LFAIData2024塑造生成式人工智能的未来开源在GenAI技术演进与实施中的作用研究报告英文版45页_8mb

Shaping the Future of Generative AI: The Impact of Open Source Innovation

This report by The Linux Foundation highlights the growing role of open source in the development and adoption of generative AI (GenAI) technologies. Key findings include:

• 94% of organizations use GenAI, with 73% planning to increase open source tool usage in the next two years.
• 84% of organizations have moderate, high, or very high GenAI adoption, and 92% view GenAI as important, with 51% considering it extremely important.
• 41% of GenAI infrastructure code is open source, and 71% of organizations report its positive impact on adoption due to transparency and cost efficiency.
• 78% of organizations prioritize open source tools hosted by neutral parties (e.g., Linux Foundation, CNCF) for compliance, trust, and collaboration.

Primary Use Cases:
GenAI is most commonly used for process automation/optimization (25%), content generation (17%), code generation (14%), customer service (11%), and research (6%). High adopters integrate GenAI into internal workflows, custom model training, and deployment, with 65% of organizations using cloud-based infrastructure for model development.

Open Source Frameworks:
Open source tools like PyTorch (63%) and TensorFlow (50%) dominate model building and training, while LangChain (44%) and LlamaIndex (30%) lead in inferencing. Open source enables cost-effective, transparent solutions, allowing organizations to reduce reliance on proprietary systems and tailor AI to specific needs.

Cloud Native Integration:
Cloud native strategies, including Kubernetes, are critical for scalable GenAI deployment. 50% of organizations use Kubernetes for inferencing workloads, leveraging its automation and flexibility. Hybrid cloud models (29%) balance on-premises control with cloud scalability, especially for compliance-sensitive industries.

Challenges:
Organizations prioritize accuracy (60%), security (54%), and cost efficiency (49%) when adopting GenAI. Concerns include data privacy, hallucination risks, and operational complexity. While 62% see revenue gains from GenAI investments, outcomes vary, emphasizing the need for strategic alignment with business goals.

Employment Impact:
67% of organizations report no impact on employment, but high adopters (26%) may hire more staff for specialized roles, while some (17%) reduce headcount via automation.

Future Outlook:
83% of respondents agree AI must become more open for trust and innovation. Open source is expected to shape the future, with 48% predicting it as the industry standard. Trends include decentralized processing, smaller specialized models, and increased focus on ethical governance and interoperability.

Methodology:
The survey of 316 professionals across industries (August–September 2024) highlights adoption rates, infrastructure practices, and challenges. Data.World provides access to raw datasets, with emphasis on transparency and neutral governance.

The report underscores open source as a cornerstone for GenAI’s growth, enabling collaboration, innovation, and regulatory compliance. By fostering shared standards and community-driven development, open source ensures accountability and sustainability in AI advancements.