I’m extremely conflicted about Suno the AI music generating site. And I mean that in the most literal sense. On one hand, I can easily imagine myself getting really into it. Over the years, I have generated—there’s that word again—a ridiculous amount of useless song lyrics that were never going to become actual songs. They were little verses, choruses, hooks and fragments that amused me for a while and then simply sat there because I don’t have a band, a producer or a recording studio. Historically, that was where the process ended. You could write all the lyrics you wanted, but unless you had some musical ability or knew the right people, they remained lyrics rather than songs. Suno changes that equation in a way that is enormously tempting to me.

The appeal is obvious. I could sit down, write lyrics about artificial intelligence, heartbreak, Richmond, the end of the world or whatever other subject happens to be rattling around in my head, and then actually hear something resembling a finished song come out the other side. That’s kind of amazing. For someone who has written a lot of lyrics without ever really having a practical way to turn them into music, a tool like Suno suddenly closes the gap between writing words and hearing a song. I can absolutely see myself losing an afternoon to that.

And yet I also kind of hate what this technology could do to music.

The nightmare scenario is obvious: the music industry gets absolutely flooded with AI slop. Not thousands of songs, but millions upon millions of songs, generated cheaply and instantly in every imaginable genre. Songs engineered to imitate whatever style is currently popular. Songs designed to game recommendation algorithms. Songs that sound enough like Taylor Swift or Radiohead or Fleetwood Mac to scratch the same itch without technically being copies. At some point, the supply of music could become effectively infinite, and I’m not sure anybody really knows what happens to culture when that occurs.

The problem, of course, is that bad AI music isn’t much of a threat. If it sounds fake, derivative or just plain awful, then human musicians don’t have much to worry about. The much more interesting—and much more unsettling—possibility is that AI-generated music becomes genuinely good. That’s why a song like “I’m Upping My P(doom),” as interpreted by Mexicat, gets my attention. It’s actually a bop, as the kids say. And I find that much more disturbing than if it sucked, because once AI can consistently produce music that people genuinely enjoy, the conversation changes completely.

At that point, one of the obvious secondary effects would be some kind of fusion between a music generator like Suno and a streaming service like Spotify. I don’t necessarily mean those exact two companies, but the model seems inevitable. Spotify already knows what you listen to, what you skip, what you replay and what genres you suddenly become obsessed with for two weeks. It probably has a reasonably good model of your taste already. Add generative AI to that and suddenly Spotify doesn’t merely have to recommend music to you. It can make music for you.

Imagine opening Spotify a few years from now and finding a song that didn’t exist ten seconds earlier. The system knows that you like a certain tempo, a certain kind of vocal, a certain era of production and a certain emotional tone. Maybe it knows you’ve been listening to a lot of early Talking Heads lately, or melancholy Radiohead, or 1980s synthpop. Instead of searching its catalog for something close to what you might want, it simply generates a new song tailored to those preferences. If you don’t like it, you skip it, and the next one is adjusted accordingly.

That would represent a pretty profound change in the relationship between audiences and music. Historically, an artist created something first and an audience discovered it afterward. Even Spotify’s recommendation algorithm still operates on that basic model. The song already exists. The system merely tries to figure out whether you might enjoy it. Generative music reverses that relationship. The listener comes first and the song comes afterward. Instead of asking, “Which existing song would this person like?” the system asks, “What song should exist right now for this person?”

That is both fascinating and deeply weird.

It also raises another possibility that I think could become important: some of these bespoke songs might escape their original audience. Imagine an AI generates a ridiculously catchy song for one person. That person shares it with friends. Somebody posts it online. Suddenly millions of people are listening to a song that was originally created for an audience of one. There is no particular reason that couldn’t happen. In fact, if enough personalized music is being generated, you’d expect at least a few songs to accidentally turn out to have mass appeal.

Celebrities could make this even stranger. Imagine Taylor Swift using generative music tools to create songs for herself, perhaps about things happening in her life. Maybe she generates dozens or hundreds of little experiments that nobody else ever hears. A song about Travis Kelce. A song about some argument. A song about waking up in Paris. A song based on something funny one of her friends said. Most of them remain private, but every now and then one of them absolutely bangs, so she releases it.

