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McKinsey: Open Source Technology in the Age of AI

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Summary of https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/open%20source%20technology%20in%20the%20age%20of%20ai/open-source-technology-in-the-age-of-ai_final.pdf

Based on a survey of technology leaders and senior developers, the document explores the increasing adoption of open source solutions within AI technology stacks across various industries and geographies.

It highlights that over half of respondents utilize open source AI in data, models, and tools, driven by benefits like performance, ease of use, and lower costs compared to proprietary alternatives. However, the report also acknowledges perceived risks associated with open source AI, including cybersecurity, regulatory compliance, and intellectual property concerns, and discusses the safeguards organizations are implementing to mitigate these issues.

Ultimately, the survey indicates a strong expectation for continued growth in the use of open source AI technologies, often in conjunction with proprietary solutions.

  • Open source AI is widely adopted and its use is expected to grow, with over 50 percent of respondents using it in data, models, and tools areas of the tech stack. Seventy-five percent of respondents anticipate increasing their use of open source AI technologies in the next few years.
  • Key benefits driving the adoption of open source AI include lower implementation costs (60 percent of respondents) and lower maintenance costs (46 percent) compared to proprietary tools. Performance and ease of use are also top reasons for satisfaction. Developers value experience with open source tools for their careers and job satisfaction.
  • Despite the benefits, organizations perceive higher risks with open source AI, particularly regarding cybersecurity (62 percent of respondents), regulatory compliance (54 percent), and intellectual property (50 percent). Organizations are implementing safeguards like guardrails and third-party evaluations to manage these risks.
  • Organizations show a preference for partially open models (models with open weights but potentially non-OSI-approved licenses or limited data), which may be influenced by the performance of such models and the ability to self-host them for better data privacy and control.
  • The AI technology landscape is evolving towards a hybrid approach, with most organizations open to using a mixture of open source and proprietary solutions across their tech stack. Popular open source tools are often developed by large technology companies like Meta (Llama) and Google (Gemma).
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