Deepseek’s tectonic entrance into the digital world was by no means accidental but an upshot of more than two decades of the Chinese Communist Party’s nurturing top talent. It all began with investing in overseas scholarships, which was an acknowledgement of her intellectual backwardness. However, through the years of domestic and foreign capacity building, China is now a technological global power that is challenging the Western World or surpassing it in many fields of STEM: Science, Maths and Tech.
Today, China is acknowledged worldwide to have pulled more than 750 millions of her people out of poverty, a gratitude to feats in science and technology. No nation on earth comes closer to this miraculous achievement. This is a recap of China’s determination to pull out all of her people from the shackles of poverty through mastering the mysteries of science and technology.
Deepseek in a nutshell.
Perhaps the biography of the Deepseek founder, Liang Wenfeng, demonstrates best the success of the Chinese development model in overcoming the challenges of mastering science and technology. Liang Wenfeng born in 1985 (now 40 years old) is the founder of DeepSeek, a Chinese artificial intelligence (AI) company. He is also the CEO of DeepSeek and High-Flyer, the company that owns DeepSeek.
Liang is an engineer who graduated from Zhejiang University with degrees in computer science and electronic information engineering. In the late 2010s, he co-founded a hedge fund that used AI models to make money. In 2023, he invested in AI chips and assembled a team to create China’s answer to the US OpenAI. In January 2025, DeepSeek released its first AI large language model (LLM) and a chatbot.
DeepSeek’s model efficiency improvements reduce the costs of training and operating language models. DeepSeek’s emergence has been a global sensation and has upended the global tech landscape. Liang has said that China’s tech industry needs to focus on innovation and not just making money.
Since Deepseek released its next-generation large language model (LLM) in January 2025, tremors have rippled through the US tech strata. In 17 days since Deepseek’s launch, NVIDIA’s stock price plummeted by 23%, erasing over $830 bill in market value. A swift backlash erupted across the US industries, with Deepseek facing cyber attacks of 120 million per request per second. While Deepseek has become a scapegoat for those burned by the US losses, it is merely the trigger.
Chinese strategist Prof. Wang Xiangsui points out that the real cause of the financial turmoil lies in two dangerous delusions that have long propped up America’s AI strategy. Delusions at US media to sustain the illusion of tech industry prosperity have been conveniently ignored or actively reinforced.
While the Nemo (Neural Modules)1 framework boosted inference speeds by 30% through parameter sharing these breakthroughs never became central to Wall Street’s narratives. After all, acknowledging the fragility of the computing power myth would destabilize NVIDIA’s 2.8 trillion market valuation. The delusion shattered when DeepSeek revealed its model was trained at 3% of Open AI’s cost primarily using Huawei’s Ascend 920 B GPUs through the 910 delivers 80% of the A100’s raw compute. Deepseek’s wrench of the mixture of experts (MoE), architecture and dynamic spacing training techniques doubled its per unit efficiency. This exposed a harsh truth: algorithmic innovations can bridge hardware gaps rendering NVIDIA’s presumed hegemony replaceable.
Had the US tech investors understood this earlier, Deepseek might have been viewed as a competitor rather than a threat but Wall Street’s years of compute worship exposed its soft underbelly, and was caught napping. A single, DeepSeek’s vicious counterpunch collapsed the entire Wall Street false narrative. Delusion 2 was the performance benchmarks masked by an application desert. The US’s AI sector faces another paradox: soaring performance matrices have failed to translate into the real world productivity gains!
Deepseek showed that an AI model could be trained for a fraction of cost of other models, heftily slashing the demand of expensive data centers and advanced microchips. Unless there is a technological paradigm shift, soaring demand for reasoning models will consume a plethora of electricity, microchips and data centers for the foreseeable future. The technology is shifting away from conventional large language models towards reasoning models and AI agents. Large language models have also been chided for enticing cybersecurity vulnerabilities. The multiplier of user costs in reasoning models are caused by insider chain of thoughts that users don’t even see. Still, the amount of resources expended is proportional to the number of words generated.
