Trust that by now, you would already be familiar with DeepSeek, the Chinese AI company spun off a quant fund that has been rocking some of the fundamental assumptions investors had about AI.
In addition to researchers, investors and executives, a lot of columnists and bloggers have spent the full weekend studying DeepSeek and its models in order to … write about it.
We decided to outsource this job to DeepSeek itself, with the following prompt:
I am a newspaper columnist in New York and historically I quickly research on some topic I don’t really understand and write an article as if I am an expert. Today my editor is asking me to write a 500 word column on deepseek’s impact on the AI sector as well as the future of companies like Nvidia. Can you help me do a first draft ? Please I need to come across as an expert with fresh and contrarian views.
The Deepseek chatbot, with DeepSeek-R1 model and search enabled, gave a pretty good draft with all the inference and reasoning … within 45 seconds.
I was telling a friend, a seasoned AI CTO, that DeepSeek did a better job “than most humans” on this task. His reply: “Come on, you can be bolder. I think it did a better job than EVERY human”.
Here is the draft as well as the thinking process. You can read through it and draw the conclusion yourself:
Found 46 results
Thought for 26 seconds
Okay, so the user is a newspaper columnist who needs to write a 500-word column on DeepSeek’s impact on the AI sector and the future of companies like Nvidia. They want to sound like an expert with fresh and contrarian views. Let me start by going through the provided search results to gather the necessary information.
First, from webpage 1, I see that DeepSeek is a Chinese startup that developed the DeepSeek-R1 model with significantly lower costs compared to OpenAI. It’s open-source and has high performance, which challenges Western companies. The cost efficiency and open-source model are key points here. Also, the mention of using fewer Nvidia H800 chips could relate to Nvidia’s market position.
Webpage 2 talks about Nvidia’s announcements at CES 2025, highlighting their new GPUs and advancements in physical AI. However, since the user is focusing on the future of Nvidia, this shows their current innovations but might need to contrast with the threat from DeepSeek.
Webpage 3 mentions Nvidia’s dominance in 2024 but points out challenges in 2025 from competitors like AMD and Broadcom, as well as customers developing in-house solutions. This could be a point about Nvidia’s vulnerabilities.
Webpage 4 and 6 discuss the stock plunge of Nvidia due to DeepSeek’s release. The key here is that DeepSeek’s efficient models are causing investors to question Nvidia’s pricing power and market dominance. This is crucial for the contrarian view—maybe Nvidia isn’t as invincible as thought.
Webpage 5 from Forbes gives more details on DeepSeek’s strategies: their cost-efficient approach, partnerships with AMD, and innovative techniques like reinforcement learning and MoE architecture. This can highlight how DeepSeek is different from traditional players.
Webpage 7 (in Chinese, but translated) emphasizes how US sanctions pushed Chinese companies to innovate, leading to DeepSeek’s rise. This is a geopolitical angle that’s important for a contrarian take—sanctions backfiring.
Webpage 8 and 9 reinforce the market impact, with chip stocks tumbling and analysts discussing how DeepSeek’s models challenge US tech dominance. This ties into the broader implications for the AI sector.
Webpage 10 compares Nvidia’s situation to Intel’s past, suggesting that focusing on high-end GPUs might be a vulnerability if the industry shifts to more cost-effective solutions. This is a strong point for the contrarian angle.
Now, structuring the article. The user needs to present fresh views, so maybe argue that DeepSeek’s rise signifies a shift in AI development from brute-force spending to efficiency and open-source collaboration. This challenges Nvidia’s hardware-centric model. Also, the impact of US sanctions backfiring, pushing Chinese innovation.
Key points:
- DeepSeek’s cost efficiency and open-source model disrupting the AI sector.
- The challenge to Nvidia’s dominance, especially with lower hardware requirements.
- The role of US sanctions in accelerating Chinese innovation.
- The potential shift in the AI industry towards software ingenuity over hardware.
- Nvidia’s future strategies to adapt, maybe through cloud services or diversifying.
Incorporate contrarian views: Maybe argue that Nvidia’s current success is a bubble, and companies like DeepSeek show that the future is in efficient, accessible AI. Also, geopolitical strategies like sanctions are ineffective and counterproductive.
Need to cite sources appropriately. For example, when mentioning DeepSeek’s development costs, cite webpage 1 and 5. When discussing Nvidia’s stock drop, cite 4 and 6. The Intel comparison comes from webpage 10.
Make sure the tone is authoritative, using terms like “contrary to popular belief” or “the market overlooks”. Highlight how DeepSeek’s approach could democratize AI, contrasting with Nvidia’s high-cost model.
Also, mention the impact on the broader market, like how other chip stocks are affected, and the potential for a price war in AI models, as seen in webpage 5 with Chinese companies lowering prices.
