The calls for OpenAI to open source GPT-4 continue to circulate on social media platforms, but this seemingly straightforward demand misses crucial practical considerations. As someone who works closely with AI models and understands their implementation constraints, I believe this debate needs a more nuanced perspective that accounts for real-world technical and business factors.
Would Anyone Actually Use an Open Source GPT-4?
Let’s start with the most basic question: If OpenAI were to open source GPT-4 tomorrow, would developers and researchers actually use it? There are several compelling reasons why the answer might be “no” for many potential users.
The Prohibitive Cost Factor
First and foremost, running GPT-4 would be prohibitively expensive for most organizations and individual researchers. The computational requirements for a model of its size are substantial, requiring specialized hardware setups that few have access to outside major tech companies and research institutions.
Even with cloud computing options, the costs would quickly become unsustainable for scenarios beyond limited, specialized use cases. While open source technically means free to access, it certainly doesn’t mean free to run – a distinction often overlooked in these discussions.
Better Alternatives for Specific Tasks
The AI landscape has continued to advance rapidly since GPT-4’s initial release. For many specific tasks and applications, newer models offer superior performance or more efficient implementations. When it comes to distillation creating smaller, more efficient models based on larger ones, there are now purpose-built models that serve as better starting points.
Models like Qwen3 demonstrate how open-source alternatives are advancing with specialized capabilities that might make them more practical starting points for many applications compared to a hypothetical open-source GPT-4. Open source models are closing the gap rapidly. Vicuna-13B, for example, achieves over 90% of the quality of ChatGPT which is based on GPT-3.5 and early versions of Google Gemini. This shows that GPT-4 might not even be the best option for many tasks anymore.
Technical Barriers to Implementation
Incompatible Architecture
Even if OpenAI decided to open source GPT-4, there’s a significant technical barrier: its architecture and implementation are likely incompatible with the inference stacks commonly used for open source models today. The foundation models ecosystem has developed its own set of tools, frameworks, and optimization techniques that differ from what OpenAI uses internally.
This incompatibility would create substantial friction. Developers would need to either:
- Adapt their existing workflows and infrastructure to accommodate GPT-4’s unique implementation details
- Wait for OpenAI to convert GPT-4 to formats compatible with open source inference stacks
Either option represents a significant investment of time and resources, potentially undermining the benefits of open sourcing in the first place. Making their architecture and implementation compatible with open source frameworks would be a major undertaking for OpenAI’s team.
Mixture of Experts and Proprietary Techniques
GPT-4 reportedly implements a mixture of experts MoE architecture – a technique that wasn’t widely used in commercial large language models when it was first developed. This approach splits computation across specialized sub-networks, potentially giving OpenAI a competitive advantage in both performance and efficiency.
There may be additional proprietary techniques and optimizations in GPT-4 that competitors haven’t yet discovered or implemented. By open sourcing the model, OpenAI would effectively be giving away these innovations to competitors, potentially eroding their market position and technical lead. This is the “secret sauce” that labs are hesitant to reveal.
The Business Perspective: What’s in it for OpenAI?
From a business standpoint, the decision to open source GPT-4 comes down to a cost-benefit analysis. What would OpenAI gain, and what would they lose?
The Limited Upside: PR Value
The primary benefit to OpenAI would be positive publicity and goodwill within the AI community. Open-sourcing a flagship model could bolster their reputation for transparency and commitment to their original mission of ensuring AI benefits humanity.
However, this PR value has diminishing returns. OpenAI has already established its position in the market, and while goodwill in the developer community is valuable, it’s unclear whether it would translate to meaningful business advantages that outweigh the costs.
The Substantial Downside: Competitive Disadvantage
On the cost side, open-sourcing GPT-4 poses several significant drawbacks:
- Engineering resources would need to be allocated to prepare the model for public release in a usable format
- Proprietary techniques would be revealed to competitors
- The ability to monetize access to GPT-4 would be compromised
For a company that has shifted from its non-profit origins to a capped-profit model with significant commercial interests, these costs likely outweigh the PR benefits of open sourcing.
The Value of Archival Access
One compelling argument for open-sourcing GPT-4 is for archival purposes. Having access to historically significant models allows researchers to study their development, benchmark progress, and understand the evolution of AI capabilities over time.
This scientific and historical value is real, but it may not be sufficient justification for OpenAI to open source GPT-4 now, particularly when they’re still actively using and monetizing the technology. Perhaps a compromise could be a commitment to open source the model after a certain period, once its commercial value has declined.
