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Deepseek: A Closer Look at the Emerging AI App

Writer's picture: Team WrittenTeam Written

Updated: Feb 7

Deepseek, a relatively new entrant in the artificial intelligence arena, has gained international attention for its claim that it can match or even surpass the performance of established AI models at only a fraction of the usual cost. Founded two years ago as a spinout of a quantitative trading fund in Hangzhou—a major tech hub in eastern China—the company originally focused on using graphical processing units (GPUs) to identify stock market patterns. Its success in algorithmic trading, combined with an innovative approach to resource allocation, laid the groundwork for the development of its open-source language model.


In what some analysts have termed an “AI apocalypse moment,” major tech stocks, particularly those linked to AI chip manufacturing, experienced a sudden and dramatic downturn. NVIDIA, for example, reportedly lost hundreds of billions of dollars in market value in a single day, alarming investors and prompting speculation about an overinflated AI hype cycle. A trillion-dollar sell-off occurred worldwide, triggering debate over whether these events signaled the bursting of an AI bubble or merely a short-lived shock that could ultimately democratize AI technology.


Although Deepseek’s creators emphasize a mission of making general-purpose AI accessible “for the fun of it, not for the money,” skeptics have questioned whether the app truly operates on the minimal budget the company claims. Some observers contend that Deepseek’s reported figure of under $6 million may exclude the hidden costs of infrastructure, data acquisition, and ongoing research. Others posit that the project leverages outputs from U.S.-based AI models without fully disclosing the extent to which those outputs contributed to its training data.


While Deepseek publicly states that it spent only $6 million on training, critics in the United States allege the company has access to far more high-end GPUs than it admits—possibly exceeding 50,000. This discrepancy leads some to suspect that Deepseek may be quietly benefiting from extensive hardware resources or state-backed initiatives. Certain venture capitalists in Silicon Valley even suggest Deepseek is part of an orchestrated effort to undermine American AI competitiveness by driving down model-pricing—an accusation Deepseek has not directly addressed.


Shortly after Deepseek’s surge in popularity—during which it topped download charts—the platform reportedly fell victim to a large-scale cyber attack. This assault severely limited new user registrations and reduced the app’s capabilities for existing users. Simultaneously, global stock markets saw over $1 trillion in value wiped out in one day, and is the largest single-day market value loss in U.S. history.


Opinions differ on whether these events were coincidental or part of a broader backlash against rapid AI development. Some have pointed to prominent AI critics like Gary Marcus, who may be taking a “victory lap” on social media over these market jitters. Despite the dramatic headlines, Deepseek’s leadership remains confident and has announced plans to bolster security measures to restore full functionality.


Much like more established platforms, Deepseek provides natural-language responses to complex questions, from real-time research assistance to the generation of creative content. However, Deepseek distinguishes itself through a family of models—V3, R1, and R10—each designed for different tasks:

V3: Notable for robust performance in fields like programming and mathematics, made possible by knowledge distillation and a lightweight structure.

R1: Deepseek’s “reasoning model” that applies multi-step inference or chain-of-thought approaches, improving both accuracy and complexity of responses.

R10: Rumored to extend the same mixture-of-experts approach to specialized domains such as legal and scientific research.


Despite debates regarding its actual hardware usage, Deepseek’s reported efficiency has drawn significant interest from consumers worldwide. By employing a mixture-of-experts (MoE) architecture, it can allocate computing resources selectively rather than powering a dense model at all times. Developers claim this design results in faster responses and a more streamlined user experience, especially in regions with limited bandwidth or high data costs.


Deepseek also relies on a combination of 8-bit floating-point precision, compressed key-value indices, and multi-token prediction. These optimizations reportedly allow it to handle inference using a fraction of the computational power consumed by other large language models. Moreover, Deepseek’s open-source foundation has spurred a grassroots wave of international contributors who localize the app for diverse linguistic and cultural contexts. This collaborative model fueled its rapid expansion beyond its Chinese roots—until the large-scale cyber attack briefly disrupted new registrations.


Analysts note that Deepseek’s rise coincides with a period in which many tech giants are pouring billions of dollars into data centers and advanced chips. In stark contrast, Deepseek’s more modest infrastructure—and its claim of achieving a 45x improvement in training efficiency—raises questions about the sustainability of scaling larger and larger models. These developments could spark renewed debate on whether capital-intensive strategies remain the best route to cutting-edge AI.


There are geopolitical dimensions of Deepseek’s ascent. While the United States tightens export controls on advanced AI chips, some see Deepseek’s progress as evidence that Chinese companies can produce high-performing models—even open-sourced—without unlimited access to the latest hardware. Critics, however, maintain that Deepseek might have far greater GPU resources and state support than it admits. For Silicon Valley, where research costs continue to soar, these revelations about a leaner approach pose a serious competitive challenge.


Although major players like OpenAI, Google, and Meta still dominate the market, Deepseek demonstrates how smaller, agile competitors might disrupt the status quo by balancing innovation, affordability, and mass appeal. Some believe the sudden stock market upheaval reflects broader anxiety over a future in which open-source AI—often trained with data distilled from larger U.S. models—brings down cost barriers for aspiring developers.





Despite ongoing controversy, consumers appear drawn to Deepseek’s straightforward interface and open-source philosophy. Early adopters praise its ease of use: students looking for study aids, small-business owners seeking quick research support, and casual users experimenting with AI-driven entertainment have all embraced the platform. Thanks to active community input, the app’s continuous updates and localization efforts serve an increasingly global user base.


Deepseek’s potential impact goes well beyond the novelty of a low-cost, high-performing AI system. Its user-driven enhancements may inspire new ways of building and distributing AI, especially in regions lacking the data-center resources of wealthier nations. Although questions linger about its true costs, its reliance on external data, and its possible undisclosed partnerships, Deepseek’s growing popularity highlights an industry-wide shift toward more resourceful and consumer-friendly AI tools.


Only time will tell whether Deepseek’s lean approach, combined with its open-source ethos, will permanently reshape AI’s competitive landscape. For now, the platform’s rapid ascent—and the turbulence it appears to generate in both markets and public discourse—serves as a powerful reminder of artificial intelligence’s unpredictable and ever-evolving nature.




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