This guy gets it.

AI Makes Music Fun

ByJohn Werner,

Contributor.

 I am an MIT Senior Fellow & Lecturer, 5x-founder & VC investing in AI

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May 10, 2026, 06:25pm EDT

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listening music

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AI is here, and music composition will never be the same.

Countless generations of musicians who created things manually would roll over in their graves at the idea of conjuring strings, piano, woodwinds, brass and percussion out of thin air, not to mention the human voice as a timeless vehicle of inspired sound. Music has been a human endeavor – until now.

New tools are blowing older ones out of the water. One such catalyst is called Suno – and it is, frankly, amazing.

Plug in your written lyrics. Write a quick prompt: minor or major key, rhythm, vocal techniques, and press “Create” and a fully formed song springs instantly out of the ether, with the voice of a singer who never lived. Or, don’t write your own lyrics, just tell Suno what you want the song to be about, and the lyrics will just appear, cadenced and scanned perfectly, in verse/chorus form, like the work of an impassioned genie from Tin Pan Alley.

It’s almost, some would say, too easy, but it’s inspired by a real vision of a new world that works differently than what we had in the first quarter of the twenty-first century.

“Some of the most fun I’ve ever had was making music with my friends,” Shulman said, detailing how an early version of the app on Discord came out of his realization of how universal music is as a human language.

“Every single person in the world is creative,” he said. “Every single person finds enjoyment and fulfillment from making things, and being creative. Everybody loves music.”

Suno, he said, is about playing with music, not just playing music. He mentioned early jam sessions in a co-founder’s basement as another part of the impetus for the model that eventually got made.

“They were bad at the beginning,” he said, of Suno’s fledgling compositions. “You needed really forgiving ears to really call it music.”

But, he noted, people were willing to pay for it, and the thing took off.

Who’s Writing With AI

Ultimately, Shulman said, most users are just regular people, although the pros are also paying attention.

“We increasingly find that huge numbers of professionals also use the product,” he said, citing buy-in from producers as well as songwriters.

No More Piano Lessons?

I asked Shulman, in light of all of this, if people will still write their own music.

He suggested that, in the end, they will, using Suno as a tool, although in reality, it’s possible to avoid almost all of the process and let the AI do it. As a power user with enough credits, one person could generate a fully formed album every ten minutes or less.

Shulman cautioned against applying the same old analysis to a new world.

“We should not do the old thing with the new technology,” he said. “We should do something entirely new. Music is communication, it’s a language, it’s meant to be between people, not just a solo thing. There’s a reason it’s part of every religion, these things are inherent to our biology, and they should be leaned into, not shied away from.”

A Chance to Express

Explaining how people think about Suno’s product within the company, Shulman pointed out a fundamental principle of commerce, that at the top of the user pyramid is fulfillment.

“There’s not enough fulfillment in people’s lives,” he said. “There’s too much doomscrolling, there’s too much paranoia.”

Ultimately, he said, he feels like tools like Suno are an “under-rated” part of the new economy, partly because they allow people the agency to create something for themselves.

I thought that was a pretty good argument. We talked about MTV, Pandora, and what the “new radio” will look like. I mentioned visiting the orchestra pits of L.A. studios, and the venerated role that music plays there. I thought this was one of the most interesting use cases that came out of the April summit. Do you make music? Have you used this tool? What do you think? Drop me a comment, and let me know.

www.forbes.com/sites/johnwerner/2026/05/10/ai-makes-music-fun/

I think most of us agree that transparency is the best policy. The only people trying to hide the AI origin of their music are the slop creators. But what these people are advocating is tracing back to the exact training data. That's like copyright claiming inspiration. If the courts declare that Suno's training methods constitute fair use, all of this becomes a moot point.

Why AI Attribution Matters for the Music Business — And How It Can Become a Reality (Guest Column)

As the industry enters a new era, creating workable attribution systems will benefit everyone in the equation — from artists to AI music companies. 

By  

Adrian Perry and Nicole Canales Courtesy of Covington & Burling LLP

AI attribution is the key to unlocking the limitless opportunities available in a music world that seeks to embrace generative AI technology. It can calm artists’ fears around compensation and unauthorized use. It can reduce litigation risk and increase profits for platforms that provide generative AI music products. And it can give rights owners more certainty on license scope, plus more nuanced and enhanced revenue shares. In short, if designed and deployed the right way, AI attribution can be a boon to the entire generative AI music ecosystem.

What is AI Attribution in the Music Industry?

In the context of generative AI and music, attribution refers to the process of attempting to trace which training inputs contributed to a given AI-generated output (and in some cases, how much each input contributed).

For artists, copyright owners and platforms alike, attribution offers what the industry has demanded since the early days of generative AI: transparency. By shining a light into what was once a black box, attribution gives copyright owners visibility into whether and how their works are being used by AI systems.

