All-In Breakdown

Open source vs closed source AI

Topic

A central debate in the AI industry regarding whether AI models should be publicly available (open source) or proprietary (closed source). OpenAI follows a closed-source approach for its frontier models.

First Mentioned

10/12/2025, 6:49:23 AM

Last Updated

10/12/2025, 6:50:15 AM

Research Retrieved

10/12/2025, 6:50:15 AM

Summary

The debate between open-source and closed-source Artificial Intelligence centers on fundamental differences in access, transparency, and control. Open-source AI, characterized by its free availability for use, study, modification, and sharing of datasets, code, and model parameters, fosters collaboration and transparency, with proponents suggesting it may ultimately surpass proprietary systems due to faster development cycles and wider participation. Conversely, closed-source AI is proprietary, restricting access to its internal workings to protect intellectual property and maintain a competitive edge. While open-source models like Meta's Llama 3 are rapidly advancing, companies like OpenAI, led by Sam Altman, defend their closed-source approach for frontier models, citing the need to protect their mission and ensure safe AGI development. The discussion also encompasses issues like "openwashing," where systems are marketed as open but offer limited access, and the concept of "open-weight" models. Both approaches present risks and benefits, with open-source facing concerns about malicious actors removing safety protocols, and closed-source criticized for dependence, privacy issues, opaque algorithms, and potential innovation stagnation. Sam Altman's insights highlight the broader implications for AI, including the need to reduce costs and latency, develop AI chips, explore new hardware paradigms, and address complex issues like AI copyright, fair use, and global AI regulation, proposing Universal Basic Compute as a model for distributing AI's benefits.

Referenced in 1 Document
Research Data
Extracted Attributes
  • Concept: Openwashing

    Marketing AI systems as 'open' while offering limited access (e.g., API only, restrictive use licenses).

  • Open-source AI Advantages

    Wider access, increased collaboration, transparency, faster development cycles, wider participation, increased scrutiny, better recruitment, greater understanding of models, facilitates bias identification, accelerates scalability improvements, ethical AI solutions.

  • Definition: Open-source AI

    AI system freely available to use, study, modify, and share, including datasets, code, and model parameters.

  • Sam Altman's Vision for AI

    Reducing AI cost and latency, developing AI chips, exploring new hardware paradigms beyond smartphones, conceptualizing AI Agents as highly capable 'senior employees', advocating for Universal Basic Compute.

  • Closed-source AI Advantages

    Protection of intellectual property, competitive advantage, enhanced performance, ease of use, commercial protection.

  • Concept: Open-weight models

    Large language models where trained parameters are publicly available, even if training code and data are not.

  • Definition: Closed-source AI

    Proprietary AI system restricting access to source code and internal components.

  • Common Open-source AI Licenses

    Apache License, MIT License, GNU General Public License

  • Open-source AI Disadvantages/Risks

    Potential for malicious actors to remove safety protocols, reliance on trust in other open-source components.

  • Closed-source AI Disadvantages/Risks

    Issues of dependence, privacy concerns, opaque algorithms, corporate control, limited availability, potential stagnation of innovation.

  • Sam Altman's Stance on Closed-source AI

    Defends OpenAI's proprietary approach for frontier models as necessary for its mission and safe AGI development.

Timeline
  • Sam Altman's temporary firing and rehiring at OpenAI, attributed to disagreements with the OpenAI Nonprofit Board over the pace and methods for pursuing safe Artificial General Intelligence (AGI), highlighting governance issues relevant to AI openness. (Source: Related Documents)

    2023-11

  • New York S.B. 822 becomes effective, amending existing law on AI and employment regarding state agencies and prohibiting the use of AI to affect existing rights of employees pursuant to a collective bargaining agreement, reflecting emerging AI regulation. (Source: Web Search Results)

    2025-07-01

Wikipedia
View on Wikipedia
Open-source artificial intelligence

Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. Free and open-source software (FOSS) licenses, such as the Apache License, MIT License, and GNU General Public License, outline the terms under which open-source artificial intelligence can be accessed, modified, and redistributed. The open-source model provides wider access to AI technology, allowing more individuals and organizations to participate in AI research and development. In contrast, closed-source artificial intelligence is proprietary, restricting access to the source code and internal components. Companies often develop closed products in an attempt to keep a competitive advantage in the marketplace. However, some experts suggest that open-source AI tools may have a development advantage over closed-source products and have the potential to overtake them in the marketplace. Popular open-source artificial intelligence project categories include large language models, machine translation tools, and chatbots. For software developers to produce open-source artificial intelligence (AI) resources, they must trust the various other open-source software components they use in its development. Open-source AI software has been speculated to have potentially increased risk compared to closed-source AI as bad actors may remove safety protocols of public models as they wish. Similarly, closed-source AI has also been speculated to have an increased risk compared to open-source AI due to issues of dependence, privacy, opaque algorithms, corporate control and limited availability while potentially slowing beneficial innovation. There also is a debate about the openness of AI systems as openness is differentiated – an article in Nature suggests that some systems presented as open, such as Meta's Llama 3, "offer little more than an API or the ability to download a model subject to distinctly non-open use restrictions". Such software has been criticized as "openwashing" systems that are better understood as closed. There are some works and frameworks that assess the openness of AI systems as well as a new definition by the Open Source Initiative about what constitutes open source AI. Some large language models are released as open-weight, which means that their trained parameters are publicly available, even if the training code and data aren't.

