By Dr Anandadeep Mandal
Birmingham Business School, University of Birmingham
The artificial intelligence (AI) landscape is evolving rapidly, and DeepSeek has been one of the most talked-about AI models in recent months. Initially hailed as a game-changer for its multi-modal learning capabilities, real-time data processing, and unsupervised learning potential, DeepSeek has faced growing scepticism due to technical difficulties, high operational costs, and concerns over its scalability.
But is DeepSeek truly revolutionising AI, or is it struggling to live up to its promises? By analysing insights from the Allen Institute for AI study, industry reports, and recent user feedback, we assess whether DeepSeek is the breakthrough model it claims to be or another overhyped AI experiment.
DeepSeek’s Hurdles: Server Failures and Accessibility Issues
Despite the initial excitement, DeepSeek is facing significant operational challenges. Many users have reported server issues and sign-up difficulties, which have raised concerns about the model’s infrastructure and scalability. Unlike established AI models from Google DeepMind, OpenAI, or Meta, DeepSeek appears to be struggling with sustained uptime and accessibility, which limits its practical usability for real-world applications.
Technical roadblocks such as unstable servers and frequent downtimes suggest that the model may not yet be fully optimised for large-scale adoption, casting doubt on its reliability in business settings. If DeepSeek is unable to deliver stable access, it risks losing credibility in an already competitive AI market.
The Hidden Cost of AI: What the Allen Institute Study Reveals
The financial and computational cost of training large-scale AI models like DeepSeek has been a critical topic in AI research. A recent study by the Allen Institute for AI highlights that the training cost for AI models of this scale can exceed $5 million, not including the additional expenses related to data acquisition, storage, and continuous updates.
The environmental impact of AI models is coming under increasing scrutiny. According to the Allen Institute report, training a single large-scale model can generate a carbon footprint equivalent to hundreds of cars operating for a lifetime. This raises ethical concerns about AI’s sustainability, especially as models like DeepSeek consume huge resources but struggle with real-world use.
If DeepSeek cannot justify its high operational costs with tangible real-world applications, it could soon be categorised as an unsustainable AI project rather than an industry revolution.
DeepSeek vs. Other AI Models: Where Does It Stand?
While DeepSeek has been positioned as a cutting-edge AI system, comparisons with other leading AI models suggest that it is not as groundbreaking as initially claimed.
- Unsupervised Learning and Multi-Modal Capabilities: Although DeepSeek handles text, images, and sound together, it’s not unique. Google DeepMind’s Gemini, Meta’s LLaMA, and OpenAI’s GPT-4 already offer multi-modal AI processing with superior infrastructure and model refinement.
- Real-Time Processing & Predictions: Despite claims of enhanced real-time decision-making, DeepSeek’s server instability has prevented it from demonstrating consistent real-time analysis at scale.
- Market Accessibility & Deployment: While GPT-4 and Claude AI models have been widely adopted in enterprise applications, DeepSeek is struggling with sign-ups, accessibility, and developer integration, making it less attractive for businesses looking for reliable AI solutions.
The hype surrounding DeepSeek appears to be more about theoretical potential rather than practical execution.
Technical Limitations of DeepSeek: Where It Falls Short
DeepSeek’s architectural design raises concerns about its efficiency, interpretability, and scalability. While the model integrates multi-modal processing, its token efficiency (how effectively an AI model processes small chunks of data like words or parts of words to generate responses) and context management (how well a model remembers, understands, and uses information from previous inputs to generate accurate and coherent responses) appear weaker than that of GPT-4 Turbo or Claude 3 Opus.
Moreover, DeepSeek lacks clear optimisation in transformer scaling (how a model handles bigger or more complex tasks efficiently as it grows in size). More advanced models like GPT-4 Turbo and Google’s Gemini 1.5 use smarter designs to balance speed, accuracy, and cost. For example, they can activate only the parts of the model they need for each task (described as “Mixture of Experts” or MoE), which saves power and improves performance, whereas, DeepSeek seems to consume high computational resources without proportionate gains in accuracy or adaptability.
User Experience: Is DeepSeek Meeting Expectations?
User feedback on DeepSeek has been mixed, with several reports of delayed responses, model inaccuracies, and system instability. Users have cited:
- Frequent sign-up issues and failed authentications, making onboarding a frustrating experience.
- Slow response times and dropped queries, suggesting that the model is struggling under real-world workloads.
- Inconsistent output quality, with users noting that DeepSeek often misinterprets context, particularly in specialised domains like finance, legal, and technical writing.
In contrast, models like GPT-4 Turbo, Gemini, and Claude 3 have streamlined user interface (UI) and user experience (UX) experiences, offering faster processing, stable API integrations (connections between systems), and more accurate contextual predictions.
For a model that claims to be a game-changer, DeepSeek’s poor user experience raises concerns about its actual competitiveness.
The Global AI Arms Race: Is DeepSeek Falling Behind?
DeepSeek’s launch came amid the intensifying AI competition between global players like Google, Microsoft, Baidu, and OpenAI. The model was expected to contribute significantly to China’s AI ecosystem, providing a homegrown alternative to U.S.-dominated AI advancements. However, its technical limitations and adoption struggles suggest that it may not be able to compete effectively on the global stage.
Recent reports indicate that government-backed AI initiatives in China, the U.S., and the EU are prioritising AI models with proven industrial applications, especially in cybersecurity, healthcare, and financial modelling. DeepSeek’s current struggles with infrastructure and accessibility could mean that it loses relevance in this highly competitive environment unless rapid improvements are made.
Conclusion: Is DeepSeek the Future or Just Another AI Experiment?
While DeepSeek initially generated significant excitement, its current challenges highlight fundamental issues in AI deployment, including scalability, accessibility, and operational costs. The model has yet to demonstrate significant advantages over existing AI leaders, and its technical difficulties are undermining its credibility.
Key Takeaways:
- DeepSeek is facing technical challenges, including server failures and sign-up issues, limiting its usability.
- The Allen Institute’s study highlights the high costs of AI model training, raising questions about DeepSeek’s sustainability.
- Compared to established AI leaders like GPT-4, Gemini, and LLaMA, DeepSeek does not offer a clear competitive advantage.
- Its role in the global AI race remains uncertain, as competitors with stronger infrastructure and real-world applications continue to dominate.
For now, DeepSeek remains an interesting AI experiment rather than a true market disruptor. Unless its scalability, accessibility, and real-world applications are significantly improved, it may struggle to maintain relevance in the rapidly advancing AI landscape.
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The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of the University of Birmingham.