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What “LFR GenAI 2024” refers to
LFR GenAI 2024 is shorthand for Shaping the Future of Generative AI: The Impact of Open Source Innovation, a report published by The Linux Foundation in November 2024 with LF AI & Data and the Cloud Native Computing Foundation (CNCF). Its central question is what role open source plays in the evolution and implementation of generative AI in organizations.
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The report is an organizational survey, not a study of consumers’ personal use of chatbots or other GenAI products. Its results describe the organizations represented by its respondents at the time of the survey.
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Linux Foundation Research and its partners ran a web survey from August through September 2024. It received completed responses from 316 people. To qualify, respondents had to work for an organization, have professional experience, and be familiar with GenAI adoption at their organization.
Respondents were recruited through Linux Foundation subscribers, members, partner communities, and social media. They represented industry-specific companies, IT vendors and service providers, nonprofits, academia, and government, and came from the Americas, Europe, Asia-Pacific, and the rest of the world.
The report gives a margin of error of ±4.7% at a 90% confidence level and ±5.5% at a 95% confidence level for its sample size. Percentages may not total 100% because of rounding. The screened, recruited sample is not a census of all organizations, so the figures should be read as survey findings rather than universal rates.
What the adoption figures mean
The report’s summary says, “Currently, 94% of organizations are using GenAI.” More precisely, 94% of organizations represented in the survey were involved with GenAI. Involvement is broader than adoption at a particular level: 84% reported moderate, high, or very high GenAI adoption. The two percentages answer different questions and should not be substituted for one another.
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Both figures reflect responses collected in August–September 2024. They do not establish the proportion of organizations using GenAI today or confirm how adoption changed after the survey.
How much GenAI infrastructure was open source?
On average, respondents said 41% of an organization’s code infrastructure supporting GenAI was open source. This describes the reported share of code infrastructure, not the share of models, workloads, organizations, or GenAI products that were open source.
The reported average differed by adoption level: organizations classed as higher GenAI adopters reported an average open-source share of 47%, compared with 35% among lower adopters. This is an association in the survey; it does not show that open-source infrastructure caused higher adoption.
How respondents viewed open source
In the 2024 survey, 71% of respondents said open source had a positive influence on decision-making. Separately, 83% agreed or strongly agreed that AI needs to become increasingly open, and 82% regarded open-source AI as critical to a sustainable AI future. These are respondents’ reported views, not measurements of a settled industry consensus.
Looking ahead from the survey, 73% of organizations expected to increase their use of open-source GenAI tools over the following two years, including 26% that anticipated a substantial increase. Those numbers record expectations expressed in 2024; they do not verify that the increases occurred.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the report says about implementation
The report describes several parts of the GenAI stack rather than recommending one architecture. It identifies TensorFlow and PyTorch as frameworks used to build and train GenAI models, and LangChain and LlamaIndex as application frameworks for inference. It also discusses cloud infrastructure and Kubernetes as an enabler for scaling inference workloads. These are examples of technologies in the report, not endorsements or requirements.
Among organizations serving or self-hosting GenAI models, 50% reported using Kubernetes for some or all inference workloads. This figure applies to that subset and workload context; it does not mean half of all surveyed organizations used Kubernetes for all GenAI activity.
The implementation choices raised by the report are useful as decision dimensions, not as a product ranking:
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- Inference operations: use managed inference or serve models on infrastructure the organization operates.
- Openness: assess how much of the code is open source and how the relevant projects are governed.
The survey does not establish that one combination is best for every organization. Its figures indicate that open-source code formed a substantial part of reported GenAI infrastructure and that many respondents valued openness; architecture still depends on an organization’s needs and operating context.
How to interpret the report in 2026
The report is useful as a dated baseline for organizational attitudes and implementation in 2024. Its adoption levels, infrastructure shares, and expectations should not be presented as current 2026 measurements. In particular, the two-year projection was a forecast made by respondents in 2024, not a later audit of what organizations actually did.
Governance is a separate question from the survey’s findings. NIST describes its Generative AI Profile as a cross-sectoral profile and companion resource for the AI Risk Management Framework 1.0, intended for voluntary use to help organizations incorporate trustworthiness considerations into AI design, development, use, and evaluation. NIST published it on July 26, 2024; it is not part of the Linux Foundation survey.
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