DENVER, Colo., Jan 27, 2025 (247marketnews.com)- As artificial intelligence continues to shape industries and drive innovation, companies like DeepSeek are aiming to capture the market’s attention with bold claims and ambitious promises. However, a closer examination reveals potential flaws in their efficiency metrics, transparency practices, and privacy policies. For investors and stakeholders, particularly in the context of DeepSeek’s public market presence, these issues warrant further scrutiny.
Efficiency Claims: Too Good to Be True?
DeepSeek’s claim of using only 2.788 million H800 GPU hours for training its latest AI model is a staggering number that demands closer inspection. Compared to similar projects, like MicroSoft (NASDAQ: MSFT) partner, OpenAI’s GPT-4 or Meta’s (NASDAQ: META) LLaMA, such a figure appears either exaggerated or taken out of context.
- Cherry-Picked Metrics: Training cost comparisons often omit critical details such as hardware utilization rates or pretraining strategies that may have cut corners. For instance, synthetic data or checkpointing can artificially lower the apparent cost.
- Benchmarks Missing: DeepSeek has not provided evaluations on standard datasets like HELM, MMLU, or BIG-bench, making it difficult to compare its performance against competitors. Without rigorous benchmark testing, even claims of groundbreaking efficiency are hollow.
- Reliance on H800 GPUs: While DeepSeek touts its use of NVIDIA’s Hopper architecture, questions remain about the system’s real-world scalability. Hardware bottlenecks, such as inter-GPU communication inefficiencies, could diminish the practical impact of its futuristic claims.
For investors, these inconsistencies in efficiency metrics raise doubts about the company’s ability to deliver sustainable, competitive solutions in a market dominated by transparent, data-driven competitors. However, the reliance on NVIDIA GPUs (NASDAQ: NVDA) highlights the ongoing demand for cutting-edge hardware, which is a bullish signal for NVIDIA and other AI-related stocks.
Transparency Gaps: A Red Flag
Transparency is a hallmark of trust in the AI sector, yet DeepSeek has been notably tight-lipped about critical elements of its operations.
- Opaque Architecture and Methods: The company has not disclosed its training methodology, hyperparameter tuning process, or even architecture details. This lack of openness makes it nearly impossible to verify their claims.
- No Peer Review or Open Source: Unlike industry leaders that provide reproducible results and open-source some of their work, DeepSeek’s refusal to publish training logs or peer-reviewed papers is concerning. For a public company, this opacity is a significant risk factor.
Privacy Concerns: User Data at Risk
DeepSeek’s privacy policy reveals alarming practices that could expose users to significant risks. Investors should take note of the reputational and legal liabilities associated with these practices.
- Data Stored in China: User data is stored on servers located in China, subjecting it to local laws that may grant government access. This is a red flag for users in regions with stricter privacy expectations, such as the EU or the U.S.
- Extensive Data Collection: The policy permits the collection of keystroke patterns, device identifiers, and activities outside the service—a level of surveillance that many users would find invasive.
- Ambiguous Retention Policies: DeepSeek’s lack of clarity around how long user data is retained or when it is deleted creates further uncertainty. Without defined timelines, user data could remain vulnerable indefinitely.
For investors, these privacy concerns could translate into regulatory scrutiny or user backlash, both of which may negatively impact the company’s valuation.
Who’s Footing the Bill?
Another pressing question is the financial model behind DeepSeek’s services. Serving 10 million users for free is an ambitious goal, but the operational costs suggested by DeepSeek seem unrealistically low.
- DeepSeek’s Cost Estimates: The company claims hardware costs of $500 million to $2 billion and monthly operational costs of $2 to $6.5 million. By contrast, estimates for similar-scale operations suggest hardware costs closer to $5 to $10 billion and monthly expenses ranging from $95 to $200 million.
- Sustainability: Without a clear explanation of how these services are being funded, the free access model raises questions. Are these costs being subsidized by other, less-publicized revenue streams, such as advertising or data monetization?
Many others, including Tesla’s (NASDAQ: TSLA) CEO, Elon Musk, are posting tweets that appear to cast doubt on DeepSeek AI’s microchip claims.
Scale AI’s CEO, Alexandr Wang, claimed that DeepSeek has around 50,000 NVIDIA H100s, which they cannot talk about, due to export controls.
Musk simply responded “Obviously”, but he wasn’t finished.
Salesforce’s (NYSE: CRM) CEO, Marc Benioff, tweeted, “Deepseek is now #1 on the AppStore, surpassing ChatGPT—no NVIDIA supercomputers or $100M needed. The real treasure of AI isn’t the UI or the model—they’ve become commodities. The true value lies in data and metadata, the oxygen fueling AI’s potential. The future’s fortune? It’s in our data. Deepgold.”
Musk responded by tweeting, “Lmao no” and then mockingly called it “DeeperSeeker” in another tweet.
Investors should be cautious about financial claims that appear too good to be true. Overpromising on operational efficiency could indicate deeper financial risks.
DeepSeek’s bold claims and innovative ambitions are overshadowed by critical flaws in transparency, efficiency, and privacy.
Users and investors deserve accountability, openness, and responsible practices. DeepSeek has yet to prove that it can meet these expectations. Until it does, caution remains the best strategy for those considering an investment in this company.
Meanwhile, the growing reliance on advanced GPUs and AI infrastructure underscores the bullish outlook for companies like NVIDIA (NASDAQ: NVDA) and other AI-related stocks, which remain central to the industry’s continued growth.
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