Rethinking Open Source in the Age of Foundational AI Models
Highlights
- Last month, seven African researchers shrank a multilingual language model by 75 percent—an outcome made possible not by brute compute but by strategic compression and the enabling role of open source architecture (View Highlight)
- InkubaLM, Africa’s first multilingual Small Language Model (SLM) for five African languages. Built from scratch, according to its model card on Hugging Face, InkubaLM follows a lightweight adaptation of LLaMA-7B’s open source architectural design, optimized for low-bandwidth environments. (View Highlight)
- The top three teams, all from Africa, employed a combination of open source techniques—distillation, quantization, vocabulary pruning, adapter heads, and shared embeddings—to reduce the model’s parameters to just 40 million while maintaining translation quality for Swahili and Hausa. (View Highlight)
- The classic open source definition, originating from software, centers on the freedom to use, study, modify, and share code. But AI introduces new components that complicate this framework: model weights (the trained parameters), training data (often massive and sensitive), and computational requirements that can make ‘modification’ practically impossible for most users. (View Highlight)
- • Some advocates argue that truly open source AI must include all three components: source code, model weights, and training data (or transparent documentation of training methods).
• Others contend that releasing weights and code is sufficient, even if the training data remains proprietary.
• Still others question whether the open source framework applies at all—after all, you can’t easily ‘read’ billions of floating-point parameters the way you can read a Python function. (View Highlight)
- The Open Source Initiative has now drawn a definitive line with their official Open Source AI Definition: AI systems must include detailed data information, complete source code, and model parameters to qualify as open source. If you can’t inspect, reproduce, and modify all three components, it isn’t open source. (View Highlight)
- Camp 1: Closed = Safer
This group comprises most of the major labs (OpenAI, Google DeepMind, Anthropic) and a number of national security experts. Their view is that advanced AI is a dual-use technology, similar to nuclear research or bioengineering. They argue that open-sourcing powerful models too early could let anyone, even those without specialized knowledge, cause significant harm. (View Highlight)
- In response, these organizations typically favor managed access, offering their models via APIs, gated licenses, or safety filters that restrict use to approved contexts. This helps them monitor deployments, enforce safeguards, and intervene when necessary. It also, not incidentally, aligns with their commercial interests. (View Highlight)
- Camp 2: Open = Safer
On the other side are open source communities like LAION, AI startups like Mistral, and major researchers like Meta’s Yann LeCun. They argue that openness breeds trust, improves safety, and decentralizes power. (View Highlight)
- These camps and their relative perspectives reflect deeper values about how AI should be governed and by whom.
• Trust and Transparency: If people don’t understand how AI systems are trained or what data went into them, it’s hard to build trust. Open models offer a pathway to transparency and accountability.
• Governance: Closed models consolidate control in a handful of companies. Open models invite collaborative oversight, which may be essential for democratic governance of powerful technologies. They also support the development of AI as digital public infrastructure (DPI): Tools that are inspectable, auditable, and governed in the public interest, much like open standards for identity or payments.
• Innovation: Openness accelerates progress. Instead of duplicating work behind closed doors, open source fosters a shared foundation for experimentation and invention.
• Security: There’s no easy answer here. Open models could be misused, but they also enable more people to build defenses, audit vulnerabilities, and reduce reliance on opaque systems.
• Equity: Openness lowers the barrier to entry and levels the playing field. It also enables countries and communities to build and adapt AI systems without relying on closed, foreign-controlled platforms. This is an essential step toward achieving digital sovereignty and an inclusive digital public infrastructure. But openness at the model level doesn’t guarantee equitable access if other layers remain concentrated. Even with open weights, organizations still need substantial computing resources, specialized expertise, and platform infrastructure, creating new chokepoints where power can reconcentrate. True equity requires addressing these deeper dependencies, not just open-sourcing the models themselves. (View Highlight)
- Meta’s widely celebrated LLaMA 2 made code and model weights available but withheld critical training data and included restrictive licensing terms, limiting practical reuse. Similarly, Mistral 7B took a more permissive stance but still fell short of full transparency, offering limited insight into its dataset and training methodologies. (View Highlight)
- Even Stable Diffusion, frequently praised as a landmark example of openness, was trained on datasets compiled from scraped online images without explicit consent, raising substantial privacy and ethical concerns. This opaque data sourcing approach undermines the foundational principles of transparency and accountability that open source advocates champion. (View Highlight)
- When the exact boundaries of openness remain blurred, it becomes challenging to establish clear governance standards, assess security implications accurately, and ensure responsible use. (View Highlight)
- Foundation LLMs are powerful. They provide organizations with quick access to advanced language capabilities, enabling teams to accelerate the development of tools for tasks such as summarization, classification, and information retrieval. For many, these large pre-trained models offer a fast on-ramp to experimenting with generative AI. (View Highlight)
- Smaller open source models, often referred to as Small Language Models, or SLMs, offer a different kind of opportunity. They allow organizations to move beyond consumption and begin shaping AI to reflect their own needs, fine-tuning for specific tasks, local languages, cultural contexts, or sector-specific domains. Open source models unlock a layer of ownership that closed systems rarely allow. (View Highlight)
- Open source LLMs are a form of digital public infrastructure. Like open standards in payments or broadband, they provide a shared, inspectable foundation that others can build on without surrendering control. (View Highlight)