I think many of us have already noticed that a growing number of open-source projects and libraries are moving towards commercial or dual-licensing models.
In the .NET ecosystem, several widely used libraries have taken this path over the past year or so. AutoMapper and MediatR introduced commercial editions under a dual-licensing model, Fluent Assertions began requiring a paid licence for commercial use with version 8, and MassTransit 9 became a commercial product.
These libraries were widely used in .NET applications and I mean widely used. Many projects treated them almost as a standard part of the ecosystem.
Now, the same change is reaching the frontend world. PrimeTek recently announced that future major versions of PrimeNG, PrimeReact and PrimeVue will no longer be released as open source.
All these projects were widely adopted, and many commercial applications depended heavily on them. Their licensing changes were primarily driven by the cost of long-term maintenance, but this raises a broader question:
Is AI also changing the world of open source?
You have probably already read many articles about code inflation. With AI, we can generate a huge amount of code in a very short time, even if the quality is sometimes questionable.
The same thing is happening in open source. Maintainers can now receive more AI-generated issues, pull requests and feature requests than they can realistically review. Producing code has become cheaper, but understanding, testing and maintaining that code still requires significant human effort. Maintainers can become overwhelmed very quickly.
AI may also discourage some developers from publishing their work publicly. Even small experiments, educational repositories and proof-of-concept projects can become training material for large language models. Some authors may therefore decide to keep their repositories private because they do not want AI companies learning from their work without permission, attribution or compensation.
Licensing creates another difficult question. Imagine that an author spends months building and publishing a project under the GNU GPL. An LLM reads the code and documentation and then helps create a competing closed-source implementation in a fraction of that time.
Proving that an AI-generated implementation was influenced by GPL-licensed code may be almost impossible for an individual maintainer.
Security is another concern. AI makes it easier to generate contributions, but it also makes it faster to discover vulnerabilities and incorrect code. Of course, finding vulnerabilities is generally a good thing. AI can help improve security and identify problems that might otherwise remain unnoticed. However, there is another side to this. Attackers can use the same tools to analyse publicly available source code, discover vulnerabilities much faster and exploit them for their own benefit. Without AI, this kind of analysis could require considerable time, expertise and effort.
None of these problems started with AI. Open-source maintainers have struggled with funding, burnout, cloud providers and commercial exploitation for years. However, AI may be accelerating all of these problems very quickly.
I am curious whether the next generation of developers will still be willing to publish their work in the same way. Even if much of the code is generated by AI, it will still require human effort to make it reliable, maintainable and useful.
Open source will probably not disappear. However, we may see more projects moving towards stricter licensing, free-to-use products with closed source code, or completely commercial development.
What about other major ecosystems, such as Java or Python? Is the same thing happening there too?
And if you were starting a valuable project today, would you still release it as open source?