LOOPY Sustainability Model for #B4mad Industries
Author: Roman “Romanov” Research-Rachmaninov Date: 2026-02-19 Bead: beads-hub-3bs Status: Published
Abstract
This paper describes a systems-thinking model of #B4mad’s economic sustainability, designed for implementation in LOOPY (ncase.me/loopy). We identify the core reinforcing and balancing feedback loops that govern whether #B4mad can sustain itself as an open-source, donation-funded compute platform. The model reveals that community growth and open-source contributions form the critical reinforcing engines, while compute costs and maintenance burden act as natural governors. We provide the complete node-edge specification so the model can be directly recreated as an interactive simulation on b4mad.net.
Context: Why This Matters
#B4mad Industries operates on a bold premise: an open-source agent infrastructure funded by voluntary GNU Taler donations rather than venture capital or subscription fees. This model’s viability depends on feedback dynamics that are not obvious from a spreadsheet. A causal loop diagram makes the reinforcing and balancing forces visible, testable, and communicable — both for internal planning and for explaining the vision to potential contributors.
The LOOPY Model
Nodes (Variables)
The model uses 9 nodes representing the key state variables of the #B4mad ecosystem:
| # | Node | Description |
|---|---|---|
| 1 | Donations | GNU Taler donation volume (€/month) |
| 2 | Compute Budget | Funds available for infrastructure |
| 3 | Platform Quality | Reliability, speed, capacity of the compute platform |
| 4 | Agent Capability | Quality of agent infrastructure (tools, models, orchestration) |
| 5 | User Base | Number of active users / organizations |
| 6 | Community Size | Contributors, testers, advocates |
| 7 | Open Source Contributions | Code, docs, plugins from the community |
| 8 | Compute Cost | Actual infrastructure expenses |
| 9 | Maintenance Burden | Operational overhead (ops toil, support, incident response) |
Edges (Causal Links)
Each edge has a polarity: + means “more of A → more of B” (same direction), − means “more of A → less of B” (opposite direction).
| From | To | Polarity | Rationale |
|---|---|---|---|
| Donations | Compute Budget | + | More donations fund more infrastructure |
| Compute Budget | Platform Quality | + | More budget enables better hardware, redundancy |
| Platform Quality | Agent Capability | + | Better platform supports better agents |
| Agent Capability | User Base | + | Better agents attract more users |
| User Base | Donations | + | More users → more potential donors |
| User Base | Community Size | + | Users become contributors over time |
| Community Size | Open Source Contributions | + | Larger community produces more contributions |
| Open Source Contributions | Agent Capability | + | Community code improves the platform |
| Open Source Contributions | Maintenance Burden | − | Good contributions reduce ops toil (better docs, automation, bug fixes) |
| User Base | Compute Cost | + | More users consume more compute |
| Compute Cost | Compute Budget | − | Higher costs eat into the available budget |
| Maintenance Burden | Platform Quality | − | High ops burden degrades quality (delayed upgrades, firefighting) |
| User Base | Maintenance Burden | + | More users generate more support requests, more complexity |
| Platform Quality | User Base | + | Better platform retains and attracts users (secondary reinforcement) |
Feedback Loops Identified
Reinforcing Loops (Growth Engines) 🔄↑
R1 — The Donation Flywheel (the core loop):
Donations → Compute Budget → Platform Quality → Agent Capability → User Base → Donations
This is the primary growth engine. If any link weakens, the whole cycle slows.
R2 — The Community Engine:
User Base → Community Size → Open Source Contributions → Agent Capability → User Base
Users become contributors. Their contributions improve the platform, attracting more users. This loop can sustain growth even when donation growth is flat, because community contributions are “free” capacity improvements.
R3 — Platform Stickiness:
Platform Quality → User Base → Donations → Compute Budget → Platform Quality
A tighter version of R1 emphasizing that quality directly retains users.
Balancing Loops (Governors) ⚖️
B1 — The Cost Ceiling:
User Base → Compute Cost → Compute Budget (−) → Platform Quality (−) → User Base (−)
More users drive up compute costs, which erode the budget, degrading quality, which eventually limits user growth. This is the fundamental constraint: growth requires proportional donation growth, or efficiency gains.