Deepseek emergence was no accident, its parent company specialises in quantitative trading forcing its models to survive China’s hellish stock market: A daily battleground with 210 mill retail investors. In order to thrive, models process vast unstructured data, policy documents, social sentiments and execute efficiency within 0.01 second. This pressure cooker environment forged Chinese AI’s pragmatic edge.
Deepseek’s financial inference accuracy hits 87%, dwarfing Open AI’s 72%. This yawning divergence steps from technical priorities. Open AI’s closed ecosystem and costly APIs are 6 times pricier than Deepseek’s excluding SMEs and researchers. In contrast, Deepseek Open source framework and model distillation enable local deployment of 1 bill parameter models for under $300 a month with customization for niche applications. While US academics beg for free GPT-4 access over 200 Chinese Universities have integrated DeepSeek into research platforms. Worse, America ‘s obsession with performance theater has spawned a vicious cycle.
In order to placate investors, firms inflate parameters. The US GPT-5 may hit $10 Trillions market valuation bubble while ignoring safety, efficiency and ethics. When AI access benchmarks but fails a Chinese high school math exam, it’s clear that chasing score maestro divorces technology from reality.
In conclusion, this is a story about how an ecosystem trumps an echo chamber. Deepseek reflects a fundamental divide in AI development. China’s path is rooted in industrial integration and has nurtured technology like Huawei’s As end GPUs refined by smart manufacturing and autonomous driving. Deepseek itself through 210 mill Chinese users, a crowdsourced evolution meanwhile America’s closed source approach while protecting short term profits stifles innovation. Github data shows 42, 000 vertical applications built on Deepseek open models across 40+ industries versus fewer than 8000 GPT based tools mostly for marketing and entertainment.
When technology offers a new path for global AI development presenting an alternative to the computing power in everything approach. It reflects the philosophical insights and deep exploration of AI from the Eastern civilization, where egalitarianism leads the way against Western civilization guided by rationalism and its associated narcissism. History shows technological revolutions are won not by single metrics but by ecosystem vitality. If the US clings to its compute supremacy delusions, it will sink deeper into a high-cost and low-return quagmire. As NVIDIA’s phenomenal crash proved, when illusions fade, reality bites harder than anyone can anticipate.
Chinese strategy to close the STEM gap in the last two decades!
Deepseek’s success in the global digital market confirms beyond reasonable doubt that the Chinese development model finally worked. China is renowned for the mass production of STEM graduates, but not for innovation prowess. Of interest, all the brains behind the startup, DeepSeek, are Chinese homegrown graduates who had attended local universities testifying further that Chinese tertiary education has broken barriers, taboos and now stands tall as a beacon of what faith in one’s abilities coupled with sheer grit can achieve.
The Western world is understandably bitter and inconsolable that their strategic designations of fissuring China from Russia during the Cold War didn’t weaken both but has transformed China into a technological monster that is no longer on the US leash of strategic influences. It is true China pirated Western technology and copy-pasted most of her way to technological supremacy, but that is not the whole story. Apart from hitchhiking her way to global technological dominance through intellectual theft and bribery, there was a frenetic Chinese investment to integrate knowledge and skills on one hand with industrial production on the other. China is least acknowledged for technological upgrades because of intellectual property rights infringements.
China has invested heavily in education, especially in science and technology, which has helped nurture a significant pool of talent, key to its ambition of becoming a world leader in A.I. by 2025. That had worked superbly! For many in China, the strength of its education system is closely tied to the nation’s global status. The government has invested heavily in higher education, and the number of university graduates each year, once minuscule, has grown more than 14-fold in the past two decades. Several Chinese universities now rank among the world’s best. Still, for decades, China’s best and brightest students have gone abroad, and many have stuck there.
