What the DeepSeek disruption is all about

Recently, Chinese Artificial Intelligence (AI) firm DeepSeek introduced its latest large language model, R1, sending shockwaves through the tech industry.

- Siddharth Pai

R1 wasn't just on par with the best AI models available; it was built at a fraction of the usual cost and released for free. The financial world reacted instantly, with the United States (US) stock market losing a staggering $1 trillion the day R1 was unveiled.

The implications of DeepSeek's move extended far beyond these financial tremors. By openly sharing the details of how R1 and its predecessor, V3, were developed and making these models freely accessible, DeepSeek shattered a long-held industry belief that reasoning-based AI models were extraordinarily difficult and expensive to create. This revelation had an immediate impact, triggering a rapid response from major AI competitors.

The reaction from competitors and the rapid shifts in the industry begs the question: What exactly did DeepSeek do to cause such a massive upheaval, and is the hype surrounding R1 justified? Understanding the impact requires a closer look at how large language models are built.

Training these models involves two primary phases: Pre-training and post-training. In pre-training, the model learns to generate text by analyzing vast amounts of publicly available documents (basically the internet's entire contents) and processing them repeatedly. This results in a base model that possesses extensive knowledge but lacks task-specific refinements.

The process is computationally intensive and represents the largest cost in AI development. The model then undergoes post-training to refine its capabilities. One key component of this is supervised fine-tuning (SFT), where human trainers curate question-answer pairs and teach the model to respond accurately.