top of page
Writer's pictureEvan O'Donnell

Will Coding Automation Create a New Paradigm for Open Source?


This issue explores how coding automation could reshape the future of open source software.


 

01 | Code as a commodity

The way we build technology is undergoing a profound shift. New development standards and advancements in coding automation are making software development significantly faster and more accessible.


Code, once the most proprietary and costly component of the software stack, is rapidly becoming a commodity.


I’ve been reflecting on where this will most affect our industry, and I believe open source software will be at the center of this transformation.


I predict that our conception of open source – how it is used, who uses it, how it gets integrated into products – might look fundamentally different than in any prior era of technology.


 

02 | The pros and cons of open source

Open source software (OSS) is software where the source code is freely available for anyone to inspect, use, and modify for their own use case. It offers several benefits:


  • Integrating OSS into in-house applications allows for deeper customization and modularity, compared to buying all-in-one, proprietary software, because the underlying code can be directly copied and edited to meet specific business needs.


  • A large community of contributors leads to rapid improvements, organic adoption, robust tooling, and (oftentimes) enhanced security, as large networks of developers identify and patch vulnerabilities.


  • OSS is often more cost-effective, allowing firms to circumvent all-in-one licensing fees and vendor lock-in. 97% of firms report net cost savings from using OSS, and without it, companies would need to spend 3.5x more on software.


However, OSS has historically been a tough sell for investors.


Generating revenue and building a competitive edge are challenging when the core product (the codebase) is free and accessible. Only a few open source companies – like MongoDB and Databricks – have surpassed $1B in revenue, with business models relying on peripheral services like support, hosting, or premium security.


Additionally, OSS implementation requires technical expertise, which limits broader adoption. Closed source alternatives – with their plug-and-play functionality, user-friendly APIs, and dedicated support – appeal to buyers seeking ease of use.


As a result, open source companies receive less than 5% of global software spend [1] and under 5% of U.S. VC investment.


 

03 | A spike in open source

In the past several years, open source projects have gained ground, especially vis-a-vis closed-source alternatives.


For example, open source data management systems are now as preferred as proprietary ones, a sharp contrast from a decade ago when proprietary solutions were nearly twice as popular. [2]



Two-thirds of companies increased their use of OSS last year, particularly in (a) machine learning, where frameworks like TensorFlow and PyTorch dominate, and (b) data processing, where companies like Netflix and Uber find Apache Hadoop and Spark now suitable for enterprise-scale workflows.


Moreover, open source's highly-engaged communities and large user bases are often driving faster, more efficient revenue growth than closed-source software, which usually relies on traditional sales and marketing. [3]



What’s driving this spike?


This rise in open source adoption and commercialization is closely tied to the standardization of web development.


I’ve written before how frameworks like React / Next make front-end development easier. In addition, standardized tools like Docker and Kubernetes, RESTful APIs, and microservices [4] have made integrating and customizing open source software significantly faster and more efficient.


As coding automation continues to advance, we may be entering a new era, one where open source is orders of magnitude more powerful and accessible.


 

04 | Will coding automation create a new paradigm for open source?

AI-driven coding automation is poised to revolutionize software development.


Programming assistants like GitHub Copilot are automating code generation, review, and deployment. (Already, Copilot estimates 55% time savings on task completion for developers.)


Like past technology cycles, where digital distribution and data storage costs fell to near-zero, the structural barriers that have always limited open source adoption – the costs of creating, customizing, debugging, and deploying code – are rapidly declining.


As this trend accelerates, software’s value proposition may shift in favor of open source. If managing OSS is more cost-effective, more customizable, and now simpler than closed-source solutions, it may well become the dominant model for software development. This may be especially true as software budgets get strained, buyers get more discerning around ROI, and sales cycles extend.


Here are key areas where AI tooling could reshape open source:



  • Wider access: Natural language processing will allow non-technical users to manipulate open source code using plain language. This opens OSS to a much wider audience, rapidly expanding the market for software development.


  • Automated maintenance: AI will streamline the maintenance and security of OSS in live environments by automating updates, proactively resolving bugs, and eliminating the need for manual oversight. This will make OSS even more reliable and secure, addressing concerns that have historically slowed its adoption in enterprise settings.


  • New business models: AI-enabled customization, security, and performance monitoring could create new, recurring revenue streams for OSS companies – and a more scalable cost structure for providing those services. For example, open source provider Elastic began charging for AI-powered features like performance monitoring and security in its Elastic Cloud business, boosting that segment’s revenue by 29% year-over-year and growing its share of total revenue to 43% in 2024, up from 35% in 2022. [5]


  • Faster R&D: Open source relies on global networks of developers to maintain code. However, collaboration at a large scale often faces bottlenecks, such as problems with code merging, conflict resolution, and quality control. AI tools are providing automated solutions for these issues, enabling open source to innovate faster than closed source in terms of quality and deployment speed.


AI is reducing the cost of code generation to near-zero, breaking down the long-standing barriers to OSS integration and monetization.


In this new paradigm, the fundamental value in software could shift – from code itself, to the unique ways it is shaped and customized directly by the end user.


As a result, open source may soon have an enduring competitive edge over proprietary software.



 

[1] In 2022, OSS spend was estimated at $25B (source) and the overall software market was estimated at $583.5B (source).

[2] Source: https://db-engines.com/en/ranking_osvsc. The DB-Engines Ranking measures popularity by combining factors like web mentions, search trends, technical discussions, job postings, professional profiles, and social media activity. These metrics are standardized and averaged to create a relative popularity score for each database system. 

[4] A microservice is a software architecture where an application is built from small, independent services, each handling a specific function. Unlike monolithic systems, microservices can be developed, deployed, and scaled separately, offering greater flexibility, modularity, and easier maintenance.

[5] Elastic N.V. (2024). Q4 2024 shareholder letter. Source. Pages 21, 59.

Comments


Commenting has been turned off.
bottom of page