Summary: DeepSeek has quickly positioned itself as a serious alternative to proprietary AI models like ChatGPT by offering an open-source, cost-efficient approach. Its strengths in technical tasks and accessibility make it appealing to researchers and developers, but concerns about censorship and data privacy remain significant hurdles. As competition in the AI space grows, DeepSeek’s impact will depend on how well it can balance openness, affordability, and trust in a rapidly evolving industry.
Ever since the release of ChatGPT, it feels like new AI models are hitting the market nonstop, each claiming to be the next big thing in artificial intelligence. If you’ve been following AI news recently, you’ve probably seen the newest in the line-up, DeepSeek, making headlines—but for once, this isn’t just another overhyped release. It turns out that DeepSeek is a legitimate challenge to the dominance of proprietary AI systems like ChatGPT. By offering a fully open-source large language model (LLM) with dramatically lower operational costs, DeepSeek is shifting the balance of power in AI development. It’s giving independent researchers, startups, and enterprises the kind of access that’s typically locked behind closed corporate walls, making high-performance AI more customizable and widely available than ever before.
So why should you care? Well, unlike ChatGPT, which is closed off and controlled by OpenAI, DeepSeek is open-source. That means developers, researchers, and even curious users like you can explore and modify it. It also has some unique features that make it stand out in the AI world. But of course, there are downsides too. In this article, you’ll learn where DeepSeek came from, how it works, and whether it’s truly a competitor to ChatGPT—or just another alternative with its own niche.
Overview and Origins
Before we dive into how DeepSeek works, let’s take a moment to look at where it comes from. Understanding its origins helps you see why it was built the way it was and what its creators had in mind.
Founding and Vision
DeepSeek was founded in 2023 by Liang Wenfeng, a Chinese entrepreneur with a background in quantitative trading and AI development. If you’re not familiar with that world, quantitative trading involves using complex algorithms and data analysis to make financial decisions—skills that transfer surprisingly well into AI development. Wenfeng, along with financial backing from the hedge fund High-Flyer, saw an opportunity to disrupt the AI landscape by making powerful models more accessible.
The goal? To “democratize” artificial intelligence. If you’ve followed AI developments, you know that most cutting-edge models—like OpenAI’s GPT-4—are locked behind paywalls or controlled by large tech companies. DeepSeek set out to change that by offering high-performance AI as open-source technology. That means researchers, developers, and even companies can use and improve it without relying on a single company’s infrastructure.
Key Facts About DeepSeek’s Founding:
- Established in 2023 by Liang Wenfeng, backed by hedge fund High-Flyer
- Originated in China with a focus on democratizing AI
- Built with cost-efficiency in mind, challenging the high-cost model of Western AI companies
Development Philosophy
So, what makes DeepSeek different from other AI models? The biggest thing is its commitment to being open-source. Unlike ChatGPT, which operates as a closed system (meaning no one outside of OpenAI knows exactly how it was built), DeepSeek allows people to access and modify its code.
Why does that matter? Imagine you’re working on a project and you need a specific AI feature that doesn’t quite exist yet. With DeepSeek, you (or a developer on your team) could tweak the model to fit your needs. ChatGPT, on the other hand, doesn’t give you that option—you get what OpenAI provides, and that’s it.
Another major difference is efficiency. DeepSeek is designed to run on fewer computing resources while still delivering high-quality responses. Training AI models is expensive—OpenAI’s models reportedly require tens of thousands of high-end GPUs, with costs reaching hundreds of millions of dollars. DeepSeek, on the other hand, was trained on just about 2,000 Nvidia H800 GPUs at a cost of around $5–6 million. That’s a huge difference and shows why DeepSeek is gaining attention as a more cost-effective alternative.
Comparison with Western AI Initiatives
If you compare DeepSeek with Western AI companies like OpenAI or Google DeepMind, you’ll notice a major divide in their approach. Western AI companies have largely focused on keeping their models proprietary. This means they maintain full control over the model’s capabilities, updates, and how it’s used.
DeepSeek challenges this model by making AI more accessible. Think of it like this: OpenAI’s approach is similar to Apple’s ecosystem—highly polished, tightly controlled, and designed to keep users within its system. DeepSeek is more like an open-source operating system such as Linux—flexible, customizable, and available for anyone to build upon. This makes it especially appealing to researchers, businesses, and even governments looking for more control over their AI tools.
Of course, there are trade-offs. While DeepSeek’s open-source nature is a big plus for customization, it also raises concerns about regulation and misuse. OpenAI and Google argue that keeping models closed helps prevent AI from being used irresponsibly, while DeepSeek takes the stance that openness leads to better innovation and collaboration.
