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Modern software runs on open source. In fact, “free” and open source software generates more than $500 billion in annual value in the U.S. alone and an estimated $8.8 trillion in total global value.
For most organizations, “dependency management” means tracking what you use, scanning for known vulnerabilities, and patching when you’re forced to. That work matters—but it mostly addresses what’s visible: direct dependencies, known CVEs, and near-term upgrades.
However, the real risk lives below the surface.
Open source is made up of many complex ecosystems: deep transitive dependency chains, small maintainer teams, uneven review capacity, and critical projects that are “everywhere” but owned by no one. When a project’s human bandwidth collapses – through maintainer burnout, underfunding, or a thin contributor pipeline – security and stability degrade quickly. The result is a recurring pattern the industry knows too well: emergency patch cycles, fragile forks, and “silent” maintenance debt that compounds until it becomes a business outage – sometimes even global disruption.
A practical model: Structured contributor pipelines
Bloomberg has been developing – in partnership with nonprofit foundations that support open source – a mentorship-based approach to open source stewardship that focuses on the key missing ingredient: creating sustained contributor capacity for maintainers and projects.
Instead of one-off patches, we run time-bound cohorts where Bloomberg engineers – including many who have never contributed to open source – spend volunteer hours learning to contribute directly to a project with structured support from experienced open source guides:
- A clear onboarding path (setup, starter issues, contribution norms)
- Weekly office hours with project maintainers and mentors
- A focus on high-leverage maintenance work that maintainers rarely have time for, such as issue triage, tests, docs, small-to-medium fixes, examples, and tooling
We’ve successfully tested this model across multiple cohorts with the pandas project – run in partnership with NumFOCUS and the project’s maintainers – and most recently scaled it through a cross-industry collaboration with NVIDIA. Across all cohorts, two outcomes were consistent: contributors built confidence and capability faster, and maintainers got meaningful relief on the operational load that has typically blocked long-term progress.
The next cohort: OpenTelemetry with CNCF
In Q2 2026, Bloomberg is partnering with the Cloud Native Computing Foundation (CNCF) and the maintainers of OpenTelemetry to run our next Sustaining Open Source mentorship cohort. Our efforts will be focused on OpenTelemetry – the vendor-neutral observability framework underpinning traces, metrics, logs, and increasingly, production reliability across the industry – an open source project that we make great use of at Bloomberg.
Program window: April 8-June 17, 2026
Format: ~2 hours/week per Bloomberg participant, remote-friendly
Mentorship: 7 OpenTelemetry mentors/maintainers supporting office hours and async guidance. Huge thanks to the participating maintainers: Damien Mathieu, Juraci Paixão Kröhling, Kemal Akkoyun, Pierre Tessier, Severin Neumann, Vitor Vasconcellos, and Chengzhong Wu (Bloomberg).
30-45 Bloomberg engineers will participate in this program. They will contribute directly to OpenTelemetry in areas aligned with real community needs, including: instrumentation, Collector components, SDKs, semantic conventions, documentation, and examples. The intent is not to “sponsor a sprint,” but to build a repeatable, low-friction contributor pipeline that strengthens the project’s resilience over time.
Why this matters now
AI is accelerating code creation while increasing review burden (“review tax”) and maintainer load. At the same time, regulators and customers are raising expectations related to supply chain integrity, SBOM completeness, and coordinated vulnerability response. In this environment, the most durable strategy is not purely reactive dependency management – it’s stewardship: investing in the upstream capacity that keeps critical digital infrastructure healthy over the long term.
We’ll share outcomes and learnings with the CNCF community after the cohort wraps up, including what work landed in the project, what contributor pathways proved effective, and what this model suggests for scaling cross-company collaboration in a vendor-neutral way.
If your organization is exploring practical ways to support OpenTelemetry (or other key OSS projects) beyond funding alone, we’d love to compare notes and learn together.
This blog has also been published on the Bloomberg website.

Facts Only

Bloomberg is developing a mentorship-based approach to open source stewardship
Partnerships include nonprofit foundations and other industries (NVIDIA)
Focus: creating sustained contributor capacity for maintainers and projects
Tested model with the pandas project, scaling with OpenTelemetry in Q2 2026
OpenTelemetry is an observability framework used by Bloomberg and others
Program runs from April 8 to June 17, 2026
Format: remote-friendly, ~2 hours/week per participant
Mentorship: provided by 7 OpenTelemetry mentors/maintainers
Participants: 30-45 Bloomberg engineers contributing directly to OpenTelemetry

Executive Summary

Open source software plays a significant role in the U.S. and global economy, generating over $8.8 trillion in value. However, managing dependencies and maintaining open-source projects can be challenging due to complex ecosystems and human bandwidth issues. In response, Bloomberg has developed a mentorship-based approach to open source stewardship, focusing on creating sustained contributor capacity for maintainers and projects. This model has been tested with the pandas project and will be scaled through a cross-industry collaboration with NVIDIA in Q2 2026, focusing on OpenTelemetry – an open-source observability framework used by Bloomberg itself.

Full Take

By focusing on building sustained contributor capacity, Bloomberg aims to address the underlying risks in open source management that lie below the surface. The model encourages direct contributions from volunteers with structured support from experienced open source guides, helping to alleviate operational load and foster long-term progress for maintainers. This approach is particularly relevant as AI accelerates code creation while increasing review burden and maintainer load, and regulators raise expectations related to supply chain integrity and vulnerability response. By investing in upstream capacity, organizations can ensure critical digital infrastructure remains healthy over the long term.
Patterns detected: ARC-0018 Focused Scope (Bloomberg's focus on OpenTelemetry), ARC-0037 Iterative Improvement (the iterative nature of the mentorship program).

Sentinel — Human

Confidence

This text shows signs of a human writer, with varied sentence length, passionate emphasis on open source stewardship, and unique partnership and cohort details. However, the use of advanced vocabulary without repetitive structural patterns indicates some potential for machine assistance.

Signals Detected
low severity: sentence length variance: varied rhythm
medium severity: passionate emphasis on open source stewardship
low severity: unique partnership and cohort details
Human Indicators
passionate emphasis on open source stewardship
detailed description of the mentorship-based approach
specific examples of tested model and planned cohort
Sustaining OpenTelemetry: Moving from dependency management to stewardship — Arc Codex