OpenAI GPT OSS
Why is OpenAI Open-Sourcing Models Now?
OpenAI’s decision to open-source its gpt-oss models marks a pivotal shift in its strategy, driven by a need to adapt to a rapidly changing AI enterprise market.
Several factors explain this move and its implications…
First, OpenAI is responding to a significant decline in its enterprise market share, which has dropped from 50% in 2024 to 25% in 2025, as shown in the chart below.
This decline stems from intense competition from closed-source providers like Anthropic and Google, as well as the rising popularity of open-source alternatives like LLaMA, which offer cost-effectiveness and flexibility.
Second, OpenAI is capitalising on the growing focus on multi-orchestration for AI agents and agentic pipelines.
Multi-orchestration involves coordinating multiple AI models to tackle complex tasks, such as automating workflows or decision-making.
By open-sourcing gpt-oss, OpenAI ensures its models integrate seamlessly into orchestration frameworks like LangChain or Haystack, strengthening its presence in enterprise AI ecosystems.
Third, hosting gpt-oss models on platforms like Hugging Face, Databricks, Azure, and AWS expands OpenAI’s reach.
These platforms are widely used by enterprises, and offering gpt-oss models here allows OpenAI to maintain a foothold in environments where companies already operate.
This move also addresses data governance and sovereignty concerns, as enterprises in regulated industries like finance and healthcare can run gpt-oss models locally or in private clouds to comply with strict data laws.
Moreover, enterprises are often hesitant to switch AI vendors due to vendor lock-in.
OpenAI’s gpt-oss initiative mitigates this by offering open-source options within existing vendor ecosystems, allowing companies to upgrade their AI capabilities without overhauling their infrastructure.
This hybrid approach bridges the gap between closed-source models, which dominate due to ease of use and vendor support, and open-source models, which offer customisation and cost savings.
The chart below highlights the continued dominance of closed-source models in enterprise AI, with open-source models gaining ground.
OpenAI’s gpt-oss models position it to capture both markets by combining its trusted brand with open-source flexibility.
Finally, developers are a key driver of this strategy.
Tools like Ollama, Jan, and LLM Studio enable developers to run models offline or locally, facilitating faster prototyping and enhanced privacy.
By ensuring gpt-oss compatibility with these frameworks, OpenAI appeals to the developer community, which values flexibility and no-code solutions.
However, this move is not without risks.
Some argue that open-sourcing could dilute OpenAI’s premium brand or enable competitors to build on its technology.
Nevertheless, the strategic benefits — regaining market share, enhancing ecosystem presence, and fostering developer trust — likely outweigh these concerns.
In conclusion, OpenAI’s gpt-oss initiative is a calculated move to adapt to a competitive and evolving AI landscape.
By bridging the gap between closed- and open-source models, OpenAI positions itself as a versatile player in enterprise AI, appealing to both corporations and developers while reinforcing its leadership in the industry.
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. Language Models, AI Agents, Agentic Apps, Dev Frameworks & Data-Driven Tools shaping tomorrow.