Fender CEO Edward “Bud” Cole has put the company back in a difficult conversation with guitar players after comments about AI and music resurfaced from a T3 interview tied to the 75th anniversary of the Telecaster.
The remarks landed at a tense moment. Fender had already angered parts of the guitar community by sending cease-and-desist letters to builders, claiming copyright of the Stratocaster body shape. Some influential guitar YouTubers have said they are done buying Fender gear, and Cole’s comparison between musicians and AI has given critics another reason to push back.
What Cole said about AI music
Cole’s broad argument, as presented in the T3 feature, is that AI is not a new force in music. He said, “I think AI has existed in music as long as there’s been recorded music,” and connected that idea to the way players learn from songs before writing their own.
He also described cover music as a kind of precursor to AI assistance. “I actually believe cover music has been sort of analog AI for a long time,” Cole said. In his account, learning songs by admired artists can become a path into playing and eventually creating.
Cole pointed to his own listening history as part of that process: “I listened a lot to REM, U2, The Smiths and The Cure, and at some point I got sick of just listening to them. I wanted to play it, so I learned to play guitar.”
The most disputed part of the interview came when the idea moved from cover songs to collaboration. According to the source article, Cole suggested that early songwriters can lean on bandmates as another analogue form of AI. A chorus or riff might be expanded by a drummer or bassist, turning a fragment into a song.
Why guitar players objected
The backlash is not only about whether AI tools can help musicians. It is about what gets lost when a human collaborator is described in the same frame as a model responding to data.
The source article argues that Cole appears to be comparing a person learning a limited set of cover songs with an AI system ingesting enormous datasets of copyrighted music. That comparison is central to the dispute because scale changes the question. A guitarist learning influences is not doing the same thing as a generative AI model trained on a body of music suspected, in the case of Suno, to be in the millions.
There is also the matter of human decision-making. Songwriting is not only the recombination of prior inputs. It includes instinct, taste, physical limits, accidents, emotional reaction, and the small choices an artist makes while trying to solve a musical problem.
That is where the bandmate comparison becomes especially sensitive. A drummer or bassist brings life experience, technique, limits, preferences, and judgment into a rehearsal room. The source article frames that as fundamentally different from a chatbot, which does not have taste or instincts in the way a human musician does.
The cover song comparison has limits
Cover songs have long been part of how musicians learn. A player can absorb chord shapes, timing, song structure, tone, and arrangement choices by studying work they admire. Cole’s comments place that learning process near AI training, but the objection is that the two processes do not match cleanly.
A human player cannot perfectly reproduce everything they study. Steve Onotera, better known as Samurai Guitarist, is cited in the source article for pointing out that a player’s physicality and small mistakes prevent exact replication of someone else’s work. Those imperfections can become part of the player’s voice.
That distinction matters because the creative value often comes from the friction. A person may mishear something, adapt a part to their hands, react to a mistake, or compensate for what they cannot play. Those steps are not just errors; they can become the path toward originality.
AI music tools, by contrast, are described in the source article as producing an output from a prompt and a network of data points. The issue for critics is that this can flatten the difference between learning through practice and generating through computation.
The bigger question is skill
Cole also argued that AI could help create new guitar players and support people as they move toward songwriting mastery. His comment was: “I believe that AI is actually going to help create a whole new world of guitar players that use it. To help connect with other musicians, to be more productive. And across the chasm into becoming a student of songwriting to a master of songwriting.”
The source article sharply rejects that idea, pointing to mounting evidence that relying on AI tools is leading to deskilling. The concern is straightforward: a tool that suggests rhymes, metaphors, or musical ideas may help someone produce an output, but that is not the same as practicing songwriting.
Songwriting skill develops through repetition and judgment. The source cites the familiar adage that a writer may need to write 100, or 1,000, or 10,000 bad songs before writing a good one. The point is not the exact count; it is that taste and craft are shaped by doing the hard work often enough to recognize when something actually works.
That is why Cole’s remarks have become more than a stray interview controversy. For many guitar players, the issue is whether a major guitar company understands the difference between useful tools and the lived, physical, imperfect process that makes musicianship meaningful.