Would Taylor Nation care that an AI had played a major role in generating the song? Maybe. But maybe not as much as we assume. Part of Taylor Swift’s appeal has always been autobiographical. Fans aren’t just listening to a melody; they’re listening for clues about her life, her relationships and her emotional state. If Taylor released a song and said, “This is what I was thinking about last Tuesday,” I suspect a lot of people would still be fascinated by it, regardless of how much of the music itself was generated. At that point, the song becomes almost like a social-media post with a chorus.

That could create an entirely new category of popular music: songs that begin as personalized artifacts and later become public entertainment. And once again, I can’t decide whether I find that thrilling or horrifying.

That’s the problem I keep running into with generative AI in general. I can imagine all these wonderful creative possibilities at exactly the same time that I can imagine the technology bulldozing the economic ecosystem that allows human artists to make a living. Those two ideas aren’t contradictory. I could personally get enormous enjoyment out of Suno while also believing that Suno, or technologies like it, might be disastrous for working musicians. In fact, that tension is probably the most honest way for me to think about it.

What worries me most isn’t that AI-generated music will remain an inferior substitute that audiences grudgingly accept because it’s cheap. The more unsettling possibility is that it becomes really good. Good enough that you tap your foot. Good enough that you replay the chorus. Good enough that after a while you stop asking whether a human being made it. If we ever reach that point, the cultural consequences could be enormous.

Maybe human musicians will remain valuable precisely because they’re human. Maybe live performance becomes more important in a world saturated with synthetic music. Maybe people begin to treat concerts and bands the way we now treat handmade goods: as something valuable partly because we know a person actually made them. I hope so. There is something about a group of people in a room playing instruments together that feels difficult to replace completely, even if a machine can generate a perfect song in thirty seconds.

And yes, I still desperately want rock music to come back in a big way. Give me guitars. Give me drums. Give me some strange new band making too much noise and producing something I didn’t know I wanted until I heard it. That’s one of the things I fear losing most in a world of perfectly personalized music: surprise. The best art doesn’t merely satisfy your existing taste. Sometimes it changes your taste.

If every song is generated by an algorithm that already knows what I like, I may end up surrounded by music that is perfectly pleasant and perfectly optimized and never challenges me at all. That’s an odd possibility: infinite music, all of it tailored to my preferences, and somehow the whole thing becomes a little boring.

So I’m still conflicted about Suno. I can easily imagine playing around with it and loving it. I can imagine finally turning all those ridiculous lyrics I’ve written over the years into actual songs. And I can also imagine looking up a few years from now and realizing that the music ecosystem has been overwhelmed by billions of synthetic tracks that nobody quite knows what to do with.

I’ll probably use it anyway.

And I’ll probably feel slightly guilty while I do.

Welcome to the future.

I’m currently half-reading Annie Jacobsen’s Nuclear War: A Scenario, which is perhaps not the healthiest book to have sitting on the nightstand if you are already inclined to think in worst-case scenarios. And now, in an almost comically unfortunate bit of timing, the subject of Jacobsen’s newest book, Biological War: A Scenario, has wandered back into the news.

A laboratory worker in Siberia has died from what appears to have been pneumonic plague. She worked at the Irkutsk Anti-Plague Institute, and Russian authorities have placed roughly 200 people who may have had contact with her under medical observation. So far, according to current reporting, there does not appear to be evidence of a wider outbreak. The people being monitored have reportedly tested negative and are not showing symptoms. In other words, at the moment this looks like precisely the sort of thing public-health systems are designed to contain: identify the case, trace the contacts, isolate where necessary and prevent further transmission.

So: no reason to panic.

And yet.

There are few phrases less reassuring to someone with an active imagination than “possible laboratory-linked case of pneumonic plague in Siberia.”

Part of the problem is Russia itself. The Soviet Union did not exactly establish a sterling reputation for transparency when something catastrophic happened inside its borders. Chernobyl remains the obvious example: denial, delay, secrecy and an institutional instinct to control information before confronting reality. Modern Russia is not the Soviet Union, of course, and none of that proves that Russian authorities are concealing anything about this particular incident. But historical memory matters. When officials say that everything is under control, the rational response may very well be to believe them provisionally while keeping one eyebrow raised.

