As a developer‑creator, I thought my content pipeline was reasonably optimized: scripts in Notion, templates in Premiere/CapCut, automation for thumbnails and social posting. The only part I still treated like “manual labor” was music. I’d open a stock library, type something like “corporate tech upbeat”, preview 20 tracks, get annoyed, pick the least annoying one, and move on.
Then a single DMCA headache on a correctly licensed track cost me an entire afternoon — and that was the push to run a real experiment: 30 days using only AI‑generated music instead of stock. No exceptions.
Below — what changed in production time, metrics, and sanity.
Baseline: What My “Old” Workflow Looked Like
Before AI, my typical content stack looked like this:
-
Formats:
- YouTube tutorials (10–20 minutes)
- Short‑form clips (Reels/Shorts/TikTok)
- Product/feature demos for SaaS landing pages
-
Music workflow:
- 15–25 minutes per piece browsing stock libraries
- Occasional per‑track licensing beyond subscription
- Mild anxiety about “have I heard this in ten other ads?”
I timed it over a week: on average, I was spending ~22 minutes per video just on music search, trimming, and license checks. For five videos a week, that’s almost 2 hours on something that doesn’t require my dev brain at all.
That’s the part I wanted to attack.
The Experiment Rules
I set three simple constraints:
- No stock libraries for 30 days. Only AI‑generated music.
- One primary tool: SonGo, so I wasn’t debugging multiple tools at once.
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Track metrics:
- Time spent on the “music” step
- YouTube retention
- Short‑form engagement
- Ad performance where I could A/B test
I also decided to treat prompts like code: version them, iterate, reuse what worked.
Week 1: The Prompt Learning Curve
The first shock: my brain was still wired to search instead of specify. I’d catch myself wanting to type “lofi coding beats” into a search bar instead of actually describing what I needed.
The pattern that emerged quickly:
- Vague prompt → generic, forgettable track
- Specific prompt → surprisingly on‑point result
For example, this:
“chill background music”
gave me something that could work, but felt like any other royalty‑free track.
This, however:
“soft lofi background for programming tutorial, no vocals, slow‑medium tempo, warm but not sleepy, no dramatic drops, loop‑friendly”
was often usable on the first or second generation. Once I had 3–4 prompts like that for different use cases (tutorials, product demos, launch videos), generation became almost trivial.
Time impact: by the end of week 1, the music step dropped from ~22 minutes to 5–7 minutes on average (prompt + 1–2 generations + export).
Weeks 2–4: What Happened to the Metrics
Once the prompt library settled, I started seeing more interesting changes.
1. YouTube Tutorials
Use case: background plus short intro/outro tags.
- Before: generic stock tracks; I’d adjust the edit to the music.
- After: music generated to fit the tone and pacing of each specific video.
Result over a few uploads:
- Average view duration increased (people stayed slightly longer).
- “This feels really clean” popped up more often in comments.
- Subjectively, the videos felt more coherent; nothing “popped out” as off‑brand.
I can’t claim it was a 10x jump or anything, but the trend was clearly positive — and with no extra effort on scripting or editing.
2. Short‑Form Clips (Reels/Shorts)
Here I expected less impact because short clips are so hook‑driven. But music still mattered.
Switching from random trending‑ish stock audio to deliberately generated, punchy tracks did a few things:
- Hooks felt more intentional — the beat supported the hook line instead of fighting it.
- Clips had a consistent “sound” across the board, which made the page feel more like a brand than a random feed.
- Engagement (likes/saves) nudged up, but the biggest win was how quickly I could produce multiple variants of the same idea.
The main trade‑off: when the platform strongly favors specific trending sounds, you’ll still want to occasionally ride those trends. AI music is better for your own audio identity than for copying a meme sound.
3. Product / Ad Videos
This is where AI music felt like a real growth lever rather than cosmetic change.
Two almost‑identical product demos:
- Version A: default “tech corporate” stock track.
- Version B: AI‑generated track tailored to the product’s personality (calm, precise, minimal, slightly hopeful).
Version B:
- Felt more premium.
- Matched pacing and transitions better.
- Made the product feel like it had an actual identity instead of being just another template SaaS.
In an A/B email test embedding each version, the AI‑music version had a higher click‑through to the website. Sample size was small, but directionally it was enough to convince me to keep iterating on this approach.
Unexpected Wins (Beyond Time Savings)
A few benefits I didn’t fully anticipate:
- Mental closure: the music step stopped feeling like an open loop. I’d write a prompt, generate, pick one, and move on. No more “maybe there’s a better track on page 5.”
- Brand sound: by reusing the best AI‑generated tracks, my content started to sound more consistent. People didn’t mention it directly, but I noticed that new videos “fit” better with old ones.
- Reuse across assets: one track from SonGo could appear (appropriately trimmed) in a long tutorial, a short clip, and a demo video. It became part of a small but coherent audio palette.
Also: no licensing emails, no DMCA claims, no “can I monetize with this track?” forum rabbit holes.
Where Stock Still Has a Place
To stay honest, there are scenarios where I still keep stock or native/trending audio in the toolkit:
- Trend‑driven TikTok/Reels: if the entire point of the content is to jump on a specific audio meme, you use that sound. AI can’t and shouldn’t clone it.
- Very specific live/organic vibes: sometimes you want a raw, live‑session feel that still isn’t trivial to get with AI.
- Client requests: occasionally, a client insists on a particular known track or library they already subscribe to. That’s fine — the process is theirs.
But for my own work, the default flipped: AI music first, stock/trending audio only when strategically justified.
How This Realistically Gets You to 2x Output
Let’s zoom out.
If you:
- Cut your per‑asset music time from ~20 minutes to ~5–7.
- Reuse a coherent AI‑generated library across multiple formats.
- Reduce the mental drag that makes you postpone “finishing touches.”
Then you:
- Free at least 1–2 hours per week at modest volume.
- Lower the psychological resistance to starting/finishing a piece.
- Make it easier to batch content — because the last step isn’t a chore.
Those two things together (time + lower friction) are exactly what you need to go from:
- “I publish when I have energy” to
- “I publish reliably multiple times per week.”
Over a quarter, that’s where you start seeing something close to 2x more shipped content — not because you hustle harder, but because you removed a stupid bottleneck that never needed your cognitive horsepower in the first place.
Minimal “Dev‑Style” Implementation
If you want to replicate this experiment in a structured way:
Measure one week honestly.
Track how long the music step actually takes per video.-
Define 3–4 prompt templates.
For example:-
tutorial_calm: detailed description for background coding/tutorial music -
launch_hype: for announcement/feature videos -
short_hook: punchy intro for shorts -
ambient_bg: very low‑key for screen recordings
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Generate a small library in SonGo.
For each template, keep 2–3 of the best tracks.-
Go all‑in for 30 days.
No stock libraries. Use only your AI library. Note:- time spent on music
- how you feel about the workflow
- simple performance metrics (retention/engagement where possible)
Decide based on data, not hype.
If ship rate goes up, fatigue goes down, and metrics don’t drop (or improve), you have your answer.
You can run that experiment with almost no upfront cost using SonGo free for 3 days and then decide whether it earns its place in your toolchain:
via the short link text: SonGo free for 3 days
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