What is the problem?

The digital age is accelerating at breakneck speed—from companies like Nvidia creating high performance AI chips to Anthropic seeking questionable funding from Gulf State investors, every major player is scrambling to claim a stake in the AI arms race. But, as Artificial Intelligence becomes more powerful, a battle over transparency is quietly unfolding.

In the early days of AI, development was built on openness with researchers sharing and publishing their code for everyone to see. However, AI soon fell to the pressures of profit and competition, and now companies like OpenAI are starting to close the doors of their most advanced models, like GPT-4.

This shift has ignited a philosophical and strategic divide among the industry’s biggest players, with some embracing radical transparency and others retreating into secrecy. This difference in ideology raises the question: who gets to decide how open AI should be?

What is “Open” and “Close” AI?

As the name suggests, the terms “closed” and “open” AI refers to public accessibility of LLM models. Many companies, like Anthropic, have built their LLM models on the foundation of openness in order to avoid black box scenarios and support AI guardrails and regulations. Openness matters a lot, since without transparency we as a society have no knowledge of how these LLMs are being created. This can lead to many problems often referred collectively as a “black box problem” — we don’t know what the AI is doing, how they're trained, or what risks they pose. All we know are the inputs, outputs, and nothing in between.

Another important aspect of openness is the release of model weights alongside the publication of the AI. These weights refer to the actual training parameters that the LLM uses that are made publicly available. By downloading these weights, developers can use them to do tasks like text generation and sentiment analysis without having to train the model from scratch. In a sense, the weights help replicate some abilities that the AI can offer at a fraction of the cost. Some examples of weights that are available for download are Llama 3 and Mistral 7B from Meta and Mistral AI respectively with some usage restrictions.

 What is the National Security Squeeze?

However, for the importance of national security, our government feels as though openness could be misused at scale. AI is very fine-tuned to create bioterrorism guides, automated phishing, fraud, or even deepfake generation. Concerns have increased after studies conducted from RAND and OpenAI have shown that with little technical effort, based models could be heavily misused (RAND 2025).

The extent of the squeeze does not stop there: Biden’s Executive Order on AI as well as the Department of Commerce export controls have incentivized keeping models closed in order to reduce scrutiny and increase fear of foreign misuse. From having to report safety testing to navigating through guardrails established around model development, many companies are finding it easier and more cost efficient to just close up their AI models (Burling LLP 2025).

In response to this squeeze, both Meta and OpenAI have opted for different solutions. OpenAI has decided to comply and become more closed—GPT 4 is proof of this as its architecture, training data, and open weights are completely undisclosed (OpenAI 2025). However, Meta has taken a different approach by releasing LLM models like Llama 2 and Llama 3 with minimal restrictions (Meta 2025). This stark difference in philosophy between two AI powerhouses could spell a fractured future for AI development and a policy divide within the digital age.

Can Transparency Be Saved?

As closed AI models start to become the norm, shielded by APIs and restricted access, it may feel like transparency of LLMs may be slipping away. All is not lost however—all that is left to do is to evolve what transparency will mean for LLMs from now on. No longer are open weights and codebases a well-defined and transparent AI model. Instead, rigorous documentation, independent audits, and safety disclosures will stand at the top of AI ethics. The question is no longer if AI will be closed, but whether we can keep its power accountable even behind closed doors.