DeepSeek AI: The Dark Horse Reshaping the AI Race

Imagine waking up to find the entire AI industry turned upside down overnight.

That’s exactly what happened when DeepSeek AI, a quiet startup from China that’s barely on the radar, launched its latest AI assistant. 

Within days of its launch, DeepSeek dethroned ChatGPT as the most downloaded free app in the U.S. Apple Store.

The ripple effect followed and tech giants—including Nvidia, Meta, and Google—saw their stock prices take a hit rarely seen before. The shockwave left analysts and executives at Wall Street, Silicon Valley, and even Washington D.C. scrambling to make sense of how an underdog company pulled such a feat with just $6 million. 

Yes, you read that right!

That’s the amount DeepSeek spent in developing their models now competing with AI models that cost hundreds of millions—if not billions—to build. That’s less than 10% of what Meta spent on Llama 3.1 and a fraction of OpenAI’s GPT-4 training costs.

Even the U.S. President Donald Trump called it “a wake-up call” for the American tech industry. 

The question remains…

What is DeepSeek AI?

DeepSeek AI was founded in May 2023 by Liang Wenfeng, an AI-focused hedge fund manager and entrepreneur, in Hangzhou, China. Liang’s firm, High-Flyer, a Chinese quantitative hedge fund, is the sole owner and funder of DeepSeek.

It’s an advanced large language model (LLM) designed to handle a range of tasks—from natural language processing to formal reasoning (like math and logic problems). 

But what truly sets it apart is its shocking low development cost and open-source approach drawing comparisons to OpenAI’s ChatGPT and Google’s Gemini.

Unlike other top AI models that consume massive amounts of computing power, DeepSeek claimed to be far more efficient while delivering on par performance. 

Another plus point is, unlike OpenAI or Anthropic, DeepSeek operates without external investors, allowing it to focus purely on research rather than driven by profit.

Wenfeng is now China’s version of Sam Altman. 

Why DeepSeek AI Is Different

DeepSeek’s biggest strength lies in its efficiency and accessibility.

DeepSeek’s strategy was different from the start. 

Unlike U.S. based models that are racing to acquire powerful Nvidia chips, Liang focused on training his AI by optimizing the most out of limited resources. 

DeepSeek claims to match and in some cases even outperform industry leaders like OpenAI’s GPT-4, Google’s Gemini, and Meta’s Llama while running on significantly fewer resources. 

In an industry where bigger is often considered better, this David vs. Goliath moment is forcing tech leaders to rethink the future of AI.

Here are the key reasons why DeepSeek is making headlines:

• DeepSeek R1 was trained on Nvidia H800 chips for just $6 million, which are available in China but are not the most advanced chips on the market

• Unlike OpenAI and Anthropic, which are closed-source models, DeepSeek open-sourced its AI, allowing any developer to build on its technology
• The open source approach has made it one of the most widely adopted AI models in the open-source community
• While many advanced AI models struggle with complex math and logic problems, DeepSeek’s R1 model showed strong reasoning capabilities
• It uses chain-of-thought training, similar to OpenAI’s GPT-4o, allowing it to explain its reasoning step by step rather than just providing answers
• If a Chinese startup can disrupt AI development so drastically, what does it mean for the future of U.S. tech dominance 

BTW, DeepSeek didn’t start as your typical Silicon Valley-style startup. It emerged from Liang’s own quantitative hedge funds company High-Flyer. 

High-Flyer leveraged AI for stock trading, using massive GPU clusters to analyze financial markets and often made scarily accurate market predictions. At its peak, the firm managed over $15 billion—making it one of China’s most successful quant hedge funds. 

In March 2023, High-Flyer announced its “starting again”—moving beyond trading to explore the essence of artificial general intelligence (AGI).

Later that year, Liang spun off DeepSeek AI as an independent research company, fully funded by High-Flyer.