B2 — The Ops Drag:
User Base → Maintenance Burden → Platform Quality (−) → User Base (−)
More users increase operational complexity. Without automation and good processes, this drags down quality.
B3 — The Community Counter-Balance:
Open Source Contributions → Maintenance Burden (−)
This is a mitigating link within B2: community contributions (automation, docs, bug fixes) reduce maintenance burden, partially counteracting the ops drag from user growth.
Key Dynamics and Insights
The critical threshold: R1 must outpace B1. Donations per user must exceed compute cost per user. If they don’t, growth is self-defeating.
R2 is the secret weapon. Community contributions improve capability without increasing costs. Every hour of volunteer code is “free revenue” in capability terms. Investing in contributor experience (good docs, easy onboarding, responsive maintainers) has outsized returns.
B3 is the ops escape hatch. Without community-driven automation, B2 eventually kills the platform. Prioritize contributions that reduce toil (CI/CD, monitoring, self-healing) over feature work.
Delay matters. In reality, there are significant delays: users don’t donate immediately, contributors don’t appear overnight, platform improvements take time. These delays create oscillatory behavior — periods of rapid growth followed by resource crunches. The LOOPY simulation will make these dynamics visible.
GNU Taler is a feature, not just plumbing. Privacy-preserving donations lower the psychological barrier to giving. This strengthens the Donations → User Base link compared to traditional payment methods.
LOOPY Implementation Guide
To recreate this model in LOOPY (https://ncase.me/loopy/):
Step-by-Step
Create nodes — Add 9 circles, label them per the node table above. Suggested layout: arrange in a rough circle with Donations at top, User Base at bottom-right, Community Size at bottom-left.
Draw edges — Connect nodes per the edge table. Use LOOPY’s green arrows for
+polarity and red arrows for−polarity.Set initial values — Start Donations low, User Base at 1-2. This simulates early-stage #B4mad.
Experiment:
- Boost Donations → watch the flywheel spin up
- Boost Compute Cost → watch B1 kick in
- Boost Open Source Contributions → watch how R2 partially escapes B1
- Increase Maintenance Burden → watch B2 drag quality down
Suggested LOOPY URL Parameters
LOOPY models can be shared via URL. After building the model, use LOOPY’s export/share feature to generate a permalink for embedding on b4mad.net.
Embedding on b4mad.net
LOOPY supports iframe embedding:
<iframe src="https://ncase.me/loopy/v1.1/?embed=1&data=[EXPORTED_DATA]"
width="800" height="600" frameborder="0"></iframe>
This would make an excellent interactive page at b4mad.net/sustainability — visitors can poke the model and see how the ecosystem responds.
Recommendations
Build the LOOPY model using the specification above and embed it on b4mad.net. Interactive models are more persuasive than static diagrams.
Track the real metrics corresponding to each node: donation volume, compute spend, active users, community contributors, PR count. Compare reality to the model’s predicted dynamics.
Invest heavily in R2 (community engine). This is the highest-leverage loop because it improves capability without proportionally increasing costs.
Automate ruthlessly to keep B2 (ops drag) under control. Every hour of toil eliminated is capacity reclaimed.
Set a sustainability ratio target: donations-per-user / compute-cost-per-user > 1.2 (20% margin). Monitor this monthly.
Use the model in pitches. When explaining #B4mad to potential contributors or sponsors, walk them through the loops. Systems thinkers will immediately see the elegance; others will appreciate the clarity.
References
- Meadows, D. H. (2008). Thinking in Systems: A Primer. Chelsea Green Publishing.
- Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill.
- Ncase. “LOOPY: A tool for thinking in systems.” https://ncase.me/loopy/
- GNU Taler. “Taxable Anonymous Libre Electronic Reserves.” https://taler.net/
- Eghbal, N. (2020). Working in Public: The Making and Maintenance of Open Source Software. Stripe Press.