DAC Comparisons using AI

AI can be fun, and can be useful in assembling information but beyond that I don't trust it much for subjective evaluation because AI can't hear and can't account for a paramount consideration-our own subjective preferences. What I regard as bright and fatiguing, you might hear as natural but detailed-not sure how AI can account for that. I also think the limited AI tools available to us are. pretty good at telling us what we want to hear. Fox News and MSNBC have proved that to be a fertile market.

Nevertheless, it a fun tool for preliminary information gathering.

WRT evaluating audio equipment, I find AI to be a database management tool (sort of like the box of Stereophile magazines many audiophiles used to keep), that also provides a level of cognitive and predictive analysis (sort of like an audio publication reviewer or a sales person at your local audio shop). The result is information that a user can choose to consider to whatever degree, or not at all.

Some may use AI for tactical information, i.e., does DAC X or Y provide a better S/N ratio? Others use it for more strategic input, i.e., if my preferences are for hard hitting bass, extended high frequencies, a maximum level of detail, and front-row, in-the-room soundstaging, which of the following DACs should sound best to me, and why?

The result is still simply information, not much different from a manufactuer’s marketing verbiage, a salesperson’s spiel, a reviewer’s conclusion, or a forum poster’s exclamations.  Like any information, from whatever source, the end user will need to decide how much weight to give to it.  Like most folks, I would never blindly accept information I receive from AI, without first comparing it to my own experiences, and whatever other information or data I can accumulate on the issue.  As several here have mentioned, hearing a component or speakers for yourself (preferrably in your own system) remains the gold standard.

Just as an aside, ask AI about these Tai Chi ads we are seeing on Audiogon and elsewhere. It is an interesting story.

@sholladay - I think that’s kind of obvious, no? Listening to music is a sensory experience… We may research wine based on ChatGPT based on reviews, but the experience of tasting the wine is always subjective and personal. And it’s that experience which is most important. 

As Mitch has described, AI can be used as a data consolidation and synthesis tool. I have been rather surprised by how apt the descriptions have been on audio components I currently own. I’m sure it’s based on aggregated comments, reviews, user comments, and of course marketing from the manufacturers. 

First, thank you all for the thoughtful input and perspectives on this topic. I genuinely appreciate the discussion — especially when it comes to something as personal and nuanced as sound quality.

I think it’s important to level-set expectations around what AI actually is and what it is not. AI is essentially advanced pattern-recognition software. It generates responses by predicting language based on large amounts of training data. It doesn’t think, perceive, or experience the world the way humans do.

That distinction really matters in discussions about listening quality and musical experience.

It does not experience imaging, warmth, depth, or dynamics.

When asked what “sounds better,” AI isn’t evaluating equipment or forming an opinion based on listening. It’s summarizing patterns found in reviews, measurements, forum discussions, and other written material. It doesn’t simulate listening, and it doesn’t generate independent experiential judgments beyond what exists in its training data.

AI systems are powered by large language models (LLMs), which function like massive statistical libraries. Not all models are trained on the same data, and they differ in scope and design. That’s why different systems can sometimes produce different answers to the same question. The way a prompt is framed also influences the response.

At the end of the day, listening is a human experience. That’s what makes it meaningful — and why discussions like this are worth having. Thanks again to everyone who contribute