The Great Divide Deepens

The split between "professional" and "hobbyist" Open Source is becoming more pronounced, and it's not just about who has money. The Cyber Resilience Act in Europe is accelerating this divide: September 2026 brings reporting requirements, and by December 2027, we're looking at full compliance, including CE marking. The timing might seem far ahead, but anyone who's dealt with regulatory compliance knows it has a way of sneaking up on you. This legislation introduces mandatory security assessments, ongoing security updates, and structured compliance protocols, making it increasingly difficult for independent developers to keep up. It could inadvertently impose significant compliance burdens on individual Open Source maintainers.

This isn't just bureaucratic busywork; the XZ backdoor incident (where a widely used Linux tool was secretly altered to create a security vulnerability) showed us why this matters. 

However, a lot of the fear around the CRA isn’t necessarily due to the act itself, but rather the lack of clear information and guidance on what it actually means for individual developers. I’ve heard from multiple Open Source maintainers who are deeply concerned—some even considering freezing their projects or stepping away entirely—because they’re unsure if simply maintaining a repository could make them legally accountable under the act. In reality, the CRA doesn’t impose heavy obligations on hobbyist developers who aren’t monetizing their work. But the absence of structured education around these nuances has left many developers in a state of uncertainty.

This highlights an important challenge: the act itself isn’t necessarily a disaster for Open Source, but the lack of clear, accessible guidance is. More effort is needed to provide transparent, developer-friendly explanations of the CRA’s actual impact—what it covers, what it doesn’t, and what steps individual maintainers should (or shouldn’t) take. Without this, misinformation will continue to push independent developers away from Open Source out of fear rather than necessity.

Interestingly, beyond CRA, there might me some unexpected tailwinds for Open Source. Particularly from across the Atlantic. There are rumblings that in response to recent EU-U.S. trade tensions (including tariff threats), some U.S. partners like Canada may reintroduce IP protections as leverage in broader trade negotiations. While this sounds bureaucratic, it could open the door for renewed conversations on Open Source exceptions and flexibilities in international IP frameworks.

As the global software regulatory environment tightens, Open Source could start being seen not just as a risk, but as a strategic alternative—especially for countries looking to reduce dependency on proprietary vendors. Whether that turns into real policy change remains to be seen, but it’s a shift worth watching.

Open Source and AI

The AI hype cycle may be fading, but its impact on Open Source is just beginning. AI-generated contributions are flooding repositories, shifting responsibility onto maintainers who must now validate, debug, and assess licensing risks.

Beyond contribution quality concerns, Open Source communities are debating whether AI-generated outputs should be considered derivative works of the Open Source code they were trained on. If an AI model was trained on GPL, should its output require the same license? Does it even count as a derivative work? Some projects are outright rejecting AI-generated pull requests out of legal uncertainty. A lot of it is to be decided in legal courts, and countries have different stances with respect to copyright implication on a model training.

This debate extends to what “Open Source AI” should mean. The Open Source Initiative (OSI) recently introduced its Open Source AI Definition (OSAID) after a long community feedback process. OSI has allowed for some leeway with training data in the name of practicality. They see this as a practical compromise to encourage Open AI development, but a big part of community argue it undermines reproducibility: a core Open Source principle.

The Digital Public Goods (DPG) AI Community of Practice (CoP) recommendation takes an even stricter stance, recommending that AI solutions cannot qualify as Digital Public Goods unless their training data is fully open. This reflects a deeper divide: should Open Source AI allow partial transparency, or must all components including data be freely available to ensure accountability?

The Software Freedom Conservancy (SFC) has gone further, criticizing OSAID as a corporate-driven compromise that lets AI vendors retroactively label proprietary systems as "Open Source." SFC argues that Open Source AI must guarantee full reproducibility not just disclose methodology but provide all necessary components (including training data) to rebuild models independently. Additionally, AI raises broader ethical concerns, as many generative models train on creative works without consent from artists, writers, and musicians.

This growing divide raises critical questions: Will AI governance frameworks converge, or will multiple competing definitions emerge? Is Open Source evolving to accommodate AI, or is it being reshaped by corporate influence? Just as the Open Source Definition (OSD) has evolved over two decades, Open Source AI will likely need continuous refinement to balance openness, ethics, and sustainability.

We also notice the ever growing “open washing” specifically prominent with LLMs. Models like Meta’s Llama and DeepSeek R1 are pretending to be Open Source but they don’t meet even the conservative definition by OSI.

Looking Ahead

Let's get specific about what's coming—from "pretty much guaranteed" to "well, this could get interesting."

1. Security Becomes Unavoidable

For a long time, Open Source projects—especially those maintained by volunteers—could get away with minimal attention to security. That’s no longer the case. With regulatory frameworks like the CRA coming into effect and enterprises becoming more risk-averse, security expectations are shifting.

The challenge isn’t just that security is becoming more necessary. it’s that the responsibility for it is unevenly distributed. Larger, corporate-backed Open Source projects have the resources to implement structured security programs, but independent maintainers don’t always have the time, expertise, or incentive to do so. This has led to a growing question in Open Source governance: who should be responsible for making security compliance manageable?

Instead of framing this purely as a “hobbyist vs. professional” divide, the real issue is governance. Some projects will seek foundation support to help handle compliance (e.g., Linux Foundation’s OpenSSF initiatives), while others may shift toward private-sector partnerships. Meanwhile, unfunded, independently maintained projects may face tough decisions: either find external support, offload security responsibilities to downstream users, or risk being abandoned altogether.

