Africa's Afrobeat Boom Faces a New Threat: Artificial Intelligence

The rapid advancement of artificial intelligence in music production has fundamentally transformed creative workflows and redefined audience interaction with music. As AI becomes increasingly embedded in mainstream media and daily life, it has sparked intense debate across the entertainment sector. The music industry, in particular, faces a surge of AI-generated 'soundalike' tracks epitomized by the viral 'Fake Drake' phenomenon prompting companies to urgently develop strategies to safeguard artist rights, copyrights, and revenue streams against the rapid expansion of artificial intelligence. However, there has also been a growing fear around artificial intelligence, and how it will impact safety and job security, as there is potential for AI-generated voice soundalikes to be used for sinister reasons or the efficiency of AI to take over jobs held by human beings. 

Today, anyone with an internet connection can use generative AI to produce a radio-ready song in under thirty seconds. But as the technical barrier to entry plummets to zero, we are forced to confront a deeper, multi-layered crisis: What happens to the value of art when the labor required to make it disappears, who legally owns the resulting sound, and what happens to the economies built on human rhythm?

In its latest flagship report, Re|Shaping Policies for Creativity, UNESCO issues a stark warning based on data from over 120 countries: the rapid rise of generative AI threatens to devastate artist livelihoods by 2028. The report projects that music creators could lose up to 24% of their revenue, while audiovisual professionals may see a 21% decline, as AI-generated content floods global markets.  UNESCO  stresses that these economic disruptions are accelerating faster than governments can legislate, creating a dangerous policy lag that exacerbates global inequalities and leaves millions of cultural workers vulnerable to income instability and copyright erosion. 

 Music creators could see their revenues fall by 24 per cent, while those working in the audiovisual sector may lose 21 per cent of their income due to the expanding presence of AI generated content in global markets. The report stresses that these disruptions are occurring at a pace that outstrips current policy responses, exacerbating inequalities and threatening the livelihoods of millions of cultural workers.

The shift toward digital production and consumption has created new opportunities but also intensified economic uncertainty. Creators are experiencing heightened exposure to intellectual property violations and diminishing returns on their work as AI generated outputs enter the marketplace.‑generated outputs enter the marketplace.

Here are five of the ways AI is already affecting the music business:  

Revolutionizing Production

Thanks to the increasing portability and affordability of technology, it’s been getting easier and easier to make professional sounding music for decades; an aspiring artist who can afford Apple products can start fiddling around with production on GarageBand, or buy “type beats” online and record vocals with a phone. Even so, increasingly popular AI-driven technology takes a wrecking ball to the already-porous wall separating civilians from musicians.

Users of the app Boomy, for example, can select a few options like Rap Beats or Global Groove and generate an instrumental in seconds that they can then rearrange, re-tool or record a vocal over. BandLab’s SongStarter can generate an instrumental based on specific lyrics and emojis. “Writer’s block is real,” BandLab noted when launching SongStarter in May. “Sometimes, you just need a nudge in the right direction.”

 Getting Stems 

Just as AI technology can help aspiring artists build songs from scratch, it also has the ability to break them up into their component parts, known as stems. Having those audio building blocks can be essential if, for example, a movie wants to use an instrumental version of a track in a film trailer, or a brand wants to incorporate a vocal a capella into a commercial. Some musicians have lost their stems over time; other artists may have cut albums before recording technology existed to isolate all the different parts, and those albums may now be in the hands of catalog owners looking for new revenue opportunities. 

This technology is only likely to become more popular at a time when the music industry is acknowledging the extent to which younger listeners want to manipulate audio on their own, crafting homemade remixes that can earn viral attention on TikTok. “It’s not just, ‘how do in some very controlled way reimagine songs with your people in-house?'” says Jessica Powell, CEO of Audioshake, which created the tech that Jerkins used for his ODB sample. “The next wave of it is how do you bring fans and artists and fans and music closer together? How do you actually give the keys [to a song] over in a way that you’re comfortable with, to really let people go wild with it?” 

The Deluge

The modern music industry was designed in a world where the supply of professional-grade music was fairly limited and largely controlled by a few large companies. But as AI technology gets better and better, it’s now possible to create a torrent of music very quickly. This has caused a fair amount of anxiety at the major labels, who face questions from financial analysts about “market share dilution”: If AI has the capacity to turbocharge the amount of music being made outside of the majors’ purview, it could hurt their payouts under the streaming services pro-rata business model. Earlier this year, JP Morgan’s Sebastiano Petti asked Warner Music Group’s new CEO, Robert Kyncl, “Are you concerned about the dilution of music from AI-generated content?” “AI is probably one of the most transformative things that humanity has ever seen,” Kyncl replied. “It has so many different implications. Because of that, yes, I’m paying very close attention to it.” 

