Why open source matters in an AI world

Open source remains a critical strategy for tech companies in the AI era, argues a former Borland employee drawing on lessons from Borland's failed open-source move with InterBase. Nvidia leverages its proprietary CUDA layer to lock developers into its GPU hardware, mirroring how Microsoft and Amazon used open-source tools to drive cloud adoption. The key takeaway: courting developers with free software is essential for commercial success, as developers ultimately decide which technologies win.

I’m an old Borland guy. I started using Borland tools in the early 1990s, from Turbo Pascal through Delphi. I dabbled in Paradox. I even tried to stick with Borland Office. I wandered through the Inprise years https://en.wikipedia.org/wiki/Borland Renaming to Inprise Corporation , and the Kylix endeavor https://en.wikipedia.org/wiki/Borland Kylix , and I was actually a Borland employee during the CodeGear https://en.wikipedia.org/wiki/CodeGear / Embarcadero https://en.wikipedia.org/wiki/Embarcadero Technologies migrations. You could write a book about the rise and fall of Borland. Suffice it to say that things got dodgy when Borland strayed from its focus on developer tools, and they never really recovered. One of Borland’s missteps came with the open-sourcing of InterBase https://en.wikipedia.org/wiki/InterBase , its RDBMS. In the early 2000s, open-source software https://www.infoworld.com/article/2262355/what-is-open-source-software-open-source-and-foss-explained.html emerged from the academic shadows and began to commercialize. With the IPO of Red Hat https://en.wikipedia.org/wiki/Red Hat , everyone was jumping on the Linux and open-source bandwagon. Borland made their move into this arena with InterBase. Two things soon happened. First, the Firebird https://www.firebirdsql.org/en/start/ project was created as a fork of the InterBase source. Second, Borland retreated from the open-source project and reincorporated InterBase as a closed-source product. One might mark this as the beginning of developers losing faith in Borland. And it always ends up being about the developers, right? I mean, who can forget a sweaty, maniacal Steve Ballmer jumping around https://www.youtube.com/watch?v=8fcSviC7cRM onstage screaming “Developers, developers, developers”? For many years, Microsoft was notoriously anti-open-source, but even they eventually recognized the power of open-sourcing major tools like .NET https://www.infoworld.com/article/2264488/what-is-the-net-framework-microsofts-answer-to-java.html and Visual Studio Code https://www.infoworld.com/article/2335960/what-is-visual-studio-code-microsofts-extensible-code-editor.html . But Microsoft didn’t do that out of the goodness of their heart — they clearly saw those tools as a means to bringing developers to their Azure cloud platform. When Amazon launched AWS, they clearly were sidestepping the IT procurement process, making it easy for developers to spin up VMs with just a credit card. Apple shipped the iPhone without an SDK, expecting developers to figure things out. That didn’t last long, and they soon course-corrected. Open source is great — but always remember that it offers benefits to companies beyond the software development process. Borland fumbled the ball with their open source efforts, but it soon became obvious how to leverage open source for corporate success. Or, as Stephen O’Grady argues in his book The New Kingmakers https://thenewkingmakers.com/ , developers are the ones who decide what technologies succeed. Companies quickly recognized that courting developers with open-source software and other free resources was the way to bring them into the fold. Because developers write all the software that makes a tool, platform, or application successful, it’s always a smart move to make developers happy. AI hasn’t changed this equation. The latest incarnation can be seen from the company Nvidia. We think of Nvidia as a hardware company, cranking out GPUs as fast as they can. But GPUs don’t sell unless there is software that runs on them. Coding against a GPU in plain C++ was miserable, so Nvidia developed CUDA https://www.infoworld.com/article/2256401/what-is-cuda-parallel-programming-for-gpus.html , a proprietary layer that made building for their hardware relatively easy. And the more CUDA is used, the more people depend on Nvidia hardware. This is not an accident. As Nader Khalil, Nvidia’s Director of Developer Technologies, told me, “You have to make the best hardware that you can, and you have to make the best software to utilize it. Software is the part of the stack that touches the user.” Today, of course, developers are all about agentic coding, and Nvidia is eyeball deep in that world. As such, Nvidia provides a set of open-weight models called Nemotron https://developer.nvidia.com/topics/ai/nemotron that developers can use, inspect, and fine-tune for their own purposes. And of course Nemotron runs really well on top of CUDA. Nvidia, like Microsoft and Amazon, is doing this because they want to sell their products to more people. Nvidia can’t make enough GPUs for Anthropic and OpenAI to keep up with demand, but they also want to sell GPUs to us developers by enabling us to run our own models on our own computers. The open source movement began with pure hearts and noble ideals. But the reality of open source is that its nobility is easily melded with business needs. Open source software is really the bait — and developers are the real catch.