It was a risky move indeed. Unlike OpenAI or Google DeepMind, which secured billions in venture capital, DeepSeek was funded solely by hedge fund profits. Liang was betting everything on AGI research.

“I wouldn’t be able to find a commercial reason [for founding DeepSeek] even if you ask me to. Because it’s not worth it commercially.” – Liang Wenfeng

DeepSeek didn’t follow the traditional AI startup playbook either. Instead of hiring seasoned engineers from Big Techs, Liang recruited PhD students from China’s top universities, including Peking University and Tsinghua University.

Why young researchers?

• Liang believed that early-career researchers were more willing to pursue bold, high-risk ideas and less constrained by corporate bureaucracy
• Most of DeepSeek’s core team consists of graduates from the past 1-2 years, mostly people who had published in top AI journals but lacked industry experience
• Unlike traditional tech companies where teams compete for resources, DeepSeek’s introduced a collaborative culture, giving its researchers unlimited computing power

This unique approach allowed DeepSeek to rapidly iterate and experiment—leading to its breakthrough DeepSeek-R1 model.

An AI ‘Sputnik Moment’ or Just Hype?

Venture capitalist Marc Andreessen called DeepSeek AI “AI’s Sputnik moment”, comparing it to the time when the Soviet Union launched the first satellite, triggering a race with the US for technological supremacy. 

Some experts argue that DeepSeek’s launch was politically timed, similar to Huawei’s smartphone release amid U.S.-China trade tensions in 2023.

But many believe it’s just another AI fad fueled by speculation.

Regardless of which side you’re on, one thing is clear: DeepSeek AI has changed the game, and its impact on AI, finance, and global tech competition is just beginning.

Let’s take a look at how the western industry is responding: 

• The U.S. has restricted AI chip sales to China, aiming to slow its progress
• The Trump 2.0 administration recently announced a $500B AI investment plan (Stargate) with OpenAI, Oracle, and SoftBank that can be seen as a direct response to China’s AI advances

However, DeepSeek’s success shows China can build powerful AI models even with limited hardware, raising doubts about the effectiveness of these bans.

DeepSeek R1: The AI Model That’s Changing the Game

DeepSeek’s latest model R1 has sent shockwaves through the AI world with its superior capabilities: 

• Smarter Model Architecture: DeepSeek optimized multi-head latent attention (MLA) and Mixture-of-Experts (MoE) to reduce computing power usage without sacrificing accuracy
• Custom Communication Between Chips: Instead of blindly increasing GPU power, DeepSeek refined how AI nodes communicate, making computations faster and more efficient
• Memory Optimization: By shrinking unnecessary parameters, DeepSeek R1 reduces memory usage, making it more efficient than rivals
• Leveraging Open-Source AI: DeepSeek openly shared its innovations, encouraging global researchers to build on its breakthroughs—a key strategy that’s helping China catch up with Western AI labs

But, DeepSeek isn’t just about one breakthrough model. It’s developing a suite of AI tools, including:

• DeepSeek-V3 – A 671B parameter model with impressive benchmarks and efficient performance
• DeepSeek-R1-Distill – Lighter, more efficient AI models, fine-tuned from existing models like Llama and Qwen
• Custom AI tools for reasoning and logic, making DeepSeek a strong contender for coding, research, and education

What can DeepSeek AI Do?

Writing: A Capable AI Wordsmith

AI-powered writing tools have become indispensable. They help you draft emails, articles, and creative stories. While both DeepSeek and ChatGPT shine in this area, there are some differences. Her are the tasks we assigned and the results: 

We tried writing this blog’s intro section with ChatGPT 4o, DeepSeek R1 and Alibaba’s latest Qwen 2.5 Max by giving them the same set of instructions and ran it through known AI content detectors like GPTZero and Quillbot. The result? 

ChatGPT output was labeled 70% AI generated by GPTZero and 63% by Quillbot. 

Both DeepSeek and Qwen’s output returned a 100% AI generated content probability on both detector tools.