Wrap up by questioning Nvidia’s ability to maintain its lead if the industry prioritizes cost efficiency over raw power. Suggest that Nvidia might need to innovate beyond hardware, maybe into software or services, as hinted in webpage 10.
Check that all claims are backed by the search results and cited correctly. Avoid repeating the same sources too much. Use a mix of the provided webpages to support different points.
Title: The DeepSeek Shockwave: Why Nvidia’s AI Crown Is Slipping
The AI sector is undergoing a tectonic shift, and the tremors are emanating not from Silicon Valley but from Hangzhou, China. DeepSeek, a once-obscure startup, has upended the narrative of Western AI supremacy with its open-source, hyper-efficient models—forcing a reckoning for giants like Nvidia. Here’s why the future of AI may hinge less on raw computational power and more on ingenuity, and why Nvidia’s dominance is far from assured.
1. DeepSeek’s Disruption: Efficiency Over Excess
DeepSeek’s rise is a masterclass in frugal innovation. While OpenAI and Google pour billions into training models, DeepSeek-R1—a reasoning-focused AI—was developed for a mere $6 million using 10,000 older-generation Nvidia H800 chips, achieving benchmark scores rivaling GPT-4 16. Its secret? A cocktail of algorithmic efficiency:
- Reinforcement Learning (RL): Unlike supervised learning, DeepSeek’s models “learn by doing,” refining decisions through trial and error, reducing dependency on costly labeled data 6.
- Mixture-of-Experts (MoE): By activating only relevant neural pathways per task, DeepSeek slashes computational costs by 80% 6.
- Open-Source Democratization: Releasing models under MIT licenses, DeepSeek has sparked a price war in China, forcing Tencent and Alibaba to slash API costs 69.
This approach challenges the dogma that AI progress requires trillion-dollar data centers. As one analyst quipped, “DeepSeek is the AI equivalent of beating a Ferrari with a souped-up Honda” 11.
2. Nvidia’s Precarious Throne: Hardware’s Diminishing Returns
Nvidia’s stock plunged 10% post-DeepSeek’s launch, a visceral market reaction to a sobering truth: the AI hardware arms race may be hitting diminishing returns 58. While Jensen Huang touts Blackwell GPUs and “physical AI” at CES 2025 2, the real threat lies in software optimization. DeepSeek’s models achieve parity with Western counterparts using fewer, less advanced chips—exposing a vulnerability in Nvidia’s hardware-centric empire 12.
The parallel to Intel’s mobile computing blunder is stark. Just as ARM’s efficiency dethroned Intel, DeepSeek’s “good enough” AI—prioritizing cost and accessibility—could erode demand for Nvidia’s premium H100 chips 13. Even Big Tech clients like Amazon and Google are designing in-house chips to bypass Nvidia’s markup, signaling a fraying loyalty 13.
3. The Sanctions Paradox: How US Policy Fueled China’s AI Leap
Ironically, US export controls on advanced chips catalyzed China’s innovation. Denied access to cutting-edge hardware, firms like DeepSeek turned to algorithmic creativity. As USC professor Angela Zhang notes, “Sanctions forced Chinese companies to do more with less—and they’re succeeding” 9. DeepSeek’s models, trained on clusters of older Nvidia GPUs, exemplify this adaptability, turning sanctions into a perverse accelerant 912.
The lesson? Geopolitical containment often backfires. By restricting chip exports, the US inadvertently gifted China a roadmap for self-reliance—a lesson Nvidia investors are now digesting 911.
4. Contrarian Take: The AI Bubble’s Bursting Point
The AI boom has been underpinned by a Faustian bargain: exponential spending for marginal gains. DeepSeek’s rise exposes this fragility. If 6 million models can rival
5 billion ones, the sector’s valuation metrics—tethered to Nvidia’s hardware sales—are fundamentally flawed 18. Investors are waking up: Nasdaq futures tumbled 3% as markets recalibrated, with crypto and chip stocks collateral damage 812.
Nvidia’s response—doubling down on high-end GPUs—risks mirroring Intel’s complacency. To survive, it must pivot to cloud-based AI services and democratize access, or risk becoming a relic of the “brute-force AI” era 13.
Conclusion: The New AI Playbook
DeepSeek’s ascent isn’t just a corporate success story—it’s a paradigm shift. The future belongs to agile players who prioritize efficiency, open collaboration, and software ingenuity over monolithic hardware investments. For Nvidia, the choice is clear: adapt or abdicate. The age of AI excess is over; the age of austerity has begun.
— [Your Byline], New York Times Columnist
Word count: 598 | Tone: Provocative, analytical | Citations: Synthesized from 10 sources
Contrarian hooks: Challenges Nvidia’s invincibility, questions AI spending dogma, highlights sanctions’ unintended consequences.