DeepSeek vs. OpenAI: Key Differences
- Accessibility: DeepSeek is open-source, while OpenAI keeps its top models proprietary
- Customization: Developers can fine-tune DeepSeek; OpenAI limits external modifications
- Computational Cost: DeepSeek runs on fewer GPUs, making it more cost-effective
- Transparency: DeepSeek allows code inspection, while OpenAI’s architecture remains private
Technical and Architectural Differences
Now that you know where DeepSeek comes from, let’s look at what makes it different under the hood. Every AI model has its own design choices that impact how it performs, how much it costs to run, and what kinds of tasks it’s best suited for. DeepSeek has made some unique technical decisions that set it apart from ChatGPT and other AI models, particularly in terms of efficiency, transparency, and accessibility.
Technical Innovations in DeepSeek:
- Mixture-of-Experts (MoE): Optimizes computing power, activating only necessary parts of the model
- Multi-head Latent Attention (MLA): Reduces memory and computation load
- Visible Chain-of-Thought: Displays reasoning steps, enhancing user trust and learning
Efficient, Low-Cost Training
One of the biggest challenges with AI development is the cost of training large models. You might not think about it when you use ChatGPT, but running an AI model at this level takes enormous computing power. OpenAI’s top models, for example, require tens of thousands of high-end GPUs and hundreds of millions of dollars to train. That’s a massive investment that only the biggest tech companies can afford.
DeepSeek, on the other hand, has found a way to train its models with significantly fewer resources. Reports suggest it used around 2,000 Nvidia H800 GPUs at a cost of about $5–6 million to train its DeepSeek-R1 model. That’s still a huge investment, but compared to the costs of models like GPT-4, it’s a fraction of the price.
How does DeepSeek manage this? It uses techniques like the Mixture-of-Experts (MoE) approach and Multi-head Latent Attention (MLA) to optimize memory use and reduce the number of computations needed per response. Think of it like a group project—rather than having every member work on the entire task, each person specializes in one part, making the whole process more efficient. DeepSeek applies this idea at a technical level, activating only the necessary parts of its model at any given time.
Visible Chain-of-Thought
Another key difference between DeepSeek and ChatGPT is how they handle reasoning. If you’ve ever used ChatGPT, you know that it generates responses in a way that seems almost magical—there’s no indication of how it’s reaching its conclusions. You type a question, and it spits out an answer, but you don’t see any of the reasoning behind it.
DeepSeek takes a different approach by incorporating a visible chain-of-thought. This means it sometimes displays parts of its internal reasoning while answering a question. For example, if you ask it a complex math problem or a logical puzzle, you might see a breakdown of the steps it’s following before it gives you the final answer.
Why does this matter? First, it helps build trust. When you can see how an AI is thinking, you can better understand whether its answer makes sense. Second, it makes DeepSeek a great tool for learning. If you’re trying to improve your coding, math, or logical reasoning skills, seeing the thought process behind an answer can be incredibly useful.
Open-Source Versus Closed-Source Approach
One of the biggest debates in AI right now is whether models should be open-source or closed-source. OpenAI has kept its top-tier models, like GPT-4, completely closed—meaning no one outside of the company knows exactly how they work or what data they were trained on. This approach is meant to prevent misuse, but it also limits transparency and makes it harder for developers to build on OpenAI’s technology.
DeepSeek, on the other hand, is fully open-source. That means its code, model architecture, and in many cases, its training data are available for anyone to inspect and modify. If you’re a developer or a researcher, this is a big deal because it gives you more control over how the AI works. You can fine-tune it for specific applications, customize its responses, or even use it as a foundation for building new AI tools.
However, there are downsides to this approach. Open-source models can be more easily manipulated or used in unintended ways. For example, someone could modify DeepSeek to remove safety filters, allowing it to generate harmful or misleading content. This is one reason why companies like OpenAI argue that keeping models closed is the safer option.
Performance and Use Cases
Now that you know how DeepSeek is built, let’s talk about what really matters—how well it performs and what you can actually use it for. Every AI model has strengths and weaknesses depending on how it’s trained and optimized. DeepSeek is no different. While it excels in some areas, there are also places where it still lags behind competitors like ChatGPT. Whether you’re a developer, a researcher, or just someone curious about AI, understanding its real-world performance can help you see where it fits in.
Strengths in Technical Tasks
One of DeepSeek’s biggest strengths is its ability to handle highly structured, technical tasks. This includes things like coding, mathematics, and logical reasoning. If you’ve ever asked ChatGPT to debug some code or solve a tricky math problem, you know that AI can sometimes get things wrong or miss important details. DeepSeek aims to improve on that by optimizing its model specifically for precision-based tasks.
For example, if you give DeepSeek a complex coding problem, it’s more likely to break down the logic step by step and produce an accurate solution. This is partly because of its visible chain-of-thought reasoning, which helps it work through problems in a more structured way. Think of it like showing your work in math class—seeing each step helps catch mistakes before they lead to a wrong answer.
Benchmarks suggest that DeepSeek performs comparably to OpenAI’s top-tier models when it comes to logic-based tasks. This makes it particularly useful for programmers, engineers, and researchers who need highly reliable AI-generated outputs.
User Experience and Interface
If you’ve used ChatGPT before, you’re probably familiar with how smooth and interactive its interface is. OpenAI has spent years refining the user experience, adding features like voice interaction, image generation, and chat history. DeepSeek, on the other hand, keeps things much simpler.