Web Search Results
  • Open-Source AI vs. Closed-Source AI: What's the Difference?

    The choice between open-source vs. closed-source AI models can impact innovation, cost, and even ethical considerations.

  • Open-Source LLMs vs Closed: Unbiased Guide for Innovative ...

    Large Language Models (LLMs) are advanced AI systems that can understand and generate different forms of content which include human like text, code, images, video, and audio. Such transparency strengthens the integrity of AI applications, making open-source LLMs a trusted choice for developers and businesses focused on ethical AI solutions. Does your workflow demand bespoke AI features that open-source models can uniquely provide, or do you value a streamlined, ready-to-use solution that minimizes technical overhead? A software company licenses a closed-source LLM from a major AI technology provider for use in their customer service chatbot. * If you have highly skilled AI users in-house who can adapt the open-source models to unique use cases * If you value ethical AI and ability to audit and adapt the AI tools you use.

  • Emerging AI: Open vs Closed Source - Center Forward

    As generative AI continues to evolve, crucial discussions are being raised around the governance of AI technologies, particularly in open versus closed source models. In a brief overview, open-source generative AI allows for collaboration and transparency, enabling diverse contributors to refine and enhance the technology. Conversely, open AI fosters increased scrutiny, better recruitment, and a greater understanding of models while facilitating bias identification and accelerating scalability improvements through collective innovation. Organizations looking to adopt generative AI currently have access to both closed- and open-source models. As organizations navigate the complexities of generative AI, they must weigh the benefits of open models—such as increased transparency, community engagement, and collaborative innovation—against the advantages of closed systems, which often provide enhanced performance, ease of use, and commercial protection.

  • Open-Source vs. Closed-Source LLMs: Weighing the Pros and Cons

    [Skip to content](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#main) * [Industry Focus](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#) * [Solutions](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#) * [](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#)#### Lydonia Solutions → [](https://lydonia.ai/solutions/) ###### [Lydonia Solutions Overview](https://lydonia.ai/solutions/) [](https://lydonia.ai/solutions/ai-solutions/) ###### [AI & Agentic Solutions](https://lydonia.ai/solutions/ai-solutions/) [](https://lydonia.ai/solutions/data-solutions/) ###### [Data Solutions](https://lydonia.ai/solutions/data-solutions/) [](https://lydonia.ai/solutions/automation-solutions/) ###### [Automation Solutions](https://lydonia.ai/solutions/automation-solutions/) [](https://lydonia.ai/solutions/security-solutions/) ###### [Security Solutions](https://lydonia.ai/solutions/security-solutions/) [](https://lydonia.ai/contract-lifecycle-management-reinvented/) ###### [CLM Solutions](https://lydonia.ai/contract-lifecycle-management-reinvented/) * [](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#)#### Lydonia Services → [](https://lydonia.ai/services/) ###### [Lydonia Services Overview](https://lydonia.ai/services/) [](https://lydonia.ai/ai-agentic-services/) ###### [AI & Agentic Services](https://lydonia.ai/ai-agentic-services/) [](https://lydonia.ai/data-services/) ###### [Data Services](https://lydonia.ai/data-services/) [](https://lydonia.ai/automation-services/) ###### [Automation Services](https://lydonia.ai/automation-services/) [](https://lydonia.ai/security-services/) ###### [Security Services](https://lydonia.ai/security-services/) [](https://lydonia.ai/contract-lifecycle-management-services/) ###### [CLM Services](https://lydonia.ai/contract-lifecycle-management-services/) * [Resources](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#) * [Industry Focus](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#) * [Solutions](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#) * [](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#)#### Lydonia Services → [](https://lydonia.ai/services/) ###### [Lydonia Services Overview](https://lydonia.ai/services/) [](https://lydonia.ai/ai-agentic-services/) ###### [AI & Agentic Services](https://lydonia.ai/ai-agentic-services/) [](https://lydonia.ai/data-services/) ###### [Data Services](https://lydonia.ai/data-services/) [](https://lydonia.ai/automation-services/) ###### [Automation Services](https://lydonia.ai/automation-services/) [](https://lydonia.ai/security-services/) ###### [Security Services](https://lydonia.ai/security-services/) [](https://lydonia.ai/contract-lifecycle-management-services/) ###### [CLM Services](https://lydonia.ai/contract-lifecycle-management-services/) * [Resources](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#) * [Industry Focus](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#) * [Solutions](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#) * [Resources](https://lydonia.ai/open-source-vs-closed-source-llms-weighing-the-pros-and-cons/#)

  • Practical Considerations in Choosing Open-Source or Closed ...

    [Skip Nav](https://www.wilsonelser.com/publications/practical-considerations-in-choosing-open-source-or-closed-source-ai-for-business-workflows#page-title) [Insights ### Practical Considerations in Choosing Open-Source or Closed-Source AI for Business Workflows August 6, 2024 Author: Sarah Fink](https://www.wilsonelser.com/publications/practical-considerations-in-choosing-open-source-or-closed-source-ai-for-business-workflows) The next piece of legislation, New York S.B. 822, effective July 1, 2025, amended existing law on AI and employment regarding state agencies and prohibits the use of AI to affect existing rights of employees pursuant to a collective bargaining agreement. The next piece of legislation, New York S.B. 822, effective July 1, 2025, amended existing law on AI and employment regarding state agencies and prohibits the use of AI to affect existing rights of employees pursuant to a collective bargaining agreement.