Is the DeepSeek hype justified? | usagoldmines.com

Chips on the Table: Do They Have More Than We Think?

One of the most puzzling aspects of the DeepSeek story is the apparent discrepancy between the resources they claim to have and those they might actually possess. Analysts are increasingly suspicious that DeepSeek may have access to far more hardware—particularly high-performance chips—than has been publicly disclosed. If true, this could have significant implications for their capacity to train and deploy their models at scale, raising questions about how they’ve managed to secure such resources.

The Training Puzzle: Costs and Methodology

Another critical angle here is the cost and methodology behind training their purportedly groundbreaking model. Training large language models (LLMs) is notoriously expensive and resource-intensive, often running into tens or even hundreds of millions of dollars. How did DeepSeek manage to foot this bill, especially given their previously disclosed financials? Additionally, there’s an elephant in the room: Did they rely on other LLMs during training? This would raise ethical and competitive concerns, as it has long been recognized as a controversial practice in the AI community. Leveraging other providers’ models for training—potentially without permission—distorts fair competition and undermines trust in the ecosystem.