Skepticism Around DeepSeek’s Claims
DeepSeek’s assertions about its advancements have drawn significant attention, but much of it remains unverified. For a technology that allegedly leapfrogs existing capabilities, the specifics around its breakthroughs are conspicuously lacking. Transparency has always been a cornerstone for evaluating cutting-edge technologies, and until DeepSeek provides more concrete evidence, skepticism is not just warranted—it’s necessary.
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.
Overreaction vs. Reality
The broader market response underscores the dangers of overreaction. While innovation in AI tools is undeniably exciting, we’ve seen time and again how unverified claims can lead to speculative bubbles. For investors, this is a moment to pause, ask questions, and demand clarity before assigning sky-high valuations to unproven technologies.
In summary, while Deepseek’s story is intriguing, it’s imperative to separate fact from speculation. The market needs to temper its enthusiasm and demand more transparency before awarding DeepSeek the crown of AI innovation. Until then, skepticism remains a healthy and necessary stance.
Don’t think this holds true. Nvidia still has a market capitalization of almost 3 trillion dollars?! If this was some overcorrection, the AI companies and suppliers would compare to regular companies by now. But they’re still worth like more than all the traditional car manufacturers added together. So it’s still a hugely overinflated bubble and nothing has popped yet. Or being overcorrected.
This article was written and sponsored by ChatGPT.
The hype for language model based “ai” was a bigger overreaction.
News orgs have gotten rather great at building hype trains, justified or not
I’m sure his opinion is no way influenced by his own company’s goals.
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DeepSeek’s assertions about its advancements have drawn significant attention <…>
Only from rival companies which 10 out of 11 top models are from US based companies.
Transparency has always been a cornerstone for evaluating cutting-edge technologies, and until DeepSeek provides more concrete evidence, skepticism is not just warranted—it’s necessary.
That is the most straight-faced lied ever told… Literally all cutting-edge tech is hidden behind NDA’s or trade secret classifications. US own LLM models are mostly closed source, with even weights not being available. There is literally lawsuits in the US about lack of transparency on the data used in training the models.
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.
Which is irrelevant in context of model capabilities. It’s already trained and that’s the competition now. Which article like these really shows US based companies are incapable of going against.
How did DeepSeek manage to foot this bill, especially given their previously disclosed financials?
Again, irrelevant in context of model capabilities.
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.
Fair and LLM is an oxymoron. But also US wouldn’t know fair if it bit them in the ass. There companies are literally begging US government to block or limit high-end hardware to being sent to even their allied countries… That’s sounds “fair”, right?
While innovation in AI tools is undeniably exciting, we’ve seen time and again how unverified claims can lead to speculative bubbles.
Yes, the whole “AI” is a speculative bubble. You just want to be able to milk it yourself before it bursts. And DeepSeek news made all that free cash to dry up.
Please stop making me defend LLMs from these paid hit pieces.
All I know is that the US government wants to ban us from using it, so it’s got my support
Speculative execution is not a new idea. Speculative training is just a new application.We tend to throw money at problems because we have it. They seem to have sufficient motivation to work more efficiently through sanctions.
I absolutely love you guys all seeing through this .
Investment advice from techradar.
My goodness.
