D'Addario admits demo video was created with Suno after weeks of denials

D'Addario, the string manufacturer, admitted over the weekend that it used the generative music model Suno to create the soundtrack of a promotional video for a new extended-range guitar string set. For close to two weeks the company denied the claims circulating online and offered a series of alternative explanations, including low-quality export, digital noise from plugins such as Autotune, and AI-based mastering tools from LANDR and Logic. After evidence accumulated, the company edited its original Instagram post and posted: "We were wrong."

The video was intended to demonstrate a string set designed for particularly low tunings, but users quickly spotted suspicious characteristics in the recording. The situation escalated when D'Addario released clips it said were from the original Logic session, yet the melodic and rhythmic differences between those clips and the final version were too pronounced. Guitarist and YouTuber Rhett Shull received the original project files directly from the company, and his analysis indicated that Suno was likely used to regenerate the original recording, not merely to process it.

In the edited post D'Addario stated that "Suno Studio was used to regenerate the original track", apologized for the misleading information it had shared, and declared that it does not support music generated by artificial intelligence. The company pledged to require creative employees and partners to disclose any use of generative tools and to enforce tighter oversight of outgoing material. Simultaneously, it apologized for aggressive moderation that included deleting critical comments and blocking users, a step that intensified community anger.

The incident adds to a series of AI-related disputes in the guitar industry, following Fender CEO’s comparison of rhythm players to "analog artificial intelligence", a remark that sparked sharp criticism from musicians. The D'Addario case illustrates how quickly deception can be uncovered when the technical community can analyze project files and detect generative model fingerprints, and how costly the reputational damage can be when a company attempts to hide such usage instead of disclosing it upfront.