If you have access to the internet (which you do since you’re reading this), you’ve seen something about DeepSeek. If you’ve paid a little closer attention, you’ve probably noticed how it’s put big tech through serious hysteria — as well as all of its investors.
In the blink of an eye, the Chinese-developed AI model has shaken the foundations of the industry, proving that large language models (LLMs) can be trained with far fewer resources than we all thought possible. But the implications of this breakthrough stretch far beyond the AI community, sending significant ripples through tech stock markets and reshaping the competitive landscape of machine learning.
So here’s everything we know about DeepSeek right now, and why its emergence, methodology, and efficiencies will actually be good for all of us in the not-so-long-run.
How DeepSeek Shook Up the Market
The existing AI arms race has been fueled by the assumption that success required access to the most innovative and expensive chips, massive amounts of electricity, and billion-dollar budgets. If you look at OpenAI’s GPT-4, for example, immense computational resources were used for training, which continued to convince everyone that only the biggest, wealthiest tech players could compete in this space.
But that’s when DeepSeek proved that all wrong. According to the company’s released reports, they managed to develop an LLM comparable to OpenAI’s early ChatGPT models at a fraction of their budget using older chipsets. How much cheaper was it really? Well, those reports are saying they used around just 5% of the resources that OpenAI used.
All of a sudden, all the major AI companies were in a super fragile position, forced to reassess their strategies, while likely getting yelled at by their management teams as to how this type of engineering was even possible when they were told it wasn’t. No longer are the most advanced GPUs from giants like Nvidia the only way to be dominant in the market.
A Frenzied Major Stock Market Selloff
Speaking of Nvidia, a chip-making company whose stock price was built on the premise that its high-end GPUs were indispensable for AI training, took the biggest hit of all this week — with a single-day market capitalization loss of $600 billion. But the leading U.S. Tech index saw a cumulative loss of $1 trillion overall, marking an unprecedented decline (The Guardian). Reason being investors had provided the company’s top-tier chip as a must-have for the AI-race, which was now in question. So the demand for their most expensive hardware might diminish.
But that’s not all. Since the model uses 10-40x less energy than its U.S. counterparts, there was also a direct hit to power, utility, and natural gas, hindering the power demand growth, at least temporarily. Companies like Talen Energy Corp and Vistra were down 22% and 30%, respectively (Reuters).
At first, some speculated that an event this impactful might signal the bursting of an AI bubble with lower costs leading to lower barriers to entry, and as a result, potential market saturation. But before you start to worry, that interpretation simply misses the bigger picture.
Why This is Just the Beginning — & a Good Thing
Rather than moving the dominance of AI development to foreign competitors, DeepSeek’s innovation will likely supercharge it here in the US. Think of it this way, with a better understanding of DeepSeek’s low-cost training methods, tech giants won’t suddenly slash their budgets — instead, they’ll optimize their spending to achieve exponentially greater results.
What may also be the most exciting (and promising for those of you who are really worried) aspect of DeepSeek is that it’s open-source, meaning any company or developer can examine its code and potentially apply the same methodology and efficiency breakthroughs to their own models. So not only could this potentially open the floodgates for a new wave of AI startups for those who act quickly. And while it could give smaller teams with more modest budgets the opportunity to create some pretty interesting stuff, it will also foster even bigger, better results from those already on top.
The biggest tech companies will study DeepSeek’s machine learning and apply those algorithms into their far more powerful infrastructures. Think about it — in what world would companies like Google or OpenAI cut their spend simply to do just what’s already being done? Realizing they can reduce training costs by 90% without hindering performance, they’ll almost certainly reinvest these savings into developing more robust and intelligent models. Forget the breaks, it’s full steam ahead for all.
You can see the panic easing already as Nvidia stock rebounded 8.8% in the days following the selloff. The tech market has always been a volatile one and AI is not a fragile trend. But this does mean Nvidia can no longer rely on the assumption that every AI model needs its latest, most expensive chips. Which in turn will also simply create a more competitive landscape for AI hardware as well.
The U.S. - China Dynamic: The Irony of Innovation
There’s a funny twist to DeepSeek’s rise (at least we think so). In an attempt to curb its AI development, the U.S. has restricted exports of high-end semiconductor technology to China. But rather than stifling progress as it intended, the limitations forced Chinese engineers to innovate with the tools they had and come up with something better — and cheaper. Like China tends to do.
This obviously isn’t the first time this dynamic has played out, and it won’t be the last. When pushed by adversity, engineers find creative solutions—and sometimes, those solutions change the game entirely. Limitation is the mother of creativity, as they say.
Stay Tuned
DeepSeek hasn’t simply disrupted AI, it’s rewritten the rules. But it won’t come without some competitive smack talk. In just the last day or two, there has been some major skepticism thrown around by the likes of Elon Musk of SpaceX and Palmer Luckey, Founder of Oculus VR, as to if these numbers can actually be taken at face value. While the low cost of training itself may be plausible, the cost of fine-tuning and post-processing would certainly add to it — which leads to more speculation as to how the model was actually built (Al Jazeera).
This is where OpenAI accused DeepSeek of not training its models from scratch, but in fact, misappropriating large amounts of data from the leading AI system. And in that case, their “cheap” training is actually a deceptive claim.
The new platform has also already been banned on Google and Apple app stores in countries like Italy and Ireland due to mounting security concerns, in an effort to protect users’ personal data.
But regardless of what comes out of the backlash, the pressure is on for all big tech to innovate even faster, while democratizing AI development through open-source access. The AI revolution here in the U.S. and abroad is far from over — it’s really just barely beginning.
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