In the last two decades we have witnessed a significant increase in the release of open-source products and services from leading publicly traded technology companies.
While open-source software has always played a role in the technology industry, leading to universally democratized products like Linux, Python, and JavaScript, both startups and established companies are using open-source as a way to sell profitable enterprise integrations.
By sharing light versions of their products for free, companies can build organic developer communities and generate new leads for their business. Signals from open-source reflect both the development of distributed systems and the demand for these systems, which could be growing exponentially thanks to AI-assisted code generation.
These signals are especially relevant for highly competitive and emerging industries, including:
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Large Language Models (LLMs)
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Vibe Coding
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Customer Relationship Management (CRM)
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Messaging
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Databases
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Code Editors
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Payments
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Voice Agents
Software developers contribute to and engage with open-source repositories by starring, forking, and watching relevant codebases. By measuring the adoption of open-source products using GitHub stars as a proxy, we can help investors anticipate and try to predict the success or failure of a technology company.
Want to learn more? Check out the full paper as we dive into the data behind the growth of open-source activity amongst prominent publicly traded technology companies and some of the private AI companies looking to disrupt them.
Sherlock Data, a product incubated at the Fidelity Center for Applied Technology, tracks open-source code contributions and engagement amongst leading public code repositories. To learn more, visit data.sherlockanalytics.com.