The practice of developing publicly accessible technologies and preventing monopolies of privately-owned, closed-source infrastructure was a pivotal technological movement in the 1990s and 2000s.
Open source models successfully paved a path for the development of a multitude of technologies, cultivating a culture of knowledge sharing, collaboration , and community along the way. Unfortunately, monetizing such projects has always been a challenge, and ensuring contributors are compensated for their contributions working on them, even more so.
On the other hand, closed-source projects offer greater control, security, and competitive advantage through proprietary innovation. These models allow companies to protect intellectual property and maintain strategic differentiation in the market. As a result, they’re significantly better funded and more lucrative.
While this may be a contributing factor for the current generation of coders that prioritize financial gain over ideology, there are also technological drawbacks in the debate between open and closed source models.
Many of the languages used to develop traditional open source technologies have been overtaken by popular alternatives – particularly in the wake of the current GenAI era. Over the past three years since the introduction of ChatGPT and other GenAI tools, Python rose to become the number one language of choice for people learning to code and ‘other coders’, as tracked in Stack Overflow’s 2024 Developer Survey.
Python was also an established and utilized programming language at the time, but C and C++ dominated systems programming and performance-critical applications, while Perl and PHP fueled the early growth of web development. These languages haven’t fallen completely out of favor, but Python is largely regarded as the language of AI – and new coders are taking note.
The appeal of developing proprietary systems for startups or innovative Big Tech companies is understandable for young developers looking for both inspiration and remuneration. With AI very much shaping the new frontier in technology and the LLM wars raging, it was common wisdom that deeply competitive, closed-source development would remain the status quo.
Until, of course, the launch of DeepSeek.
The rise of a new open-source era?
In December 2024, DeepSeek released DeepSeek-V3, an advanced open-source LLM designed to enhance performance in areas such as mathematics, coding, and language tasks. Wherever you may land on the security implications of using DeepSeek, it’s undeniable that its launch dropped a bomb on the AI ecosystem – and crucially, on open versus close source programming.
DeepSeek’s dedication to open source AI models has sparked meaningful discussions on openness and collaboration in AI development – and may well be the catalyst needed to ignite interest in open source development amongst young coders. While these projects may still aim to become commercially successful, they nonetheless democratize AI beyond major tech corporations and provide opportunities for collaboration within the developer community.
While this is undoubtedly a positive development, striking the right balance between open and closed source technology is essential for long-term innovation and business success. Open source approaches fuel rapid innovation, fosters developer trust, and creates a foundation for community-driven improvements. Open source technologies also provide transparency, flexibility, and cost efficiency, allowing developers to adapt and scale solutions quickly. But relying solely on open source can expose businesses to security risks, IP conflicts, and support limitations – which can have significant ramifications in our highly competitive, digital-first age.
Striking the right balance
The key to success – for both young coders and businesses – lies in adopting a hybrid strategy. Leveraging open source for foundational infrastructure and rapid development while building proprietary layers to create value-added differentiation can ensure long-term scalability and security.
By combining the strengths of both approaches, developers can innovate faster while businesses reduce costs and maintain a competitive edge, all the while safeguarding their intellectual property and enhancing customer trust.
This balance allows companies to harness the collaborative power of open source without sacrificing the strategic benefits of proprietary technology. And in turn, this approach will better serve the needs of the next generation of coders.
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