AI and the Future of Guitar Tone: An Interview with Neural Amp Modeler’s Inventor, Steven Atkinson
Steve Atkinson, creator of NAM, explains how he's using machine learning to revolutionize guitar tone.
For the past two decades, guitarists chasing great recorded tone have wrestled with the same compromises: do you mic up a loud tube amp and hope the neighbors don't complain, or settle for a digital modeler that feels "close, but not quite"?
In 2025, Neural Amp Modeler (NAM) and TONE3000 have upended that dilemma. Instead of relying on circuit simulations or proprietary software, NAM uses AI and machine learning to learn the sound of real amps, pedals, and signal chains. The result: hyper-realistic digital models that can be captured and shared on TONE3000 by anyone with a computer, a guitar, and a bit of curiosity.
While NAM is already being widely used to capture more accurate models of guitar and bass gear than ever before, this new technology can be used to model a wide variety of audio processing equipment. Which means audio engineers, producers, and mixing engineers can now easily model every part of their production chain.
To understand how NAM came about, and where it's heading, we sat down with its inventor, Steven Atkinson. Let's learn how he guided the project from a fun, personal experiment into a tool that's been changing the entire conversation about how AI can help empower musicians in the future.
The Origins of NAM
Q: Steve, let's start with the basics. How did you come to create Neural Amp Modeler?
Atkinson: I started NAM as a kind of fun side project while I was getting deeper into machine learning. I've been a musician since I was a kid, I played cello, then guitar and bass, and I've always been drawn to versatile digital gear. My first amp was a Line 6 Spider, and later I was a happy Axe-Fx user. Around 2018–2019, I thought, "What if I applied deep learning to guitar amps?" I did it "closed book," not even checking what others had tried. My expectations were low. Honestly, the first time I got a model to sound even vaguely like the real amp, it was thrilling.
How NAM Works
Q: For guitarists who may not know, how do you explain what NAM actually does?
Atkinson: The simplest way is to think of it like making an impulse response of a speaker cabinet, but instead of just capturing how a cab sounds, NAM can capture the whole behavior of an amp or pedal, or the combination of those things. You feed the system some recordings—either a sweep signal designed specifically for NAM, or your own playing—and the software "learns" how the gear responds. The result is a file you can load in a plugin or pedal that responds just like your amp would.
Q: But you're just capturing just that one tone, right? Doesn't that create some limitation with what you can model with NAM?
Atkinson: In a way, yes. But limitations can spark creativity. I like that. There's artistry in saying, "Here's the version of this amp I want to preserve." NAM really offers you a snapshot. It doesn't give you every knob and option, but it gives you the exact tone you dialed in. It's your best foot forward. Of course, parametric NAMs are possible too, where you model controls like gain or EQ, but I think the snapshot idea is really powerful for musicians.
Q: Compared to traditional modelers, what's the advantage of capturing your own tones?
Atkinson: With commercial modelers, you're often using someone else's idea of what a Marshall or a Fender or whatever should sound like. With NAM, if you love how your amp chugs, you literally record yourself chugging, and now that sound is yours forever. It's personal. That's something older modeling approaches couldn't really offer.
The Accuracy Question
Q: How accurate are NAM models today? Are we at the point where they're almost indistinguishable from real amps?
Atkinson: For me, they're already indistinguishable. I don't think I have golden ears, but that's what I hear.
Q: That's pretty incredible. Especially considering that some people are making these NAM profiles with pretty humble equipment that they have at home compared with what guitar modeling manufactures have access to.
Atkinson: Exactly. What excites me is that NAM doesn't need the most up-to-date computer to run. It helps to have a GPU for training, but TONE3000 can handle that for you. You can stick them in a DAW session or even inside a pedal, and they just work. We're at a place where the tech is both highly accurate and practical. And like you said, you can literally make these NAM profiles at home. That's pretty powerful.
Digital vs. Analog Longevity
Q: One thing people wonder about with any digital tool is obsolescence. Tube amps last decades. Software usually doesn't. What's your take on that?
Atkinson: That's a fair concern. My hope is that being open-source gives NAM a kind of insurance. If I stopped working on it tomorrow, the code is out there. Others can maintain it, improve it, or adapt it. Nothing lasts forever. Even tube amps rely on parts that go out of production. But software plus community knowledge has a real shot at long-term survival.
Getting Technical: Capture Methods
Q: Can we geek out a bit? To actually capture a tone, I understand there are two main ways to create a NAM profile, either by using the "sweep signal" or the "dry/wet pair." What's the difference?
Atkinson: The sweep signal is usually what I recommend most. It's a carefully designed recording that hits the amp with chords, single notes, dynamics, even odd tunings, so the model learns everything it needs in just a few minutes. The dry/wet method is great if you already have a recording; say you tracked a DI and a miked amp years ago. You feed both tracks to NAM, and it learns the relationship. That means you can capture tones from old sessions, or even classic multitracks if you had them.
Q: That's wild. It almost suggests you could "bottle" the tones off classic records.
Atkinson: Exactly. People have been doing versions of this for years with tone matching and EQ matching, but NAM makes it more direct. It's not science fiction, but it is science. If you had the right isolated tracks, you could capture Brian May's Bohemian Rhapsody tone. But the more common use right now is that people can recreate tones they've recorded in the past and want to use again.
Why NAM Sounds "More Real"
Q: The hype on the forums say that NAM sounds more real than traditional guitar modelers. Is that true? And how is that possible?
Atkinson: NAM just models tones more accurately than what we could do before we had AI. But that comes with other issues, too, one of which is that NAM can actually model what some people think of as noise. Often what people call "noise" is actually detail. A real amp isn't silent: it picks up hum, room quirks, or even the tick of a wristwatch near your pickups. NAM reproduces that. Other modelers sometimes smooth those details away, which can sound cleaner, but also less real. Whether that's good or bad depends on taste.
Live Performance and the Future
Q: NAM has already started to spread beyond home recording and make its way into live performance. Is the latency performance good enough to work like this already? And where do you see this going in the future?
Atkinson: We're already seeing NAM run in pedals with sub-millisecond latency, which is basically imperceptible. Hardware makers are experimenting with it, and I hope more big names adopt it. What excites me most, though, is the community. Thousands of guitarists are making and sharing their own models on TONE3000 and elsewhere. NAM is out there in the wild on pedalboards and in racks. It's really rewarding to see people perform with it.
Q: Last question: do you see NAM as part of a bigger shift in how guitar tech evolves?
Atkinson: I hope so. Open-source and community involvement can accelerate innovation in a way that's different from how a closed system might. Musicians get tools they actually want, not just what a company decides to sell. And for me personally, it's just been an incredible surprise to see something I started as a puzzle for myself turn into something guitarists all over the world are using to make music.
AI Tools for Actual Musicians
In a world where musicians are feeling somewhat threatened by generative AI, Neural Amp Modeler is shifting the conversation around how AI can empower musicians and artists. Instead of making music for you, NAM is giving guitarists and bassists better tools to support their own creativity.
What once required expensive equipment and studio access can now be captured and shared from a bedroom setup, giving everyday players access to sounds that used to be out of reach. Atkinson's project suggests a future where tone isn't limited by which amp sits in your room, but by the sounds a global community chooses to document and pass along.
And as that archive grows, communities like TONE3000 are becoming not just libraries of gear, but living records of how artists will shape their sound in the 21st century and beyond.
