Suno: Mental Maturity in AI Music Creation

Suno, the most highly rated AI music generator among users, suffers from a technical issue known as generation fatigue: the longer the track, the more the sound quality degrades. The practical solution is to generate short segments and assemble them later.

There is a term rarely used outside engineering circles that perfectly explains why some digital tools disappoint users with unrealistic expectations. It is called generation fatigue and affects AI music generators. Among them is **Suno**, currently favored by users over alternatives like Udio or Mureka.

The mechanism is simple to describe but difficult to solve. The generator must constantly account for the musical content preceding the fragment it is creating to maintain melodic and structural coherence. This continuous effort leads to pogre sound degradation, introducing artifacts and unwanted noise. Engineers are still working on a solution, but the problem remains unsatisfactorily resolved.

## What is generation fatigue and why does it occur?

Generation fatigue is not a one-time bug or a configuration error. It is a structural consequence of how these systems work: each new fragment relies on everything before it, accumulating inaccuracies. The result is audio that worsens as the track pogre.

Patches exist. One can resample fragments or apply correction techniques, but they do not always yield good results. **Suno 6.0** introduced "Max Mode," which consumes double the credits and aims to ensure greater consistency in generation. However, this does not necessarily guarantee better sonic quality. That is the crux of the matter.

The temptation is to think the tool should work differently. That one could simply request a five-minute song and get impeccable quality from start to finish. Today, that is like expecting a car to start with a full tank and have exactly the same amount of fuel after a hundred kilometers. It does not work that way.

## Generate in segments under two minutes

The sensible solution is to adapt to the tool rather than demanding it adapt to our wishes. We must accept an uncomfortable reality: AI-generated songs may suffer pogre degradation as their duration increases. Although degradation can begin in the first few seconds, there is consensus among users that it becomes particularly evident after **two minutes**.

Based on this premise, the strategy is to generate in parts, ensuring none exceed that threshold. **Suno 6.0** added an option for this in the "More Options" section via the "Duration" setting. There is no need to fight the system; just provide the conditions where it performs best.

A common procedure among power users: since Suno generates two versions per run, prepare a short lyric with three stanzas and set a duration limit of **1:50 minutes**. This yields two versions of approximately 1:50 minutes each. Then, select the best ones, assemble them using an audio editor, and achieve a song of roughly **3:45 minutes**, largely avoiding the degradation issues found in longer generations.

## Mental maturity as a workflow method

The underlying concept is mental maturity, defined as the ability to adapt to reality, whether comfortable or not. In other words: accepting things as they are, without expecting reality to conform to us. The opposite, immaturity, is insisting the world works according to our idealized expectations.

Applied to AI music creation, this means stopping the demand for a perfect five-minute piece generated in one go and starting to work with the medium's actual possibilities. We cannot always make things work as we would like, but we can learn to work with what we have.

The generative music industry is advancing rapidly, and generation fatigue will likely find a technical solution soon. Meanwhile, users should know its limits before spending credits. It is also worth remembering that any commercial use of AI-generated music requires reviewing license terms and implied rights beforehand.