Coding: Can DeepSeek Rival ChatGPT for Developers

Another one of AI’s most used applications is coding, assisting programmers in debugging, optimizing, and even writing entire scripts.

Javier Aguirre, AI researcher at Samsung Medical Center, tested DeepSeek on a complex coding problem that even ChatGPT o1 couldn’t solve—but DeepSeek handled it effortlessly.

Google’s Addy Osmani noted that DeepSeek was “significantly cheaper” than ChatGPT and Claude Sonnet for coding tasks.

In another instance, both ChatGPT and DeepSeek were given a task to write a code to create a rotating triangle with a ball moving in a circular path inside the triangle, staying aligned with the rotation. 

GPT failed the task with the ball often coming outside of the triangle whereas DeepSeek created a perfect example adhering to the instructions. 

Trend shows developers are increasingly combining DeepSeek with other AI models (e.g., Claude Sonnet) for better results.

Idea Brainstorming: Creative, Yet Different Approaches

AI has proven itself to be an incredible brainstorming partner. But DeepSeek’s and ChatGPT’s approach is different.

Both models were given a task for generating ideas for a Children’s story about a boy who lived on the moon. 

ChatGPT provided six different ideas, each neatly summarized and DeepSeek wrote an entire story instead.

This implies ChatGPT is more suited if you just want the ideas. DeepSeek on the other hand, jumps straight into execution.

Learning & Research: Simplified vs. In-Depth Explanations

AI chatbots summarize complex topics in seconds greatly helping in education and research. 

We asked ChatGPT, Google Gemini and DeepSeek to explain the causes of World War 2. 

ChatGPT provided a detailed, structured answer covering key events and players. Gemini did the same but included external reputable sources for citation.

DeepSeek crafted a rather shorter, more concise summary but managed to capture the main points.

This shows ChatGPT or Gemini might be better for in-depth research. For quicker, digestible summaries, DeepSeek does it more efficiently.

Final Verdict: Is DeepSeek Better Than ChatGPT?

DeepSeek AI shines in cost-efficiency, coding, and reasoning but ChatGPT still leads in writing, enterprise trust, and general adoption.

Who Should Use DeepSeek AI?	Who Should Stick to ChatGPT?
Developers & researchers needing open-source AI.	Businesses needing enterprise security & global support.
Startups looking for low-cost AI alternatives.	Writers & content creators who need high-quality text generation.
Users who prioritize coding & logic tasks.

How DeepSeek AI Achieved More with Less

DeepSeek has proven that bigger budgets don’t always mean better results. 

How did they do it? 

By prioritizing efficiency over brute force. Here’s a breakdown of DeepSeek’s low-cost, high-performance approach.

Reinforcement Learning: Teaching AI to Teach Itself

Unlike traditional AI models that rely on massive datasets and supervised learning, DeepSeek AI uses pure reinforcement learning—allowing its models to learn through trial and error rather than being hand-fed labeled data.

✔️ Reduces dependence on expensive, high-quality datasets
✔️ Improves AI’s reasoning ability by self-correcting over time
✔️ Requires significantly less computing power 

Mixture-of-Experts (MoE): Smarter, Not Harder

DeepSeek employs a Mixture-of-Experts (MoE) architecture, which activates only the necessary parts of the model during any given task.

Think of it like this, instead of making the entire model work on a task, DeepSeek’s AI calls upon specialized “expert” nodes for different problems—like coding, writing, or logical reasoning.

✔️ Reduces energy and processing costs
✔️ Allows DeepSeek models to perform as well as larger models with fewer parameters
✔️ Improves speed and efficiency 

Multi-Head Latent Attention: Sharper Focus, Smarter AI

DeepSeek-V3 integrates multi-head latent attention, enabling it to process complex queries more efficiently.

Imagine an AI with multiple eyes, each focusing on different aspects of a question. Instead of treating every input equally, DeepSeek prioritizes the most relevant information, improving accuracy and response time.

