“Generative AI, commonly referred to as GenAI, stands at the forefront of a technological revolution, profoundly altering diverse sectors by synthesizing vast amounts of data and generating new outputs,” said the “2023 Open Source Generative AI Survey Report,” published by the Linux Foundation (LF) in December of 2023. “From creating intricate artworks and composing music to designing novel pharmaceutical compounds and simulating realistic human language, the potential applications of GenAI are vast and transformative.”
“The open source approach, rooted in principles of transparency, collaboration, and shared innovation, holds transformative potential for the advancement of GenAI technologies. By democratizing access to AI algorithms and datasets, open source initiatives allow a broad and diverse pool of developers to contribute to, refine, and critique GenAI systems. This collective intelligence accelerates the pace of innovation and uncovers and rectifies biases or vulnerabilities that might otherwise go unnoticed in closed development environments.”
To help understand the potential impact of open source GenAI on the market, LF Research and LF AI & Data launched a worldwide survey. The survey explored the current state of GenAI technologies in companies, including models, databases, applications, and frameworks.
The survey received 284 responses, 92% from the US and Canada.
- 29% of respondents worked for companies with less than 999 employees, 47% worked for companies with between 1,000 and 9,999 employees, and 24% worked for companies with more than 10,000 employees.
- 88% worked for companies extremely or very reliant on open source software (OSS), and 11% worked on less OSS-reliant companies.
- 37% worked for IT companies and 57% worked for companies outside the IT industry.
- 86% were extremely or very familiar with their organization’s adoption of GenAI.
- 31% of respondents were AI or ML engineers, 20% were non-IT senior/executive managers, 17% were in IT management, 13% data scientists, 6% product managers, 6% marketing/communications, and 4% developer/software engineer.
Let me summarize the survey’s key findings.
Overall Strategy and Plans
How important is GenAI to the future of the company you work for? Extremely important - 21%; moderately important - 42%; slightly important - 25%; neither important or unimportant - 3%; unimportant - 9%.
To what extent is your company involved with GenAI? Extremely involved - 31%; very involved - 49%; involved - 14%; slightly involved - 5%; not involved at all - 1%.
What percentage of the overall IT budget will your company invest in GenAI in the next 12 months? Almost entirely focused on GenAI - 9%; a large percentage encompassing several projects - 51%; a moderate portion of the budget - 29%; a small percentage for pilot projects - 8%; and no investments at all - 0%.
Identify key areas where your organization expects to develop and use GenAI: Quality assurance - 35%; software testing - 34%; documentation of software and applications - 34%; cybersecurity analysis and mitigation - 31%; software development - 29%; marketing, sales collateral and articles - 23%; customer service, support and recommendations - 20%; access to company knowledge and data - 20%; language understanding and translation -19%; customer satisfaction analysis - 14%; personal assistants - 14%; education and training - 13%; research - 11%; and finance - 11%.
Where does your company use or plan to use GenAI technologies? Enhance internal processes - 16%; embed into existing products and services - 29%; and create new products and solutions - 55%.
How does your company employ or plan to employ GenAI technologies? Out of the box with little or no customization - 13%; extensive customization to fit company’s needs - 57%; and develop in-house GenAI technologies - 30%.
If your company does not deploy GenAI systems in the next 12 months, what are the primary reasons? Security - 49%; cost - 33%; technology maturity - 31%; no in-house AI expertise - 17%; no compelling business case - 14%; and does not apply to us - 12%.
Open Source Considerations
The definition of open-source software has been around for about 25 years. But open-AI systems require distinct definitions, protocols, and development processes because AI systems don’t behave like traditional software. The Open Source Initiative (OSI) is currently developing an Open Source AI Definition that’s being reviewed with the AI community.
“The level of openness can vary greatly between the different GenAI models currently available, but most of them would likely not earn the open source title, since availability and access to the underlying code, data, model, and documentation are rare,” said the LF Survey Report. “The open approach is vital, as confirmed by our survey respondents’ concerns about the openness of the GenAI technologies they are using or developing.”
Does your organization prefer proprietary or open source AI models? Prefer open source - 41%; 9% prefer proprietary - 9%; and both types are fine - 50%.
How concerned is your organization about the openness of the GenAI systems you’re developing or using? Responses varied depending on how the organizations intended to deploy their GenAI systems.
- Out of the box with little or no customization: Extremely concerned - 16%; moderately of slightly concerned - 45%; unconcerned or neither concerned/unconcerned 39%.
- Extensive customization to fit company’s needs: Extremely concerned 17%; moderately or slightly concerned 72%; unconcerned or neither concerned/unconcerned 12%.
- Develop in-house GenAI technologies: Extremely concerned 20%; moderately or slightly concerned 67%; unconcerned or neither concerned/unconcerned 13%.
How much would your organization’s data control and transparency change if the GenAI technologies were open source? Significantly increase - 14%; moderately or slightly increase - 55%; stay the same - 8%; moderately or slightly decrease - 16%; significantly decrease - 2%.
For each of the following security concerns, which type of GenAI solutions would you prefer?
- Security: open source 46%; proprietary 33%; the same 21%.
- Privacy: open source 42%; proprietary 37%; the same 22%.
- Trustworthy data and models: open source 42%; proprietary 34%; the same 24%.
- Regulatory compliance: open source 39%; proprietary 38%; the same 23%.
For each of the following adoption concerns, which type of GenAI solutions would you prefer?
- Widespread adoption: open source 42%; proprietary 36%; the same 21%.
- Access to diverse data and models: open source 42%, proprietary 35%; the same 23%.
- Transparency and reproducibility: open source 42%, proprietary 37%, the same 21%.
- Cost and budget: open source 41%; proprietary 32%; the same 27%.
For each of the following governance considerations, which type of GenAI solution would you prefer?
- Collaboration and community involvement: open source 43%; proprietary 37%; the same 20%.
- Ease of integration: open source 43%; proprietary 33%; the same 24%.
- Long term sustainability: open source 42%; proprietary 32%; the same 26%.
- Responsible AI and ethical considerations: open source 40%; proprietary 39%; the same 22%.
- Rapid iteration and innovation: open source 39%; proprietary 36%; the same 25%.
For each of the following business consideration, which type of GenAI solution would you prefer?
- Ability to align with business needs: open source 41%; proprietary 33%; the same 26%.
- User experience: open source 38%; proprietary 41%; the same 21%.
- Support and maintenance: open source 39%, proprietary 41%; the same 20%.
- Performance/scalability: open source 37%, proprietary 36%, the same 27%.
- Accuracy: open source 35%, proprietary 36%; the same 28%.
Conclusions
Businesses are concerned by the openness of the GenAI technologies they are using. “Around two-thirds of respondents are either extremely or moderately concerned about this aspect, reflecting the importance of transparency and control in technology deployments.”
Survey respondents generally lean in the direction of open source. “This finding highlights a recognition of the benefits associated with open source technologies, including transparency, reproducibility, access to diverse data and models, and ease of integration. Security, an important concern for any technology deployment, does not appear to be a deterrent for open source GenAI adoption.”
A neutral governance approach is key to GenAI development. “Neutral governance is not only crucial for fostering responsible growth of GenAI but also for ensuring that its benefits are widespread and aligned with societal values. This approach is vital in maintaining the integrity and sustainability of GenAI advancements, ensuring that they serve both communities and stakeholders.”
Comments