A visual essay
How streaming pays independent artists today, what we're building instead, and why the honest answer isn't as simple as a new payment model.
You pay a monthly subscription fee.
Every fee in your country, every month, is gathered into a single pool. The pool is divided across every track on the platform, weighted by each track's share of total streams.
This is the pro-rata model. It has been the default since music streaming began.
Your money flows to whoever was streamed most globally, not necessarily the artists you listened to. Even if you only listened to your favorite artist all month, they may not receive any of your subscription fee if their tracks don't reach a thousand plays—or aren't popular enough on the platform.
catalog.fm uses an alternative payout model that makes it clearer how each listening subscription directly supports the artists a listener actually plays.
The status quo · Pro-rata
Every subscriber's fee in a country is gathered into a single monthly pool, then divided across every track on the platform by each track's share of all platform streams. Most of your fee follows whoever dominates aggregate listening—not necessarily the artists you listened to.
catalog.fm · User-centric
Your fee is split only among the artists you listened to, weighted by how long you listened.
Listen to one artist all month and they get your whole contribution. Listen to twenty-five and it splits twenty-five ways. Either way, the people you played are the people you paid.
Three streaming-era platforms have each addressed part of the problem.
None has done all of: user-centric streaming, ongoing revenue from listening, and transparent reporting to subscribers about where their money went.
That gap is what catalog.fm is built around.
Two variables drive most of it: how many people listen to the artist each month, and the artist's average listening share—the portion of each listener's monthly streaming time spent on this artist, averaged across the whole audience.
For example, imagine an artist has 4 listeners. Listener A listens 3%. Listener B listens 19%. Listener C listens 10%. Listener D listens 8%. Add those up (40%) and divide by 4 listeners = 10% average listening share.
Pick a number of monthly listeners and an average listening share below. The calculator estimates a monthly payout under user-centric, assuming every listener is a paying subscriber.
Payout is monthly listeners × subscription revenue per listener after fees × avg. listening share.
100,000 × $10.37 × 2.0% ≈ $20,746
Subscription revenue per listener after fees (same constant as in the model):
$13 × (1 − 15% − 2.9%) − $0.30 = $10.37
Illustrative only: every listener counted is modeled at full-price subscriber economics—the payout math above—not a forecast for any one career. Totals still swing with real audience size and how fragmented listeners are across the artists they play.
The average streaming subscriber listens to hundreds of artists in a single month. Each of those artists is splitting that subscriber's $10.37 hundreds of ways. The big number above shrinks fast.
And the split isn't even. Some artists get 30% of a listener's attention; most get less than 1%. The shape of that distribution is captured by the Gini coefficient: the same metric economists use for income inequality. A coefficient of 0 means every artist would be paid equally; at 1, the top artist would receive the entire pool and everyone else would receive nothing.
$100 split between 100 artists
G = 0.50
As G rises, money concentrates into fewer, bigger dots.
Drag to compare
Streaming sits in the 0.85–0.95 Gini range. More concentrated than income inequality in any country on earth. Switching the payment model from pro-rata to user-centric barely moves that.
The math model isn't what flattens earnings. Listening habits are.
Listening on catalog.fm comes with a running ledger of who got paid because of you. A donut chart shows the artists you played this month. An impact card shows the time and the dollars that flowed to each one.
Transparency isn't a mood. It's a product pillar we run features against. When listeners can see the consequences of how they listen, which artists they're actually supporting and how much, they listen with more intention. The narrower the listening, the more meaningful the support per artist.
We don't dictate how anyone should listen. We make the consequences of how you listen transparent.
Your support · Big Thief
You listened to Big Thief for 11h 24m this month, about 38% of your total listening time. That share of your subscription went directly to them.
What we can and can't change
A more equitable platform is something we'd love to build. We can't promise that user-centric, even paired with the design choices above, will flatten platform-wide inequality. The same artists who dominate everywhere else might still dominate here.
That isn't the goal. The goal is simpler:
We use the Gini coefficient and Zipf concentration as our north stars. Not as guarantees, but as the metrics we measure each new feature against. Does it surface artists outside the head of the curve? Does it make focused listening easier? Does it concentrate or distribute attention?
Where your money goes is built into the system. How you listen is yours. We'll be transparent about how the two interact.
catalog.fm is in development. If this sounds like something you'd use or want to support, join the waitlist and we'll keep you updated on our progress.