✔️ Enables faster, more nuanced responses to user queries
✔️ Improves the model’s ability to handle multiple variables simultaneously

Distillation: Shrinking AI Without Losing Intelligence

DeepSeek mastered distillation, a process where a large, powerful AI model trains a smaller, more efficient model to achieve similar performance.

Think of it like passing knowledge from a professor to a student—the student doesn’t need to memorize every single fact but understands the key concepts well enough to perform independently.

• Allows AI models to run efficiently on smaller devices
• Expands AI accessibility to businesses who can’t afford high-end AI hardware for precision tasking such as competitor analysis and business intelligence

DeepSeek didn’t just build a more efficient model—it also changed the economics of AI development.

With these innovative methods, DeepSeek managed to train its DeepSeek-V3 model for a fraction of the cost of Meta’s Llama 3.1 and OpenAI’s GPT-4. This also reflects in their affordable pricing: 

• DeepSeek-R1 API: $0.55 (input), $2.19 (output)
• OpenAI API (GPT-4o): $15 (input), $60 (output)

DeepSeek’s Impact on the AI Market

DeepSeek AI’s meteoric rise sent a seismic ripple in the global AI landscape. 

Its efficiency challenges Silicon Valley’s spending spree, as investors question whether U.S. AI companies are overspending

France’s Élysée Palace called DeepSeek proof that agile companies with smart techniques can compete in AI, even without vast resources.

Meta reportedly created four internal “war rooms” to analyze DeepSeek’s impact on generative AI.

Microsoft CEO Satya Nadella compared DeepSeek’s rise to the Jevons paradox—suggesting that as AI gets cheaper, its use will skyrocket.

Scale AI CEO Alexandr Wang called DeepSeek’s R1 model “earth-shattering,” signaling a major shift toward more efficient, cost-effective AI development.

AI giants like OpenAI, Google, and Meta now face pressure to cut costs and justify their billion-dollar spending on AI models.

However, some investors believe that the market overreacted, arguing that DeepSeek’s models still require huge infrastructure for large-scale deployment.

DeepSeek AI vs. Nvidia: A Threat to AI Chip Dominance?

For years, Nvidia has remained the undisputed leader in AI chip technology. Those GPUs are crucial to power almost every major AI model, from OpenAI’s GPT-4 to Google’s Gemini.

But after DeepSeek revealed its R1 model was trained on just 2,000 Nvidia H800 GPUs, investors started questioning Nvidia’s long-term dominance and whether AI firms really require massive amounts of Nvidia GPUs.

Such speculations led to a panic mode among traders and a mass sell-off followed. As a result, the company’s market value plummeted by 12%, taking nearly $600 billion hit. 

A domino effect ensued and the world saw a global stock market dip. 

The development has sparked debates on whether Nvidia’s grip on AI infrastructure is really as unshakable as once thought.

In its official statement, Nvidia acknowledged DeepSeek’s achievements, calling them an “excellent AI advancement” that showcases efficient model scaling. But quickly pointed out that AI inference (running models in real-time).

In clear English, training may get cheaper, but AI companies still need Nvidia’s hardware to deploy these models at scale.

Can Western AI Companies Keep Up?

The short answer? 

Yes. 

But they’ll need to adapt fast. 

OpenAI and Google still have one major advantage: scalability. 

While DeepSeek’s AI is impressive, deploying AI models at scale requires more than just efficient training. It demands robust infrastructure, enterprise-grade security, and seamless integration into global markets.

However, DeepSeek’s open-source model means that its innovations won’t remain exclusive for long. 

Companies like OpenAI, Google, and Meta will likely incorporate its efficiency techniques into their own models, just as they have done previously.

In a sense, DeepSeek may have just accelerated the AI arms race. 

Instead of dethroning the West’s AI giants, it has forced them to think smarter, move faster, and optimize their models in ways they hadn’t considered before.

Is DeepSeek AI Available in the U.S.?

Yes—but there are some roadblocks.