It also creates the potential for more customized compensation opportunities. Where licensing arrangements exist today, they generally take the form of upfront fees or revenue-sharing models that are not tied to the actual contribution of any specific work. Attribution, in theory, changes that calculus by enabling compensation to be linked to traceable impact.

The potential upside is industry-wide. For AI developers, attribution could make licensing discussions less contentious. It is much easier to negotiate with artists, labels and publishers when there is a credible way to offer visibility into how works are used and how value is tracked. More transparent systems could also lend greater comfort to investors mindful of the legal exposure of generative AI platforms in today’s climate. Plus, this creates an opportunity to have more targeted data about what music fans and music creators find most useful in generative AI products.

Importantly, attribution offers more legitimacy. Platforms can point to sourcing and compensation mechanisms that are more trustworthy and easier to build on. Music fans and creators, and copyright owners, are more likely to partner with or use generative AI music platforms if they believe the underlying system can explain where the value came from and whether the relevant rights were compensated.

The Attribution Landscape Today

In recent years, a range of technical methods have been proposed to investigate whether particular training materials may have influenced a given AI-generated output. Some methods compare generated outputs to candidate training materials in a separate database to identify similarity or proximity. Other methods examine the AI model itself, using signals from the model’s parameters to estimate whether, and to what extent, particular training materials contributed to a given output. Watermarking training content is another method, where the presence of a watermark in the output has been suggested to indicate which specific training materials contributed to it.

At present, however, none of these methods yield answers about influence with certainty. Some rely on probabilities, while others may find correlations, which does not necessarily mean causation. Often the results depend on underlying assumptions and, in some cases, on access to technical information about the AI model that may not be available in practice. Some options rely on significant computing power, which can be expensive.

Because current attribution methods carry various limitations, it may take time before any single approach is widely adopted across the industry, or before effective hybrid approaches that combine the best of these methods emerge. That doesn’t mean that these technologies shouldn’t be used. As long as everyone understands the limitations, the benefits of these technologies merit development because the better they get, the better the commercial opportunities will be in the music space. And the technology won’t improve at the pace users will expect without actually using them, perhaps in limited data environments (“sandboxes”) to mitigate risk. In the meantime, as these technologies evolve, several questions matter from a commercial contracting perspective.

Where the Rubber Meets the Road: Questions Worth Asking in Your AI Music Deals

Capability – Before the attribution data is used to support payment or other rights-sensitive decisions, the parties should first ask: Is the attribution system capable of identifying the kind of influence you intend to compensate? This is important to ask because, in practice, an attribution tool’s capabilities (and its limitations) become part of the parties’ commercial bargain. Consider a hip-hop producer who licenses his catalog to an AI platform under an agreement intended to compensate him when the model draws on his unique production style. The model later generates a track in a new genre that incorporates his distinctive production signatures, but the output sounds nothing like any particular recording in the producer’s catalog. An attribution tool that looks for close resemblance to recordings in a reference database may return no close match to the producer’s catalog (or low confidence), potentially leaving his contribution uncompensated despite the parties’ intent to the contrary.

Auditability – If attribution data will inform payments to copyright owners, there should be visibility into how the system works and how the results are produced. What documentation exists to describe the attribution methodology? Are independent audits permitted or even possible with respect to these technologies?

Liability – Current attribution technologies are still developing and, like any technology, are not immune from error. If the attribution system over-credits, under-credits, or fails to trace the influence or credit at all, what are the consequences? Who should bear the cost of investigation and resolving these claims? Is there a process for dispute?

Data ownership – the data generated about how often each work in a training set influenced an output, in what context, to what degree, and other considerations could have secondary value for everyone in this ecosystem. The data can reveal which training content is commercially relevant or stylistically influential, which may be useful for A&R professionals in picking which artists and songwriters to sign; copyright owners and generative AI music platform developers in what content should be most valued in licensing; and to anyone interested in learning more about what music fans and creators find most relevant in their worlds. Who owns this attribution data and any derived analytics? Can this attribution data be leveraged for other purposes?

While AI attribution technology may feel like a nuts-and-bolts topic, it is worthwhile for the various music and AI stakeholders, creators and technologists alike to engage with its development and work cooperatively to maximize its efficacy. Implementation of reliable attribution technology could be a boon to the entire music and AI ecosystem, helping the commercialization market mature and bringing both more certainty and higher revenues to its constituents.

Adrian Perry is a partner at global law firm Covington & Burling, co-chair of its Entertainment and Media Industry group, and a driving force behind the firm’s artificial intelligence transactional and advisory work.

Nicole Canales is an associate at Covington & Burling who advises on transactional matters across the firm’s technology and music industry practices.

https://www.billboard.com/pro/ai-music-attribution-matters-make-it-reality-guest-column/