DeepSeek: A New Blueprint for AI
DeepSeek is turning out to be a disruptor, not only due to the quality of its AI but also its cost-effectiveness and reinforcement learning capabilities.
While OpenAI, Google DeepMind, and Anthropic dominate headlines with mind-boggling infrastructure requirements, DeepSeek shows that performance does not need to come at the expense of exponential compute costs.
This article looks at what makes DeepSeek different. We’ll explain its approach to reinforcement learning and how it optimizes hardware utilization.
What is DeepSeek?
DeepSeek was founded in 2023 and is headquartered in Hangzhou, China. It aims to disrupt the cost-prohibitive approach that pervades AI development.
Whereas Silicon Valley companies have pushed AI forward, they’ve done so at a phenomenal cost, requiring enormous GPU clusters and outrageous computing power. DeepSeek noticed an opportunity to develop models that provide the same strength without the overwhelming overhead.
DeepSeek V1
The first step in its development was a general-purpose large language model. DeepSeek-LLM was designed to handle standard AI tasks—text generation, coding, summarization, and analysis.
Deepseek built two versions of this model. The compact version had 7 billion parameters and was optimized for efficiency. Deepseek built a more advanced version with 67 billion parameters to compete with other high-end models.
With the base model in place, the focus shifted to reducing computation costs. DeepSeek-MoE introduced Mixture-of-Experts (MoE) architecture, which selectively activates only the necessary parts of the model for each request. Instead of processing every query at full computational power, it activates only the relevant neural pathways. This reduces processing time and lowers operational costs.
General-purpose models struggle with math. They can predict text but often fail when solving complex numerical problems. DeepSeek-Math was explicitly trained for mathematical reasoning. It approaches problems step by step rather than predicting answers based on patterns. It was fine-tuned using reinforcement learning to improve accuracy over time.
DeepSeek V2 & V3
A common limitation in AI is context memory. Most models can only retain a few paragraphs of text before losing track of earlier details.
DeepSeek V2 and V3 increased the context window to 128,000 tokens, allowing them to process long-form documents, research papers, and contracts without losing coherence. Both models could maintain a more structured flow in extended interactions and improved multi-step reasoning for tasks that require deeper analysis.
DeepSeek R1
The final addition was DeepSeek R1, a model developed for structured problem-solving. R1 was designed to break down complex queries into logical steps. It applied reinforcement learning to improve reasoning and decision-making. R1 focused on applications in coding, structured analysis, and advanced problem-solving.
What is Reinforcement Learning?
Reinforcement learning (RL) is a form of machine learning focused on how an AI agent acts in an ever-changing environment.
Most AI models are trained similarly: give them data, adjust parameters, and redo. This method is effective but expensive. Each improvement requires more data and drives costs higher.
DeepSeek optimizes how its models learn using reinforcement learning (RL). Rather than providing a model with static datasets, RL allows it to interact with an environment and learn from feedback. The AI performs an action, analyzes the result, and adapts.
How RL Works in Training LLMs
LLMs generate answers by predicting the next most probable word from their training. However, accuracy in prediction does not always translate to utility.
RL introduces reward systems that teach the AI to distinguish what is likely from what is right and useful. In this procedure, the AI produces outputs, which the system subsequently analyzes.
Rewards are also given based on quality, such as accuracy, coherence, and logical flow. Thus, the model learns to emphasize outputs that are of higher quality as opposed to merely probable ones.
Why DeepSeek Uses RLHF
DeepSeek employs Reinforcement Learning from Human Feedback (RLHF) to improve its performance. As stated earlier, a model trained only on probabilities may produce text that looks correct but does not make sense.
To counter this, DeepSeek incorporates RLHF, using human judgment instead of depending solely on mathematical probabilities. Trainers rank the model’s outputs according to usefulness, coherence, and factual accuracy.
These rankings direct the reinforcement learning procedure, allowing the AI to generate improved responses that are statistically probable and qualitatively better.
The Role of Chain-of-Thought Reasoning
Other language models have trouble reasoning. When presented with multi-step problems, they often oversimplify or make up responses.
They don’t naturally “think” in steps.
Chain-of-thought (CoT) reasoning alleviates this problem by allowing the model to divide the problem into digestible chunks instead of leaping directly to a solution.
The model lays out the reasoning step by step, just like a human. This method enhances the precision of answers in mathematics, computer programming, logical reasoning, and others.
How DeepSeek Compares to ChatGPT
The competition between DeepSeek and ChatGPT highlights two different approaches to LLMs. Here’s how they compare.
Performance Insights
DeepSeek has demonstrated competitive performance in reasoning and problem-solving tasks, particularly in structured domains such as mathematics and coding. Its chain-of-thought (CoT) model, DeepSeek R1, was specifically designed to break down queries into logical steps, though its performance has varied across different tests.
Recent independent evaluations have shown that DeepSeek outperforms some of OpenAI’s models in structured reasoning tasks and lags in other areas, such as open-ended problem-solving. Unlike traditional benchmarks optimized for AI training, adversarial testing methods—designed to assess how models handle ambiguous or challenging queries—suggest that DeepSeek holds its ground against established models like ChatGPT-4o but remains slightly behind top-tier offerings from OpenAI and Anthropic.
Development Efficiency
One of DeepSeek’s standout achievements is its cost-effective model training. Compared to OpenAI’s substantial investment in ChatGPT’s latest iterations—estimated between $100 million and $1 billion—DeepSeek trained its R1 model in just two months for approximately $5.6 million. This cost efficiency stems from using Nvidia’s H800 chips and optimization techniques like Mixture-of-Experts (MoE), which selectively activates only the necessary parts of the model.
ChatGPT, developed with significant investment in infrastructure, offers a robust, general-purpose AI with multimodal capabilities, supporting text, image, and voice processing. While this enhances its versatility, it also increases the computational overhead.
Ethical and Regulatory Considerations
Both models operate within different regulatory and ethical frameworks. DeepSeek, developed in China, aligns with local laws, which means it enforces stricter content moderation on politically sensitive topics. This has led to instances where certain queries are met with responses indicating limited accessibility.
Developed in the West, ChatGPT adheres to OpenAI’s ethical guidelines, emphasizing user safety and broader information access. However, concerns about biases, content moderation policies, and potential external influences exist for both models. As AI becomes more deeply embedded in global business and governance, transparency and accountability in model training and response filtering remain critical discussion points.
What DeepSeek Means for the Future of AI
The industry has been fixated on scaling models at any cost, throwing billions of dollars into larger datasets, more GPUs, and endless training cycles.
DeepSeek has done the opposite. It has proven that artificial intelligence does not have to be a luxury reserved for companies with limitless budgets. Reinforcement learning has been at the core of this shift.
However, the more significant implication is this: DeepSeek has forced the AI industry to reconsider what progress looks like. Is the future of AI about models that consume more energy, require more servers, and become too expensive for anyone outside of big tech to use? Or is it about intelligence that works smarter, adapts faster, and delivers value without excessive overhead?
AI’s future will not be decided by who can build the biggest model. It will be decided by who can build the most effective one. DeepSeek has laid down a blueprint for what that looks like.
