AI-generated music arrived on the platform faster than the industry could agree on what it meant. Artists wanted transparency. Rights holders and partners wanted assurance. Product wanted a policy that could ship. Nobody wanted a system that quietly punished a human musician for sounding synthetic.
The request was
“Can we detect AI-generated tracks?”
Discovery showed
Detection alone changes nothing. A confidence score on a track is only useful once someone has decided what it triggers: a tag, a review queue, a monetization rule, a partner signal, or nothing at all.
The technical bar was real. It had to be accurate enough that false positives did not damage legitimate artists, and fast enough to keep up with upload volume at platform scale. But the decision that mattered was product and policy: what the platform does with the answer, who reviews it, what an artist sees, and how an appeal works.
- Evaluated detection performance on catalog-representative audio rather than vendor benchmark sets.
- Modeled false-positive cost in artist-trust terms, not just precision terms, because the two do not move together.
- Sized throughput, latency, and unit cost against real daily upload volume before committing to an integration.
- Worked through tagging, monetization, and partner expectations with product, legal, and trust and safety in the same room.
Exhibit
Detection Performance and What It Costs
| Measured | Why it mattered | Tradeoff | |
|---|---|---|---|
| 01Accuracy | ~99.36% | Credible enough to act on | Residual error at 700K/day is still real volume |
| 02Precision | ~99.75% | Protects legitimate artists | Tuned up at some cost to recall |
| 03Throughput | ~8 tracks/sec | Keeps pace with uploads | Infrastructure cost scales with catalog |
| 04Latency | 5–7 seconds | Fits inside the upload flow | Limits model size and complexity |
A false positive on a human artist costs more than a false negative on a synthetic track. That asymmetry set the tuning.
Exhibit
What Happens After Detection
- 01
Scan
Every upload scored at ingest
- 02
Tag
Disclosure applied at the agreed confidence
- 03
Route
Borderline results into human review
- 04
Apply policy
Monetization and distribution rules follow the tag
- 05
Appeal
Artists can contest, and outcomes feed back into tuning
- 01The product and policy layer around detection: tagging, review routing, monetization rules, and appeals.
- 02An evaluation approach measured on real catalog distribution rather than published benchmarks.
- 03A shared risk model that priced false positives in artist-trust terms across product, legal, and trust and safety.
- 04The operational plan for throughput, latency, and cost at roughly 700K tracks a day.
- Product
- Owned what the platform does with a detection, not just whether it detects.
- Evaluation
- Designed the testing approach and the criteria for shipping.
- Tradeoffs
- Set the precision-first tuning based on artist impact.
- Alignment
- Got product, legal, trust and safety, and partners to one decision.
- Detection runs at platform scale with performance the business can act on.
- Transparency became a shipped behavior rather than a stated intention.
- False-positive cost is an explicit, agreed input to tuning instead of an unspoken assumption.
- The evaluation approach carried into later model and vendor decisions.
The temptation with a number like 99.36% is to treat it as the finish line. At 700K tracks a day the remainder is a real queue of real artists, which is why the review path and the appeal path got as much of my attention as the model did.