DeepSeek has emerged as the latest sensation in the AI world, not only matching ChatGPT’s capabilities in multiple benchmarks but also taking the bold step of “going open source.” This decision has created quite a stir in the international tech community: While OpenAI’s CEO Sam Altman initially praised it as “impressive,” OpenAI later accused DeepSeek of unauthorized “knowledge distillation.” Meanwhile, the tech community has embraced DeepSeek’s open approach, playfully dubbing OpenAI as “CloseAI.” But this raises an important question: Does DeepSeek’s approach truly qualify as “open source”?

Think of today’s AI landscape as the software equivalent of the fast-food versus open-kitchen restaurant debate. OpenAI’s ChatGPT is like a high-end restaurant where customers can only enjoy the final product, while DeepSeek is more like a chef who shares their recipes but keeps certain sourcing details and techniques private. The comparison might sound simplistic, but it highlights a crucial debate in the AI community.

What makes this particularly interesting is DeepSeek’s claim that they developed their competitive AI model for just $5.58 million – a fraction of what tech giants typically spend on AI development. This cost-effectiveness, combined with their commitment to sharing, has captured the industry’s attention.

I. Technical Perspective: The Value of “Model Weights”

Understanding model weights is crucial to this discussion. In layman’s terms, model weights are like the precise settings of a complex machine – they determine how the AI processes and responds to information. DeepSeek’s decision to share these weights has several significant implications:

1. Performance Optimization

DeepSeek has shared some groundbreaking technologies:

  • FP8 Mixed Precision Training: Think of this as smart compression that maintains quality while reducing resource usage
  • MoE Architecture: Imagine a team of specialized experts working together efficiently, each handling their area of expertise

2. Model Modification

The open weights allow developers to build upon and improve the existing model – similar to how open-source software enables developers to create new applications based on existing frameworks.

II. Defining “True” Open Source

1. Current Industry Consensus

In the AI world, “open source” typically means sharing model weights and technical documentation. This is similar to how Linux distributions share their core components while allowing different implementations.

2. Points of Contention

DeepSeek has shared:

  • ✓ Model weights
  • ✓ Technical documentation
  • ✗ Complete training data
  • ✗ Full codebase

While some argue this isn’t fully open source, it’s worth noting that other respected open-source AI models like LLaMA and Mistral follow similar practices.

III. Real-World Impact

1. Lower Barriers to Entry

The availability of these open models has democratized AI development, allowing smaller companies and researchers to build upon existing work rather than starting from scratch.

2. Enhanced Transparency

Open source enables verification of model claims and capabilities, with platforms like Hugging Face serving as independent validation sources.

Conclusion: Beyond the Binary Debate

Rather than getting caught up in whether DeepSeek is “open enough,” we should consider the practical impact of their approach. They’ve demonstrated that competitive AI development doesn’t require massive budgets, and their sharing strategy has already enabled numerous innovations in the field.

The value of DeepSeek’s contribution lies not in whether it meets a strict definition of open source, but in how it’s advancing the democratization of AI technology. As the industry evolves, perhaps we need to move beyond binary open/closed distinctions and focus on how different sharing models can best serve technological progress.

This balance between openness and innovation might just be the key to ensuring AI development benefits the broader tech community while maintaining the incentives for continued innovation.