Personalized Soundtracks

A number of start-ups are producing malleable music that morphs in real time to underscore actions in video games, VR, workouts, and Snapchat filters, using cutting-edge technology. Often called “dynamic” or “personalized” music, companies like Reactional Music, Life Score, Minibeats, and others employ artificial intelligence not to generate music at the click of a button, but rather to take human-made music and shuffle its individual elements (called “stems”) around to arrange newfound compositions that best underscore a user’s needs and actions, much like a film score does for your favorite scene. Their work begs the question, “How magical would it be if we listened to music and music listened back to us?” as Lifescore co-founder and CEO Philip Sheppard puts it. 

Pitch Records

Some songwriters and publishers are now experimenting with AI voice synthesis technology to help them place their compositions with top-tier artists. These days, “pitch records” — songs that are written just by professional songwriters and later shopped around to artists to record — can be especially hard to land as more artists want to play a larger role in the song creation process, so AI voice technology has helped tech-forward publishers and writers show the artists’ team what the singer might sound like on a track before they even record it.

While it hasn’t been widely adopted yet, some proponents say this use of AI is a cheaper and more exact alternative to hiring demo singers with voices similar to popular artists, a common industry practice. However, detractors warn this could lessen work opportunities for those demo singers, and replicating an artist’s voice with AI might also scare them away.  

Voices From the Studio Floor

The debate stops being abstract the moment it reaches the people actually making the music. Nigerian producer Eclipse Nkasi built Africa's first AI-powered album, "Infinite Echoes," fronted by a virtual singer named Mya Blue , after using ChatGPT to write lyrics and a separate AI tool to generate the underlying tracks before feeding his own recorded vocals through a voice-conversion model. In an interview with Arise News, Nkasi framed the project as a test of access rather than a rejection of craft: AI, he argued, lets people who understand what good music sounds like but lack a singer's technical skill still make something that matters, without taking away from the work of musicians who built their skill the conventional way. It is a position that sits uneasily next to UNESCO's warning, since the same accessibility Nkasi celebrates is the mechanism the report blames for flooding the market and depressing the value of trained labor.

Lagos producer Jinmi Abduls, who has worked with Oxlade, Teni, and Joeboy through his company Chase Music Nigeria, offers a more measured read on what a "musician" will even mean going forward. He predicts that once a finished song can be assembled from a text prompt, the label "someone who can make music" will stretch to cover almost anyone but he does not treat that as a verdict against trained artists. If anything, he argues, it raises the bar for what musicianship is understood to mean once the baseline of mere production is no longer scarce.

Not everyone in the industry believes resistance is even worth the energy. DJ and producer Diplo, speaking on the Behind The Wall podcast, told host Daniel Wall flatly that artists fighting AI are wasting their time and would be better served mastering the tools than opposing them. The bluntness of that position works with it, because there is no winning against it is precisely the kind of capitulation this piece is arguing against, and precisely the friction the industry cannot afford to lose.

Who Owns AI Music and Does It Feel Real?

The rapid ascent of generative AI has precipitated a dual crisis in the music industry: a legal ownership void and a psychological authenticity deficit.  As algorithms increasingly compose, perform, and produce tracks, the traditional pillars of authorship and emotional connection are being fundamentally eroded. 

The Legal Ownership Void

The question of "who owns the music?" has no single answer in the AI era, creating a precarious legal landscape for all stakeholders. Current jurisprudence, particularly in the United States, has established that prompts alone do not constitute authorship.  According to the U.S. Copyright Office, if an AI system executes the expressive elements of a work based solely on a prompt, the output lacks "meaningful human authorship" and effectively falls into the public domain, meaning no one, not the prompter, nor the developer can claim exclusive copyright. 

This creates a tripartite conflict over value:

• The Prompt Engineer: Often holds only commercial usage rights (via platform Terms of Service) but lacks copyright ownership of the raw audio unless they significantly modify, arrange, or edit the output. 
• The Software Developers: While they own the model, recent settlements (e.g., Suno and Udio with major labels in 2025) suggest they must license training data, acknowledging that their tools rely on the copyrighted catalogs of others. 
• The Training Artists: The thousands of human musicians whose work was scraped to train these models remain largely uncompensated. While new "dataset transparency" standards are emerging, the core tension persists: human creativity is being used to generate synthetic competitors without a robust remuneration framework, exacerbating the economic inequalities highlighted by UNESCO.

The Psychological Boundary of Authenticity

Beyond the legal quagmire lies a deeper, existential challenge: the uncanny valley of emotion.  Despite technical perfection, AI-generated music often triggers a subconscious rejection in listeners. Research indicates that audiences perceive AI music as "accurate" and "immersive" but consistently rate it lower in empathy, nostalgia, and emotional intensity compared to human compositions. 

This phenomenon stems from the "human imperfection effect." Listeners intuitively associate micro-variations in timing, pitch, and breath often deemed "flaws" in a technical sense with genuine human experience and vulnerability.  When an AI generates an "emotionally perfect" song, it lacks the lived context and intentionality that listeners subconsciously scan for. 

Perceived Value and Authenticity in Algorithmic Music 

When audiences learn a song was generated in seconds rather than crafted over years, their appreciation fundamentally shifts due to the effort heuristic. Recent studies confirm that simply labeling a track as "AI-generated" causes listeners to engage less deeply, imagining fewer stories and feeling less emotional connection, even if the music is indistinguishable from human work.  The knowledge that no human struggle or intention went into the creation strips the music of its narrative weight, rendering it "hollow" despite its technical perfection.