DeepSeek AI’s assistant is available in the Apple App Store and can be accessed via its website. 

However, due to massive demand and alleged cyberattacks, new user registrations were temporarily limited. Existing users were able to log in as usual though.

DeepSeek’s Strategic Partnerships 

DeepSeek’s rise isn’t just about its own innovation. They made a strategic alliance with AMD, another known leading provider of high-performance computing solutions. 

DeepSeek uses AMD’s open-source AI hardware & software stack such as Instinct GPUs & ROCm software to develop models like DeepSeek-V3.

By leveraging both Nvidia and AMD technologies, DeepSeek avoided over-reliance on a single supplier, making its AI infrastructure more flexible and cost-effective.

This multi-vendor strategy maintains supply chain stability while keeping costs down. 

Truly, DeepSeek has positioned itself as a forward thinker. 

Hugging Face’s Open R1: A DeepSeek Clone

As expected, DeepSeek’s open-source approach inspired a wave of replication. 

Hugging Face is leading the charge with their Open R1, an ambitious project aiming to fully recreate DeepSeek-R1’s development pipeline.

Here ‘s what they are doing: 

1. Reverse-engineering DeepSeek-R1’s training methods to make AI reasoning models more accessible
2. Replicating DeepSeek’s reinforcement learning pipeline
3. Providing open-source scripts for training & evaluation, allowing researchers and developers to build their own versions of DeepSeek AI

If successful, Open R1 could democratize AI further, further challenging proprietary AI models.

But… Is DeepSeek AI Safe?

For all its breakthroughs, DeepSeek AI raises some serious privacy and security concerns—especially for users outside of China.

1. Data Collection: How Much Does DeepSeek Know About You?

Like all large language models (LLMs), DeepSeek gathers massive amounts of data to train and improve its AI. But its data collection practices extend beyond what many Western AI companies disclose.

According to DeepSeek’s privacy policy, it collects:

• Personal details: Name, email, date of birth, phone number.
• User activity: Chat history, text/audio input, uploaded files.
• Device information: IP address, operating system, keystroke patterns (which are as unique as fingerprints).
• External data sources: Information from advertisers, social media sign-ons (Google, Apple), and third-party analytics tools.

And here comes the main blow—this data is stored on servers in China, which, under the Chinese cybersecurity laws, must be accessible to the government authorities upon request.

This starkly differs from U.S. privacy laws, where federal agencies typically require a warrant to access data from American tech firms. 

In other words, if you’re using DeepSeek AI, you should assume your data could be accessed by the Chinese government at any given time, whether you live in or outside of China.

2. Censorship & Political Bias

“Sorry, I’m not sure how to approach this type of question yet.”

“Sorry, that’s beyond my current scope. Let’s talk about something else.”

That’s the response you get if you ask DeepSeek about topics that are rather sensitive to its country of origin.

Many experiments have clearly demonstrated DeepSeek AI follows a content censorship, particularly around topics sensitive to the Chinese Communist Party (CCP).

This has sparked many memes trolling the AI model for its bias. 

Go ahead and try giving prompts regarding any of these examples:

• Tiananmen Square Massacre (1989): Initially begins answering, then self-deletes the response and redirects to a different topic
• Hong Kong Protests (2019): Gives detailed responses at first, but then erases its answer and refuses to engage further
• Chinese Leadership & Policies: Presents a rather state-approved narrative and avoids controversial discussions
• Indo-China Border Disputes: Starts answering, then deletes response mid-way and refuses to respond on the topic

But this isn’t entirely unusual! 

Even U.S. based AI models like ChatGPT have shown certain content restrictions. 

However, DeepSeek’s alignment with Chinese state narratives raises concerns about how AI could shape global discourse and suppress dissenting perspectives.

3. Security Risks: The Next TikTok Controversy?

DeepSeek’s privacy concerns mirror those raised about TikTok, another Chinese-owned platform accused of posing national security risks. 

