When AI-Generated Music Actually Works: What Suno's Surprising Viral Moment Means for Creative Teams

---

A Music Critic's Reluctant Admission Is a Signal Worth Paying Attention To

Generative AI has had a credibility problem in creative fields. The outputs are often described as flat, derivative, and soulless — technically competent but missing whatever it is that makes art resonate. So when a seasoned music critic publicly wrestles with the fact that he actually does not hate a song made with Suno, that is worth stopping to examine.

Writing for The Verge on July 19, Terrence O'Brien — who describes himself as someone who generally finds AI-generated music "offensively boring," particularly from Suno — admits to struggling with a track by 1010benja titled "Semiramis Dream," which was created using the platform. O'Brien is careful not to overclaim. He does not declare AI music suddenly good. But the very fact that he is processing genuine ambivalence, rather than dismissing the track outright, marks a small but meaningful inflection point.

This matters beyond music fandom. It matters for anyone running a business that creates content, trains teams, or is weighing where to deploy AI tools in their workflow.

---

Why the "Offensively Boring" Problem Has Been Real

For the past two years, the dominant critique of generative AI in creative domains has not been that it is threatening to replace human artists. It has been that the outputs are too mediocre to bother with. Music from tools like Suno, images from Midjourney's early iterations, and marketing copy from first-generation LLMs all shared a common flaw: they averaged everything into mush.

That averageness is a product of how these models are trained. They optimize for the most statistically likely next token, note, or pixel. The result is content that feels familiar in all the wrong ways — recognizable but hollow, competent but forgettable.

O'Brien's admission suggests that at least some AI music creators are finding ways to work around that limitation, possibly by treating the AI as one instrument in a larger creative process rather than the sole author. That distinction is important.

---

What This Means for Business Teams Using AI in Creative Work

The lesson here is not "AI music is now good, use it everywhere." The lesson is more nuanced and more actionable for teams.

The quality ceiling on generative AI is rising, unevenly. Some creators are finding genuine value in these tools when they treat them as collaborators rather than vending machines. Businesses that have written off AI content tools based on early experiences should be running periodic re-evaluations. The landscape is not static.

Human direction still determines the outcome. The track O'Brien is wrestling with was made by a human artist using Suno — not simply generated by typing a prompt and hitting publish. For business teams, this mirrors what we consistently see in AI tools for business: the best results come when a skilled person is steering the process, not outsourcing judgment entirely.

Creative credibility is slow to build and fast to lose. Critics like O'Brien represent the harder-to-impress segment of any audience. If AI-assisted content is going to earn trust in competitive markets, it has to clear a higher bar than "technically passable." Businesses using AI for marketing, training materials, or customer-facing content should be asking whether their outputs would pass the O'Brien test — would someone who is predisposed to skepticism still find value in this?

The ethical conversation is not going away. AI music tools like Suno have faced significant criticism over training data and artist compensation. Before integrating any generative AI platform into a creative workflow, teams should review the tool's data practices, licensing terms, and public controversy history. This is not just an ethical consideration — it is a reputational one.

---

The Bigger Picture for SMBs

For small and mid-sized businesses, the real opportunity in this story is not about music at all. It is about recalibrating expectations for what AI can do in creative contexts right now. The tools are better than they were. They are not magic. And the gap between mediocre AI output and genuinely useful AI output is almost always filled by human skill and intention.

Teams that learn to work with these tools — guiding them, editing the results, and applying judgment — are building a capability that will compound over time. Teams that either ignore AI entirely or treat it as a replacement for creative thinking are leaving value on the table in different ways.

For businesses looking to build those workflows intelligently, platforms like WRRK.ai are designed to help teams integrate AI tools into real work processes without losing the human oversight that makes the difference between forgettable output and something worth paying attention to.

Original reporting by Terrence O'Brien, published July 19, 2026, at The Verge. Read the original article here.

---

Explore how your team can use AI automation for content workflows to build quality into the process from the start.

---

Frequently Asked Questions

Is AI-generated music getting better?

Based on the reaction from critics like Terrence O'Brien at The Verge, at least some AI-generated music is beginning to clear a bar that earlier outputs did not. However, quality appears to depend heavily on how human artists are directing and shaping the AI's output rather than on the AI tools alone improving in isolation.

Can businesses use Suno for commercial content?

Suno does offer licensing tiers for commercial use, but businesses should carefully review the platform's current terms of service, its history of legal disputes with music publishers, and how its training data practices align with their own ethical and reputational standards before committing to it in a commercial workflow.

How should small businesses approach AI creative tools?

Small businesses get the most value from AI creative tools when they treat them as assistants rather than replacements. Assigning a skilled team member to direct, review, and refine AI outputs — rather than publishing raw generations — produces significantly better results and reduces the risk of low-quality or off-brand content reaching customers.