Success of DeepSeek and potential benefits of free access to AI for global-scale use
Bibliographic record
Abstract
The introduction of DeepSeek R1, an AI language model developed by the Chinese AI lab DeepSeek, has made a significant impact in the tech world[1]. Within a week of its release, the app surged to the top of download charts, triggered a massive $1 trillion (£800 billion) sell-off in tech stocks, and prompted intense reactions from Silicon Valley. As artificial intelligence (AI) continues to evolve rapidly, it has become a cornerstone of global technological progress, with nations vying to push the boundaries of what AI can achieve. While companies like OpenAI and Nvidia in the United States have led AI research and deployment, the rise of DeepSeek represents a noteworthy shift in the landscape. DeepSeek’s innovative use of reinforcement learning (RL) and model distillation has significantly enhanced the reasoning capabilities of large language models (LLMs), while also advancing more efficient algorithms that reduce computing resource and energy consumption. This paper explores the factors behind DeepSeek’s success and its broader impact on making AI more accessible and efficient, especially for the developing world. By contributing to AI’s global accessibility, China’s advancements hold great potential to positively transform diverse sectors, from agriculture to energy and healthcare, supporting the goal of peaceful coexistence and improving life around the globe. Key words: AI, DeepSeek, reinforcement learning, model distillation, free access, global-scale utilization DOI: 10.25165/j.ijabe.20251801.9733 Citation: Okaiyeto S A, Bai J W, Wang J, Mujumdar Arun S, Xiao H W. Success of DeepSeek and potential benefits of free access to AI for global-scale use. Int J Agric & Biol Eng, 2025; 18(1): 304–306.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".