussed so upcoming artists’ interests are heard.

C. Use AI tools strategically—not naively

Utilise AI for:
- Drafting demos, arrangement ideas, stems.
- Improving sound quality (mastering assistants).
- Marketing assets (visuals, captions, language versions).
Avoid uploading your full, unreleased masters or detailed stems to experimental AI platforms that take broad rights in their terms of service.
Clearly label what is human‑performed vs AI‑assisted in your own work to maintain brand trust and authenticity.

4.2 For African CMOs, labels, and industry bodies

A. Negotiate Africa-wide, creator‑first AI licensing frameworks

Work with initiatives like the Berlin AI Think Tank and Creative Weight Attribution (CWA) model presented at ARMC to build:
- Consent-based licensing: no AI training on African catalogs without explicit permission.
- Two-layer compensation model:
  - Training-use fee for including works in datasets.
  - Ongoing output-based royalties where particular songs, voices, or styles measurably influence AI outputs[4][6].
Ensure any deal explicitly:
- Includes indies and upcoming artists, not just big catalogs.
- Has transparent reporting on which works are used and how.

B. Build or adopt detection and attribution technology

Partner with tech institutes (e.g., Fraunhofer IDMT, African universities) to:
- Detect when African recordings or voices appear in AI outputs.
- Trace usage back to the original rights-holders.
- Feed this into licensing and royalty distribution systems.
Encourage platforms to label AI-generated content clearly so human artists’ works are not confused or diluted.

C. Educate and support artists

Run regular workshops at conferences like Music Imbizo, Africa Rising Music Conference, and local events on:
- AI & copyright basics.
- How contracts handle AI.
- Opportunities and risks of AI music tools.
Provide template contract clauses that upcoming artists and small labels can use to protect themselves regarding AI.

4.3 For African governments and regional bodies (AU, SADC, ECOWAS, etc.)

A. Strengthen copyright and personality rights

Update laws to:
- Recognise and protect voice, likeness, and style (personality rights) against unauthorised AI cloning.
- Require explicit consent for using protected works in AI training datasets, not just vague “text/data mining” exceptions.
- Introduce meaningful penalties and enforcement mechanisms for unauthorized AI use.

B. Invest in Africa-led AI and creative infrastructure

Fund and support projects like:
- Wits AI & African Music Project and similar initiatives in other regions.
- Africa-wide registries of works and rights-holders to make licensing easier and more transparent.
Anchor AI-related policy discussions in African creative realities, not just imported EU/US models.

C. Coordinate regionally

Use bodies like the African Union and regional trade blocs to:
- Develop Pan-African standards for AI licensing in music and the wider creative industries.
- Negotiate framework agreements with major AI companies on behalf of African states, so individual artists are not bargaining alone.

5. Direct answer to your question

Facts:The “REPORT: NEARLY 300 AI LICENSING” headline is factually grounded: as of early 2026, 274 commercial AI licensing deals between AI companies and creative rightsholders have been documented globally[1][2][3].
Reasons:The boom is driven by:
- Explosive growth of generative AI needing legal training data.
- Lawsuits and regulatory pressure against unlicensed scraping.
- Industry efforts to turn a serious threat into a structured revenue stream through licensing.
How this affects upcoming African artists:
- Negative if unmanaged: greater risk of their music and voices being scraped and cloned without pay; possible loss of a significant share of future royalties; more competition from AI-generated soundalikes; weak IP regimes make it hard to fight back.
- Positive if managed well: potential new royalty streams, access to affordable AI tools that level the production/marketing playing field, and opportunities to shape Africa-led AI models that respect local culture and rights.
Way forward:The path forward for upcoming African artists is to:
- Protect their rights in contracts and join CMOs.
- Use AI tools strategically while avoiding giving away broad rights.
- Work with industry bodies and policymakers to build consent‑based, transparent, Africa‑centric AI licensing frameworks that include training fees and ongoing royalties, backed by detection and attribution technology.

If you tell me which country you’re in and whether you’re a performer, producer, or songwriter, I can turn this into a short, concrete action checklist tailored to your situation.

References

ORP Digital Marketing Agency