The rapid rise of DeepSeek has raised questions about how they achieved such compatibility and capabilities with existing models.

Open-source AI models, like those released by Deepseek, are closing the gap with proprietary models, and Google has also released competitive open-source models.

DeepSeek's R1 is an open-source reasoning model that claims to achieve performance comparable to OpenAI's models at a significantly lower cost, causing a stir in the AI community.

The reasoning model of DeepSeek allows for deeper answers by thinking through the steps of a problem, which was not released with previous models like OpenAI's.

DeepSeek's rise in the open-source community is due to its strong reasoning model and user experience innovations that allow users to see the model's thought process, which had not been achieved before, and the 'DeepSeek moment' disrupted the market by providing a transparent and open-source approach that resonated with the community.

The success of open-source models like DeepSeek is attributed to their effective engineering and the ability of small teams to innovate quickly, integrating research and engineering seamlessly.

DeepSeek's R1 model is significantly cheaper than OpenAI's offerings, with a cost of $2 per million tokens compared to OpenAI's $60.

Reinforcement learning is used by both OpenAI and DeepSeek to shape the behavior of their language models based on feedback and reward signals, enhancing their usefulness and alignment.

Fine-tuning models like DeepSeek for specific tasks has proven effective, allowing customers to customize their AI solutions to better meet their business needs.