Thanks for sharing this, I’ve gone through it in detail, and I appreciate the depth of the dataset work and the ambition behind the analysis. There’s a lot here that is genuinely valuable for understanding how streaming economics are evolving.
What I think the piece does well (its value)
The strongest contribution of this report is that it moves beyond headline figures and interrogates distribution, not just growth. That’s important, because most platform reporting focuses on total revenue increases rather than how that revenue is experienced at artist level.
Key strengths:
The tier-based breakdown ($1K → $10M+) is very effective at showing structural inequality in a way that is easy to grasp.
The distinction between total pool growth vs per-artist outcomes is one of the most important insights in the piece.
The identification of mid-tier erosion is particularly valuable — this is often the least discussed but most structurally important part of the ecosystem.
The attempt to include external distortions (spam, fraud, algorithmic effects) shows awareness that this is not a purely linear system.
The inclusion of multi-year comparison helps shift the conversation from “yearly noise” to “structural trend.”
Overall, it contributes meaningfully to a conversation that is often simplified or marketing-led.
What could be strengthened
From an editorial and industry credibility perspective, there are a few areas that could be refined:
1. Tone and framing consistency
At times the language moves into interpretive or value-laden framing (for example implying intent or “bad faith” behaviour). While the frustration is understandable, tightening this into mechanism-based language (what the system does rather than why actors “want” it) would make the argument more robust and harder to dismiss.
2. Clearer separation of forces
The analysis brings together several different drivers:
These are all important, but separating them more explicitly would strengthen the causal clarity of the argument.
3. Methodology transparency earlier in the piece
The “at least” methodology and missing <$1K cohort explanation is strong, but it would land better if it was introduced earlier and more explicitly framed as a limitation rather than embedded mid-analysis. This would improve trust in the headline conclusions.
4. Stronger neutral framing of conclusions
Some conclusions could be expressed more as observable outcomes rather than interpreted motivations, which would increase the piece’s defensibility in industry settings.
What this means for emerging artists and new talent
This is where the report is most important in real-world terms.
For emerging artists, the key implications are:
1. Entry into the system is easier, but sustainability is harder
More artists are able to enter streaming platforms and generate at least some income, but the data suggests that entry does not translate into stable or scalable income for most artists.
In practice:
visibility is more accessible
but economic progression is less predictable
2. The “middle career” is becoming structurally fragile
The erosion of the $100K+ tier and mid-tier poolshare suggests that the traditional “working musician career ladder” is weakening.
This has major implications:
fewer viable long-term independent careers
increased reliance on either scale success or diversification (live, sync, direct-to-fan income)
greater precarity for artists who are “successful but not superstars”
3. Upward mobility is increasingly nonlinear
The data suggests that moving up is still possible, but:
progression is less evenly distributed
more income concentrates at the top once success is achieved
fewer artists make the transition into the highest earning tiers
This creates a system where breakthroughs still happen, but consolidation is stronger than diffusion.
4. Algorithmic ecosystems favour reinforcement, not discovery equity
For new artists, this means:
early traction matters disproportionately
sustained algorithmic visibility can outweigh traditional “career building” stages
competition is increasingly with catalogue and legacy behaviour, not just peers
Overall view
This is a strong, data-rich piece that contributes meaningfully to understanding streaming economics beyond surface-level narratives. Its most important value is in highlighting distributional inequality and mid-tier erosion, which are often overlooked in platform reporting.
With some tightening around tone, methodological clarity, and separation of causal forces, it could become even more persuasive in policy, industry, and artist advocacy contexts.
Thanks Saskia, glad you got a lot out of it! Hear you on tone, if someone were to commission me to do this work I'd tone it down, when it's my own substack I gotta call it like I see it ;)
Hi Hunter,
Thanks for sharing this, I’ve gone through it in detail, and I appreciate the depth of the dataset work and the ambition behind the analysis. There’s a lot here that is genuinely valuable for understanding how streaming economics are evolving.
What I think the piece does well (its value)
The strongest contribution of this report is that it moves beyond headline figures and interrogates distribution, not just growth. That’s important, because most platform reporting focuses on total revenue increases rather than how that revenue is experienced at artist level.
Key strengths:
The tier-based breakdown ($1K → $10M+) is very effective at showing structural inequality in a way that is easy to grasp.
The distinction between total pool growth vs per-artist outcomes is one of the most important insights in the piece.
The identification of mid-tier erosion is particularly valuable — this is often the least discussed but most structurally important part of the ecosystem.
The attempt to include external distortions (spam, fraud, algorithmic effects) shows awareness that this is not a purely linear system.
The inclusion of multi-year comparison helps shift the conversation from “yearly noise” to “structural trend.”
Overall, it contributes meaningfully to a conversation that is often simplified or marketing-led.
What could be strengthened
From an editorial and industry credibility perspective, there are a few areas that could be refined:
1. Tone and framing consistency
At times the language moves into interpretive or value-laden framing (for example implying intent or “bad faith” behaviour). While the frustration is understandable, tightening this into mechanism-based language (what the system does rather than why actors “want” it) would make the argument more robust and harder to dismiss.
2. Clearer separation of forces
The analysis brings together several different drivers:
Spotify-specific policies
broader streaming economics
external market effects (AI spam, inflation, behaviour shifts)
These are all important, but separating them more explicitly would strengthen the causal clarity of the argument.
3. Methodology transparency earlier in the piece
The “at least” methodology and missing <$1K cohort explanation is strong, but it would land better if it was introduced earlier and more explicitly framed as a limitation rather than embedded mid-analysis. This would improve trust in the headline conclusions.
4. Stronger neutral framing of conclusions
Some conclusions could be expressed more as observable outcomes rather than interpreted motivations, which would increase the piece’s defensibility in industry settings.
What this means for emerging artists and new talent
This is where the report is most important in real-world terms.
For emerging artists, the key implications are:
1. Entry into the system is easier, but sustainability is harder
More artists are able to enter streaming platforms and generate at least some income, but the data suggests that entry does not translate into stable or scalable income for most artists.
In practice:
visibility is more accessible
but economic progression is less predictable
2. The “middle career” is becoming structurally fragile
The erosion of the $100K+ tier and mid-tier poolshare suggests that the traditional “working musician career ladder” is weakening.
This has major implications:
fewer viable long-term independent careers
increased reliance on either scale success or diversification (live, sync, direct-to-fan income)
greater precarity for artists who are “successful but not superstars”
3. Upward mobility is increasingly nonlinear
The data suggests that moving up is still possible, but:
progression is less evenly distributed
more income concentrates at the top once success is achieved
fewer artists make the transition into the highest earning tiers
This creates a system where breakthroughs still happen, but consolidation is stronger than diffusion.
4. Algorithmic ecosystems favour reinforcement, not discovery equity
For new artists, this means:
early traction matters disproportionately
sustained algorithmic visibility can outweigh traditional “career building” stages
competition is increasingly with catalogue and legacy behaviour, not just peers
Overall view
This is a strong, data-rich piece that contributes meaningfully to understanding streaming economics beyond surface-level narratives. Its most important value is in highlighting distributional inequality and mid-tier erosion, which are often overlooked in platform reporting.
With some tightening around tone, methodological clarity, and separation of causal forces, it could become even more persuasive in policy, industry, and artist advocacy contexts.
Thanks Saskia, glad you got a lot out of it! Hear you on tone, if someone were to commission me to do this work I'd tone it down, when it's my own substack I gotta call it like I see it ;)
I respect you .. Real talk ..