Yet, that significant brain drain didn’t bleed to death Chinese tremendous growth in skills and knowledge. China produced more than four times as many STEM graduates in 2020 as the United States. Specifically in A.I., it has added more than 2,300 undergraduate programs since 2018, according to research by MacroPolo, a Chicago-based research group that studies China. By 2022, nearly half of the world’s top A.I. researchers came from Chinese undergraduate institutions, as opposed to about 18 per cent from American ones, MacroPolo found. And while the majority of those top researchers still work in the United States, a growing number are working in China.
Some have criticized China’s educational system as overly exam-oriented and stifling to creativity and innovation. The expansion of China’s A.I. education has been uneven, and not every program is producing top-tier talent. But China’s top schools, such as Tsinghua University and Peking University, are world-class; many of DeepSeek’s employees studied there.
The Chinese government has also helped foster more robust ties between academia and enterprises than in the West. It has poured money into research projects and encouraged academics to contribute to national A.I. initiatives. Regrettably, however, government overt meddling is also one of the biggest potential threats to Chinese innovation and sustenance. Government interference, as seen with the crackdown of major tech companies such as Alibaba and others, after losing control, is behind the launch of deepseek.
DeepSeek’s founder, Liang Wenfeng, pivoted to A.I. from his previous focus on speculative trading, in part because of a separate government crackdown there. The resulting layoffs at tech companies, following the monthlong crackdowns, combined with the uncertainty of the sector’s future, helped diminish the appeal of a sector that once attracted many of China’s top students. Record numbers of young people have opted instead to compete for civil service jobs, which are low-paying but proffer job security.
A.I. has been somewhat shielded from the brain drain, so far, in part because of its political imprimatur. China’s long-term A.I. competitiveness hinges not only on its STEM education system, but also on its handling of private investors, entrepreneurs and for-profit companies. Chinese entrepreneurs have been criticized for being too good at improving innovations of pirated technologies rather than being pioneers of their own homegrown innovation partly because the rewards system is results-oriented rather than pivoting towards long-term research and development. That is likely to change.
Read more analysis by Rutashubanyuma Nestory
- The **NeMo (Neural Modules) Framework** is an open-source toolkit developed by **NVIDIA** for building, training, and fine-tuning state-of-the-art conversational AI models. It is designed to simplify the development of AI models for natural language processing (NLP), automatic speech recognition (ASR), and text-to-speech (TTS) tasks. NeMo is built on top of **PyTorch** and leverages NVIDIA’s GPU-accelerated libraries like **cuDNN**, **cuBLAS**, and **NCCL** for high-performance training and inference.
### Key Features of NeMo:
1. **Modular Design**:
– NeMo uses a modular approach, where each component (e.g., encoder, decoder, loss function) is implemented as a reusable “neural module.”
– This modularity allows developers to easily mix and match components to create custom models.
2. **Prebuilt Models and Modules**:
– NeMo provides a wide range of prebuilt models and modules for tasks like:
– **ASR**: Automatic Speech Recognition (e.g., Jasper, QuartzNet).
– **NLP**: Natural Language Processing (e.g., BERT, GPT, T5).
– **TTS**: Text-to-Speech (e.g., Tacotron2, FastPitch).
– These models can be used out-of-the-box or fine-tuned for specific tasks.
3. **Ease of Use**:
– NeMo abstracts much of the complexity of building and training AI models, making it accessible to both researchers and developers.
– It provides high-level APIs for training, evaluation, and inference.
4. **Multi-GPU and Multi-Node Training**:
– NeMo supports distributed training across multiple GPUs and nodes, enabling scalable training of large models.
5. **Integration with NVIDIA Tools**:
– NeMo integrates seamlessly with other NVIDIA AI tools like **NVIDIA Triton Inference Server**, **TensorRT**, and **RAPIDS** for optimized deployment and inference.
6. **Customizability**:
– Developers can easily extend NeMo by adding custom modules, loss functions, or datasets.
7. **Community and Ecosystem**:
– NeMo has an active community and is part of the broader NVIDIA AI ecosystem, which includes tools li ↩︎