In 2024, the U.S. passed a law requiring TikTok’s parent company, ByteDance, to divest its American operations. 

The White House has already begun investigating DeepSeek AI for potential national security implications. If the U.S. government views it as a risk, similar regulatory action could follow, impacting DeepSeek’s availability in Western markets.

Can You Use DeepSeek AI Without Sending Your Data to China?

If you’re concerned about privacy but still want to explore DeepSeek’s capabilities, there are a few ways to minimize risk:

• Use a throwaway email or register with a separate email address not linked to your other accounts
• Avoid sharing sensitive data, financial information, or trade secrets in your queries
• Some users have downloaded DeepSeek’s open-source models and run them offline—bypassing Chinese servers entirely
• Encrypt your connection to prevent tracking and data collection

Challenges Ahead 

Despite its rapid rise, DeepSeek faces several hurdles on its path to long-term success.

Compute Limitations

DeepSeek lacks access to cutting-edge Nvidia AI chips due to U.S. export restrictions, forcing it to rely on less powerful hardware. 

While it has optimized its models for efficiency, this hardware gap could hinder its ability to scale and compete with AI giants in the long run.

Trust and Market Perception

Unlike established AI leaders, DeepSeek is still a relatively unknown brand outside China. 

Concerns over data privacy, security, and long-term reliability may deter global businesses from adopting its models.

Intense Competition

The AI race is evolving rapidly. 

Tech giants can (and will) adopt DeepSeek’s efficiency techniques, improve on them, and leverage superior resources to stay ahead.

Censorship Concerns

Being a Chinese company, DeepSeek remains subject to government censorship. This raises several concerns about bias and free speech. 

How AI Assistants Like DeepSeek are Changing the Global Workforce

AI assistants are no longer just tools—they’re evolving into autonomous digital employees. 

The rise of advanced “agentic AI” tools like OpenAI Operator, is reshaping industries and redefining the roles of human workers.

Think of it this way: 

Traditional large language models (LLMs) like ChatGPT or DeepSeek generate responses based on input—like a chef writing a recipe. 

AI agents, however, go a step further—they follow the recipe, cook the meal, and even serve it. 

This automation of decision-making and execution is what’s fueling both excitement and concern across global job markets.

At the Davos 2025 conference, Nvidia CEO Jensen Huang declared, “The age of agentic AI is here.” Meta’s Mark Zuckerberg went even further, predicting that by 2025, AI agents could perform tasks at the level of mid level engineers.

What does this mean for workers worldwide?

The Good News

• Businesses can automate tasks without sacrificing accuracy, allowing them to scale more efficiently
• Rather than outright replacing employees, AI agents can assist in handling repetitive, time-consuming work, allowing workers to focus on higher-value tasks
• The AI-driven workforce still needs specialists to train, oversee, and optimize agents, leading to new career paths in AI operations, ethics, and model fine-tuning

The Bad News 

• Industries like customer service, data entry, and even software development are seeing AI take over tasks traditionally performed by humans
• Companies adopting AI need workers with expertise in AI management, cybersecurity, and digital literacy—which not everyone currently possesses
• Developing economies relying on outsourced digital labor may face increased unemployment as AI takes over remote work

Final Takeaway

DeepSeek AI’s rise is no accident! It’s a testament to smart innovation and efficiency. 

But the bigger question is: will its open-source model democratize AI development, or will it simply hand Silicon Valley the tools to leap ahead? 

As investors and tech leaders recalibrate their strategies, DeepSeek’s efficiency-first approach could redefine the economics of AI.

One thing is clear: DeepSeek has already changed the game. It’s proof that innovation doesn’t always require billions—just bold thinking and adaptability.

The AI race is no longer about who has the deepest pockets; it’s about who can adapt the fastest. Whether DeepSeek becomes a global powerhouse or a catalyst for evolution, its impact is undeniable.

The world is watching. 

And if there’s one lesson to learn, it’s this: in the AI game, the next disruptor could come from anywhere. 

Are you ready?