This psychological shift accelerates the content-ization of sound, where music risks devolving from a revered art form into disposable, algorithmic background noise. 

• Disposable Consumption: Infinite, effortless generation turns songs into ephemeral commodities rather than cultural artifacts. 
• The Narrative Void: Without a human story of creation, tracks fail to inspire the empathy that drives true fandom.
• Economic Impact: As music becomes perceived as "cheap" and effortless, the audience's willingness to support artists financially diminishes, threatening the livelihood of human creators in a sea of synthetic "sludge."

The African Economic Threat: A Battle for Cultural Sovereignty

The economic stakes of the AI revolution are disproportionately high for Africa. While recorded music revenues in Sub-Saharan Africa surged by 15.2% in 2025 to reach $120 million, this growth faces an existential threat from unlicensed generative AI. Investors are pouring billions into foreign AI infrastructure exemplified by Suno’s recent $5.4 billion valuation while African creators risk losing up to 25% of their royalties annually to synthetic track flooding and streaming fraud. 

The Mechanics of Displacement

The threat operates through two primary channels that directly undermine the region's booming industry:

• Synthetic Flooding: AI models can generate infinite tracks mimicking African genres like Afrobeats and Amapiano, diluting the royalty pool and competing for algorithmic attention without compensating the original artists whose styles were scrapped. 
• Streaming Fraud: Bot networks artificially inflate streams of AI-generated content, siphoning revenue from legitimate artists. This "theft" hits emerging markets particularly hard, where per-stream payouts are already low and every dollar counts toward livelihood. 

The Risk of Cultural Extraction

Beyond immediate financial loss, Africa faces cultural extraction.  If the continent's unique music, languages, and rhythms are harvested as free training data for foreign tech empires, it effectively donates its cultural inheritance to build wealth abroad. This "data colonialism" leaves local musicians economically displaced, forced to compete against algorithms trained on their own voices yet owned by external powers. Without robust local AI governance and fair licensing, the region risks seeing its cultural renaissance monetized entirely by outsiders, stripping value from the very creators driving its global influence.

What African Musicians Are Actually Saying

The abstraction of "cultural extraction" has a face on the ground, and it belongs to working musicians who are watching the terms of their own industry get rewritten without them. Kenyan musician Tabu Osusa has warned publicly that AI's capacity to replicate African sounds without proper attribution amounts to a new form of cultural appropriation, and has raised a quieter concern that gets lost in the revenue figures: that AI tools could discourage younger players from ever learning the traditional instruments those sounds came from in the first place, hollowing out the spiritual dimension of the music along with its market value.

In South Africa, a rights advocate identified only by surname, Hakim, in reporting by Johannesburg-based correspondent Tšeliso Monaheng for OkayAfrica, has described the platforms' terms of service as an inversion of Creative Commons logic: a framework once built to let artists share their work while keeping credit and commercial value now functions, in practice, as artists handing over their work for training data and receiving nothing back. Monaheng's reporting also points to the stalled state of South Africa's Copyright Amendment Bill first introduced nine years ago, twice returned unsigned by the President even after the Constitutional Court  gave Parliament a 24-month deadline to fix it as concrete evidence of the policy lag UNESCO warns about.

The same reporting features South African jazz musician Benjamin Jephta, who is described as carefully optimistic about AI's possibilities and its risks, and who sits inside a recurring industry tension: the suspicion that greets any new technology when it moves from the margins into the mainstream, demanding artists prove their authenticity on the spot or have it questioned. Monaheng's framing is worth holding onto here live musicians are not bystanders in this conversation, they are its raw material, since their recorded catalogs are what the models were trained on to begin with.

Why Human Music Still Matters 

The rise of AI in music has created a clear divide. On one side, technology offers new tools for creation. On the other hand, it threatens the very livelihood of human artists. As UNESCO warns, music creators could lose 24% of their income by 2028 if current trends continue.  This is not just a number; it represents millions of artists struggling to survive as their work is replaced by machines. 

The core problem is simple: AI systems are trained on human music, often without permission or payment. This allows companies to generate endless songs that compete with real artists, driving down the value of human effort. When a song takes seconds to make, listeners may care less about it, turning music into disposable background noise instead of meaningful art. This hurts everyone, but it hits hardest in places like Africa, where booming music scenes risk losing their royalties to foreign tech companies that use their sounds for free. 

However, the future is not yet written. We can choose a path where AI helps artists instead of replacing them. This requires:

• Clear Labels: Listeners should know if a song is made by AI or a human. 
• Fair Pay: Artists must be paid when their music is used to train AI.
• Human Rights: Laws should protect human creators so technology serves them, not the other way around. 

Music is special because it comes from human experience—our joy, pain, and stories. Machines can copy the sound, but they cannot feel the emotion. If we protect the human side of music, we can ensure that art remains a connection between people, not just a product of code. The choice is ours: let AI take over, or use it to make the human voice stronger.