The real shift in 2025 won’t just be the need for better security but the emergence of different models for handling it. Some centralized, some distributed. The question isn’t whether security will be required; it’s whether the burden will be fairly shared across different types of Open Source projects.

2. AI gets specific

I believe the AI hype train is running out. We're starting to see where AI actually helps and where it just creates more work. There is more work being done to highlight negative impact of LLMs on climate, be it in energy consumption or amount of water per query. There are also studies being done on LLM’s impact on critical thinking. Beyond the need of real “Open” AI systems, there is a need for open: full transparency and comprehensive reporting on resource consumption and environmental consequences.

Watch for:

• AI tools becoming more specialized and focused on specific development tasks.
• A decline in generic "AI-powered" projects.
• Better integration with existing workflows rather than trying to replace them.
• More emphasis on AI as an assistant rather than a replacement.
• Growing sophistication in handling AI-generated contributions (I wish).
• New patterns for managing AI in collaborative development.

3. Community Models Evolve

The old "if you build it, they will come" approach to Open Source communities is showing its age, and we’re seeing this shift within UNICEF Ventures, too. What's emerging is a more dynamic and intentional model:

• Regional tech hubs are becoming more influential than global ones.
• New hybrid collaboration models that actually work (not just Zoom fatigue).
• More emphasis on asynchronous communication tools.
• New metrics for measuring community health beyond commit counts.
• Creative approaches to maintainer sustainability.

4. Funding Models Get Weird

The widening gap between enterprise-backed Open Source and independent projects is forcing new funding models to emerge, but it’s not as simple as "some get paid, others don’t." The real change is in how projects are structuring financial sustainability.

Beyond traditional sponsorships and grants, we're seeing a diversification of Open Source funding models:

• Foundation-led funding (e.g., Sovereign Tech Fund, Open Source Collective, FLOSS Fund) supporting projects critical to infrastructure.
• Bounty-driven contributions becoming more structured through platforms like GitHub Sponsors or OpenSSF's security incentives, but also through localized initiatives designed to create new entry points for developers. For example, I’m currently working with the UNICEF Sierra Leone on a bounty platform aimed at empowering local developers. The goal is to provide both financial opportunities and structured incentives for learning and growth in tech, ensuring that Open Source can be a viable career pathway even in emerging markets.
• Cooperative funding models where maintainers pool resources to sustain projects collectively.

5. Tool Consolidation Accelerates

The sheer number of developer tools has reached a tipping point. Instead of improving productivity, tool fragmentation is making workflows more complex, and developers are starting to push back.

This isn’t just about big companies acquiring smaller tools, it’s about consolidation happening from the bottom up. Developers are increasingly favoring tools that integrate well rather than using a scattered collection of standalone solutions. The rise of developer platforms (e.g., GitHub Copilot, VS Code integrations like cursor, unified CI/CD suites) signals a shift away from managing multiple niche tools toward using fewer, more powerful solutions.

At the same time, standardization is emerging as a counterforce to unchecked tool sprawl. Projects are adopting common interfaces (e.g., OpenTelemetry for observability, SPDX for compliance) to reduce the need for bespoke integrations.

Instead of just corporate consolidation, 2025 will be about developer-driven consolidation where the tools that survive are the ones that reduce complexity rather than add to it.

What This Means For Those Working on or with Open Source Projects

Start planning for compliance now. Even if you think it doesn’t apply to you, understanding the requirements costs less than scrambling later.

Rethink your AI strategy. If you've been waiting to see how AI plays out, good call. Now's the time to develop clear policies about AI contributions and usage.

Stop assuming community support is infinite. Independent maintainers are burning out. If you rely on Open Source, you also need to support it. Whether through funding, contributor time, or upstream engagement.

Rethink sustainability as shared responsibility. Instead of assuming someone else is handling security, governance, or community health, ask how your organization is contributing. The Open Source ecosystem is only as healthy as the care we invest in it.

What This Means for UNICEF

At UNICEF Ventures, we advocate for Open Source as a means to promote digital inclusion and  transparency; we actively support Open Source in the development of digital public goods, working across several countries where openness enables adaptability, equity, and long-term sustainability. As Open Source governance evolves, so must we.

Here’s how we’re responding:

Building an internal Open Source Community of Practice (CoP): Aimed at supporting compliance and strengthening Open Source literacy across UNICEF, this CoP will connect staff across HQ and country offices to share experiences and co-develop practical guidance.

Improving compliance literacy across teams: Whether it's understanding licensing obligations or preparing for future regulatory shifts, we’re investing in upskilling our teams to navigate this landscape with confidence—not confusion.

Building locally relevant pathways into Open Source: Through helping initiatives like the upcoming DPG pipeline and bounty platform in Sierra Leone, we’re exploring how Open Source can be a meaningful career pathway for young developers in emerging markets—not just a volunteer activity.

Promoting real openness in AI and DPGs: As co-leads in the DPG AI Community of Practice, we’re pushing for clearer standards around transparency, reproducibility, and ethics in AI projects claiming to be open.

The Open Source world is becoming more complex, more regulated, and more fragmented. That's not necessarily bad, it might actually be healthier in the long run. But it does mean we need to be more thoughtful about how we build and maintain projects.

Thanks to Ben Cotton and Brian Exelbierd for their thoughtful posts that sparked these reflections. This is my take on where things are headed, heavily informed by their insights.