Sometimes, a moment hits the tech world so hard that it forces everyone to sit up and rethink everything. The launch of OpenAI’s ChatGPT was one of those moments. But now, China’s DeepSeek is rewriting the rules—and doing it on a fraction of the budget.
Last week, I tuned into The Take podcast by Al Jazeera, where MIT Tech Review’s Caiwei Chen broke down DeepSeek’s shocking rise. What’s fascinating isn’t just what they built—but how they built it. Against all odds, DeepSeek has rattled Silicon Valley, sent tech stocks tumbling, and disrupted the AI landscape like never before.
Caiwei Chen on Why DeepSeek Is Reshaping AI Faster Than Expected
A week ago, barely anyone outside of China had heard of DeepSeek. Now? It’s a name that’s making AI researchers and investors sweat. Reuters reports that Nvidia’s stock plunged by 17% following DeepSeek’s AI model announcement, a sign of how much of a shake-up this has been for the market.
Caiwei Chen, speaking on The Take, explains that while DeepSeek wasn’t widely known outside of China, there was already buzz about it among AI researchers. “The company was already having a little hype within the Chinese AI world,” she says. Still, no one predicted the kind of global reaction it received.
Think about that for a second. OpenAI has spent billions training its models, relying on cutting-edge microprocessors and bottomless capital. Yet, Pymnts.com notes that DeepSeek built its model with a lean team of just 100-200 researchers and under $6 million, making it one of the most cost-efficient AI breakthroughs in history.
This isn’t just an AI race anymore—it’s a revolution in efficiency.
Chen Explains Why DeepSeek’s AI Is Beating the Odds
DeepSeek’s R1 model doesn’t just match ChatGPT-3.5—in some cases, it outperforms it. But the real surprise? They did it without the massive resources that Silicon Valley companies throw at AI.
Chen describes how DeepSeek’s efficiency “amazed a lot of AI researchers” who didn’t think such results were possible with limited resources. “People tend to think this will make China more stunted in the progress of AI development,” she notes, but instead, DeepSeek’s constraints forced it to be more innovative.
Unlike OpenAI, which depends on Nvidia’s cutting-edge H100 chips, DeepSeek worked with much less powerful hardware. Yet, they still managed to build something that rivals the best AI out there. It’s like winning a Formula 1 race in a car that costs a tenth of the competition’s budget.
The secret isn’t brute-force computing—it’s smarter engineering. DeepSeek’s model uses Mixture of Experts (MoE), a method that only activates the parts of the AI that are needed for a task. That means it’s faster, more efficient, and doesn’t waste processing power like other models do.
Then there’s the open-source factor. While OpenAI locks down its technology, DeepSeek has made its model public, letting developers and researchers around the world test, improve, and innovate on top of it. “It’s not gatekeeping its research results,” Chen points out. “They have published all their training process and findings under an open-source license.”
This approach is shaking up the industry. AI development is no longer just about who spends the most—it’s about who thinks the smartest.
DeepSeek’s Secret Strategy: How They Trained AI Despite U.S. Sanctions
Here’s where things get even more interesting. The U.S. has restricted China’s access to advanced AI chips, making it harder for Chinese companies to compete. Yet, DeepSeek still found a way.
According to Wikipedia, DeepSeek’s founder Liang Wenfeng stockpiled over 10,000 Nvidia chips before the U.S. export ban went into effect. This strategic move ensured they had enough computing power to train their AI despite sanctions (Wikipedia). Some U.S. AI experts, including Google’s AI chief Demis Hassabis, have expressed skepticism about DeepSeek’s reported low development costs, suggesting that their budget numbers may be misleading (Pymnts).
And it’s not just about AI. This moment highlights a larger shift in global technology leadership. If Chinese companies can achieve top-tier AI with fewer resources and tighter budgets, what does that mean for U.S. tech dominance in the long run?
Is This the Moment That Breaks Silicon Valley’s AI Monopoly?
Until now, the assumption was that only a handful of big-tech giants could dominate AI—Google, OpenAI, Microsoft, Meta. But DeepSeek just shattered that illusion.
For years, Silicon Valley has argued that AI requires massive investments, cutting-edge chips, and vast data centers. DeepSeek has shown that this might not be true anymore.
Chen notes that even U.S. politicians are paying attention. Former President Donald Trump called DeepSeek’s release “a wake-up call” for U.S. industries, urging companies to stay competitive. Meanwhile, industry leaders like Elon Musk have raised doubts about whether DeepSeek’s model was trained using OpenAI data. “There’s some bitterness from U.S. companies,” Chen adds, “because their previous narrative was that you need massive resources to build this advanced technology.”
This shift raises big questions:
- Can AI innovation survive outside of billion-dollar companies?
- Will the open-source movement outpace closed-source AI models?
- How will governments react to China’s rising AI influence?
One thing’s for sure—the AI race is no longer a one-country game.
What This Interview Revealed About AI’s Future—And My Take on It
This isn’t just another AI company making headlines. DeepSeek’s success is a wake-up call.
Here’s what I see happening next:
- Silicon Valley will rethink its spending. If DeepSeek can do this for $6 million, how long will investors tolerate billion-dollar AI budgets?
- OpenAI and Google will face real competition. The era of a few U.S. giants dominating AI is coming to an end.
- Efficiency will become the new gold standard. The next AI breakthroughs won’t come from who spends the most—but who engineers the smartest.
We’re watching a fundamental shift in how AI is built, who builds it, and how it shapes the future. And personally? I can’t wait to see what happens next.
Final Thoughts & Source Credit
This blog was inspired by insights from Caiwei Chen’s discussion on Al Jazeera’s The Take podcast. Huge credit to the entire production team for breaking down this complex story. If you want to listen to the full episode, check it out here.
Additionally, factual data has been sourced from Reuters, Pymnts.com, and Wikipedia to ensure credibility.
DeepSeek’s rise is just the beginning. AI is evolving faster than ever, and the game is changing in ways we never expected. The only question left is: who will adapt the fastest?