OpenAI’s Future Open Source Plans
It’s worth noting that OpenAI hasn’t abandoned open source entirely. They’ve announced plans to release a new open-source language model in early summer 2025, focused on reasoning capabilities and designed to run on consumer hardware.
This suggests a more strategic approach to open sourcing: creating purpose-built models for open release, rather than giving away their most advanced commercial technology. This approach allows them to contribute to the open source community while protecting their competitive advantages.
Similarly, Meta’s approach with Llama models demonstrates how companies can strategically release powerful open-source AI while still maintaining commercial advantages with their most advanced systems.
What Would Make More Sense?
Rather than demanding that OpenAI open source GPT-4 specifically, a more productive approach might be to:
- Encourage OpenAI to release technical papers detailing GPT-4’s architecture and training methodology
- Support the development of purpose-built open source models that address specific needs
- Focus advocacy on ensuring that future open source releases from OpenAI are genuinely useful and accessible
These approaches would achieve many of the benefits of open sourcing without imposing unrealistic burdens or commercial disadvantages on OpenAI.
The Pragmatic View
While the idealistic call for open sourcing GPT-4 comes from a good place – wanting more democratized access to advanced AI – the practical realities suggest it’s neither feasible nor particularly beneficial in its most straightforward implementation.
The technical barriers to making GPT-4 usable in an open source context are substantial, and the commercial disincentives for OpenAI are clear. Meanwhile, the open source AI community continues to make remarkable progress with models like Llama, Qwen, and others that are built with open source compatibility in mind from the ground up.
Instead of fixating on opening up yesterday’s proprietary models, a more productive focus might be on building better open alternatives and creating pressure for future commercial models to be more transparent and accessible. This pragmatic approach acknowledges both the ideals of open source and the realities of how AI development happens in today’s mixed ecosystem of commercial and open projects.
The debate shouldn’t be about whether GPT-4 specifically should be open sourced, but rather how we can create an AI ecosystem where both commercial and open source models thrive, with each playing appropriate roles based on their strengths and limitations.
Considering the Counterarguments
Some argue that open sourcing GPT-4 would spur innovation and allow researchers to build upon its capabilities. While this is true in principle, the practical hurdles of cost and compatibility significantly limit who could actually benefit from such a release. The innovation would likely be concentrated among well-funded labs, not the broader community. Furthermore, the open source community is already innovating rapidly with models designed for open deployment, arguably leading to broader access and experimentation than a difficult-to-run GPT-4 would allow.
Another argument is that open sourcing would help with AI safety and alignment research. By allowing external researchers to examine the model’s inner workings, potential risks and biases could be identified and mitigated. While transparency is valuable for safety, it’s not the only path. OpenAI can and does release research papers and work with external safety researchers without open sourcing the full model. Additionally, open sourcing a powerful model also introduces new safety risks if the model is misused.
Finally, some believe open sourcing is a moral imperative for OpenAI, given their original non-profit mission. While their mission has evolved, they are still a business with significant investments and a need to compete. Expecting them to act purely out of principle, sacrificing their competitive edge and resources, is unrealistic in the current market.
The Future of Open Source in AI
The future of open source in AI looks promising, but it won’t necessarily involve open sourcing every past proprietary model. The focus is shifting towards developing new open source models that are competitive with proprietary ones, often with specific strengths like efficiency, reasoning, or specialized capabilities. Open source models are particularly valuable for privacy-sensitive applications, where businesses prefer to run models on their own infrastructure rather than sending data to a third-party API. This is one of the key reasons I value open source models myself.
The ideal scenario is a healthy ecosystem where companies like OpenAI push the frontier with proprietary research, and then strategically release open source models or contribute to open source projects in ways that benefit the broader community without undermining their core business. This is a more sustainable path than simply demanding the release of their most valuable assets.
The conversation should move towards how we can best support the development and deployment of truly useful open source AI that can be run by a wider range of users and organizations. That’s where the real potential for democratizing AI lies, not in the impractical open sourcing of models like GPT-4.
Ultimately, while the desire for an open-sourced GPT-4 is understandable, the practical realities of cost, technical compatibility, competitive dynamics, and the existence of rapidly improving open alternatives make it an unlikely and perhaps even unnecessary outcome. The focus should be on building the future of open source AI, not rehashing debates about yesterday’s proprietary models.
