Chinese AI lab DeepSeek has released groundbreaking AI models that rival and sometimes surpass Silicon Valley’s leading offerings, showcasing a novel approach to artificial intelligence problem-solving. The company’s innovative methodology employs test-time or inference-time compute, a technique that breaks down complex queries into smaller, manageable tasks, with each subtask becoming a new prompt for the model to process.
Business Insider recently conducted an in-depth test of DeepSeek’s capabilities using its DeepThink mode, a transparent feature that reveals every step of the AI’s reasoning process to users. The test was designed with input from Charlie Snell, an AI researcher at UC Berkeley, who specifically selected challenging mathematical problems from the American Invitational Mathematics Examination (AIME), a prestigious competition for high school mathematics students.
The demonstration involved a complex mathematical puzzle: finding a sequence of operations (+, -, /, *) that could be applied to the numbers 7, 3, 11, and 5 to reach 24, using each number exactly once. DeepSeek’s performance was remarkable, displaying human-like problem-solving characteristics including self-correction, strategic thinking, and persistence.
The AI model began by acknowledging the challenge and systematically worked through multiple approaches over approximately 16 pages of detailed reasoning. What distinguished DeepSeek’s performance was its ability to recognize mistakes, backtrack when necessary, and explore alternative solution paths. The model demonstrated metacognitive awareness, at one point noting, “Almost got close there with 33 / 7 * 5 ≈ 23.57, but not quite 24. Maybe I need to try a different approach.”
Particularly impressive was the AI’s self-awareness about its own process. The model caught itself repeating solutions and adjusted its strategy accordingly. After several minutes of exploration, DeepSeek successfully identified the correct answer. Snell praised the model’s transparent reasoning, stating he could follow and understand the chain of thought, noting that “You can see it try different ideas and backtrack.”
The researcher emphasized that DeepSeek performs exceptionally well on complex mathematical problems requiring extended reasoning and staged thinking, positioning it as a significant competitor in the global AI landscape.
Key Quotes
I put questions from that into the DeepSeek model. I read through the chain of thought. It was understandable.
Charlie Snell, an AI researcher at UC Berkeley, commented on DeepSeek’s performance with complex mathematical problems from the American Invitational Mathematics Examination. His observation highlights the model’s transparency and interpretability, crucial factors for AI trustworthiness.
Alright, so I’ve got this problem here: I need to use the numbers 7, 3, 11, and 5, and combine them with the operations of addition, subtraction, multiplication, and division, using each number exactly once, to get to 24. At first glance, this seems a bit tricky, but I think with some systematic thinking, I can figure it out.
This quote from DeepSeek’s own reasoning process demonstrates the model’s human-like approach to problem-solving, showing self-awareness and strategic planning before diving into calculations.
This is getting really time-consuming. Maybe I need to consider a different strategy. Instead of combining two numbers at a time, perhaps I should look for a way to group them differently or use operations in a nested manner.
Snell highlighted this particular moment as noteworthy, as it shows the AI model demonstrating metacognitive awareness—recognizing when an approach isn’t working efficiently and pivoting to a new strategy, a sophisticated reasoning capability.
Almost got close there with 33 / 7 * 5 ≈ 23.57, but not quite 24. Maybe I need to try a different approach.
This self-correction by DeepSeek illustrates the model’s ability to evaluate its own progress, recognize near-misses, and maintain persistence in seeking the correct solution rather than settling for approximate answers.
Our Take
DeepSeek’s demonstration reveals a crucial evolution in AI development: the shift from brute-force scaling to smarter reasoning architectures. While Western AI labs have focused on building ever-larger models requiring massive computational resources, DeepSeek’s test-time compute approach suggests that strategic problem decomposition may be equally or more effective. The transparency of the DeepThink mode is particularly significant—it addresses one of AI’s most pressing challenges: the “black box” problem. By showing its reasoning process, DeepSeek builds trust and enables users to verify correctness beyond just the final answer. This could prove essential for high-stakes applications in medicine, law, and engineering. The geopolitical implications are equally important: China’s AI capabilities are clearly advancing rapidly, potentially reshaping the global technology landscape and intensifying competition that could accelerate innovation worldwide.
Why This Matters
DeepSeek’s emergence represents a significant shift in the global AI competitive landscape, demonstrating that Chinese AI labs can produce models matching or exceeding Western counterparts. The test-time compute approach showcases an alternative methodology to traditional AI training, potentially offering more efficient problem-solving capabilities without requiring massive computational resources during the training phase.
This development has profound implications for AI accessibility and democratization. If smaller labs can achieve comparable results to tech giants like OpenAI and Google, it could accelerate innovation and reduce the concentration of AI power among a few well-funded companies. The transparent reasoning process displayed by DeepThink mode also addresses growing concerns about AI explainability and trustworthiness, allowing users to understand how the model reaches its conclusions.
For businesses and researchers, DeepSeek’s mathematical reasoning capabilities suggest practical applications in scientific research, engineering, financial modeling, and educational technology. The model’s ability to self-correct and explore multiple solution paths mirrors human problem-solving, potentially making it more reliable for complex analytical tasks where showing work and reasoning is as important as reaching the correct answer.
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Source: https://www.businessinsider.com/deepseek-demo-chinese-ai-model-math-reasoning-2025-1