For the moment, that is exactly where I am. I have no particular reason to believe this event is going to become anything larger. The available evidence suggests that it probably won’t.

But I think in stories.

More specifically, I tend to think in scenarios. Give me a small, strange event somewhere in the world and some portion of my brain immediately begins gaming out Act Two. What happens if the containment procedures fail? What happens if somebody was infected before anyone realized what was happening? What happens if the first official explanation turns out to be incomplete? What happens when international travel enters the equation? What happens when governments begin withholding information because they are afraid of panic, only to create far more panic once the truth finally emerges?

Most of the time, of course, the answer is: nothing happens. The weird news story remains a weird news story. Someone gets sick, professionals do their jobs, the disease is contained and the rest of us move on to whatever fresh catastrophe the Internet has selected for us the following morning.

The difficulty is that biological risk has a peculiar asymmetry. Nuclear weapons are horrifying, but they are also conspicuous. Someone has to build the bomb. Someone has to maintain it. Someone has to deliver it. Somewhere along the line there are satellites, launch facilities, submarines, bombers, missiles, command systems and thousands of people whose job is to know where all of those things are.

A pathogen is different. Once it becomes transmissible among humans, the delivery system is us.

That is the nightmare at the heart of Jacobsen’s Biological War: A Scenario. Like Nuclear War, the book uses a fictional sequence of events to explore dangers that are rooted in real technologies, historical programs and government planning. Her biological scenario begins with a laboratory accident in Russia involving an engineered pathogen and then follows the consequences outward as infection outruns the systems designed to contain it. Jacobsen has described the timeline of her scenario as going from the initial outbreak to anarchy in just six days.

There are perfectly reasonable criticisms to make of this approach. Scenario books almost inevitably choose assumptions that make the scenario interesting. A pathogen that fizzles out after three infections does not make much of a thriller. Critics of Jacobsen have argued that her biological scenario pushes too confidently toward rapid social collapse and does not spend enough time on the many ways containment, medicine, human behavior and sheer luck might interrupt the chain of events. Even sympathetic reviewers have noted that specialists could reasonably dispute the speed and certainty with which her imagined catastrophe unfolds.

That criticism matters. There is an enormous difference between saying that something could happen and saying that it will happen.

But that is also why I find scenario thinking useful.

The point of the exercise is not necessarily prediction. It is stress-testing assumptions.

Chernobyl did not happen because somebody accurately predicted Chernobyl in advance. Neither did COVID-19 unfold according to some screenplay sitting in a government filing cabinet. Real disasters are messy. They depend upon bureaucratic failures, human mistakes, bad incentives, missed signals, unlikely coincidences and decisions that seem reasonable right up until the moment they are revealed to have been disastrous.

Scenario thinking simply asks us to follow the chain.

What if?

And then what?

And then what?

That can become paranoia if you aren’t careful. Every laboratory accident is not the opening chapter of The Stand. Every unusual cluster of disease is not the beginning of civilization’s collapse. Human beings are notoriously bad at reasoning about rare, emotionally vivid events, and a frightening story can make an extraordinarily unlikely possibility feel imminent.

But there is an opposite failure mode too: refusing to imagine a catastrophe because imagining it feels hysterical.

That is the uncomfortable territory I find myself occupying when I read about this incident in Russia while simultaneously reading Annie Jacobsen. My rational brain says the situation appears contained. There is presently no evidence that we are watching the beginning of some global biological catastrophe. Public-health authorities know how plague works, antibiotics exist, contacts are being monitored and there is every reason to expect this story to disappear from the headlines.

I hope it does.

But another part of my brain cannot help turning the little wheel marked SCENARIO.

Because the lesson of the last century is not that civilization is constantly on the verge of collapse. It plainly isn’t. The lesson is stranger and perhaps more unsettling: systems that look enormously robust can occasionally prove frighteningly brittle when confronted with exactly the wrong sequence of events.

Almost every day, nothing happens.

Almost every alarm turns out to be manageable.

Almost every frightening possibility remains only a possibility.

And then, very occasionally, history decides to write the scenario for us..