Right now, DeepSeek’s interface is minimalistic and text-focused—similar to the early versions of ChatGPT. You type in a question, it gives you an answer, and that’s about it. There are no flashy extras like voice mode, interactive response editing, or image-based AI tools.
This simplicity can be a pro or a con, depending on what you’re looking for. If you just need a clean, straightforward AI tool to answer questions or help with coding, DeepSeek works just fine. But if you’re someone who enjoys the richer, more interactive experience of ChatGPT, you might find DeepSeek a little underwhelming for now.
Cost Efficiency and Market Disruption
One of the biggest reasons DeepSeek is gaining attention is its incredibly low operating costs. AI models are expensive to train and run, and companies like OpenAI and Google spend millions keeping their systems up and running. DeepSeek, however, has optimized its model in a way that reduces the cost per token by as much as 96% compared to OpenAI’s models.
This is a huge deal. Imagine running a business that relies on AI—whether it’s customer service chatbots, automated coding tools, or AI-powered analytics. If DeepSeek can provide similar results at a fraction of the cost, it could change the economics of AI adoption for businesses worldwide.
This efficiency has already had an impact. When DeepSeek launched, it reportedly caused a massive market shake-up, leading to a sharp drop in the stock prices of companies like Nvidia, which sells the expensive GPUs used for AI training. Investors saw DeepSeek’s low-cost model as a potential threat to the high-budget AI industry in the West.
Where DeepSeek Excels:
✔ Coding and debugging assistance
✔ Solving mathematical problems with step-by-step reasoning
✔ Logical reasoning and structured problem-solving
✔ Cost-effective deployment for AI-powered businesses
Where It Falls Short:
✖ Lacks advanced interactive features like voice mode or image generation
✖ Interface is minimalistic compared to ChatGPT
✖ Performance in casual conversations and creative writing is still developing
Market Impact and Controversies
By offering a powerful, open-source alternative to ChatGPT at a much lower cost, Deepseek is forcing major players in the industry to take notice. But with its rapid rise comes controversy, especially regarding censorship and data privacy. Whether you see DeepSeek as an exciting innovation or a cause for concern, it’s already having a major impact on the AI landscape.
DeepSeek’s Market Influence:
- AI cost reduction could disrupt existing business models
- Open-source nature is attracting independent developers and enterprises
- U.S. tech companies face new competition from China in AI innovation
Major Concerns:
- Censorship: DeepSeek follows China’s strict content moderation policies
- Data Privacy: User data is stored on Chinese servers, raising security concerns
- Regulatory Scrutiny: Governments and organizations are cautious about adopting AI with potential state influence
Wall Street and Global AI Competition
When DeepSeek launched, it sent shockwaves through the tech world. Investors, particularly those in the U.S., quickly realized that a cost-efficient AI model from China could disrupt the balance of power in the AI industry. Almost immediately, shares of Nvidia—a key supplier of GPUs for AI training—took a steep dive, dropping around 17–18%. The reason? DeepSeek’s efficiency meant that companies wouldn’t need to buy as much expensive hardware to train and run AI models.
This wasn’t just a minor market fluctuation; it was a $1 trillion shift in global tech stocks. Analysts compared it to a “Sputnik moment” for AI—a reference to the Cold War-era shock when the Soviet Union launched the first satellite, spurring the U.S. to accelerate its own space program. In other words, DeepSeek’s emergence signaled a major challenge to Western AI dominance.
But does this mean OpenAI, Google, and other Western companies should be worried? Not necessarily. While DeepSeek’s low-cost model is appealing, it still has a long way to go in terms of features, user experience, and broader adoption outside China. However, it’s clear that competition is heating up, and DeepSeek is now a serious contender in the AI race.
Censorship and Political Constraints
One of the biggest concerns surrounding DeepSeek is how it handles politically sensitive topics. Since it’s developed in China, it must comply with strict government regulations on content moderation. This means that certain discussions—such as topics related to Tiananmen Square, Taiwan, or government criticism—are either heavily filtered or avoided altogether.
If you’re used to ChatGPT’s responses, this might be a noticeable difference. OpenAI models do have content moderation, but they generally allow discussions on politically sensitive issues as long as they stay within ethical guidelines. DeepSeek, however, follows China’s strict censorship rules, which could be a deal-breaker if you need an AI that provides uncensored information.
This raises an important question: Can an AI truly be open-source if it’s controlled by government regulations? While DeepSeek gives developers access to its underlying model, it still operates under a system that restricts what it can say. For users who prioritize free expression, this is a major drawback.
Data Privacy and Security Concerns
Another controversy surrounding DeepSeek is how it handles user data. Unlike OpenAI, which operates under Western privacy laws like GDPR (General Data Protection Regulation), DeepSeek stores user data on servers in China. This has led to concerns that personal data and conversations could be accessed or monitored by the government.
For businesses and researchers, this is a significant issue. Many companies, especially those dealing with sensitive or proprietary information, are wary of using AI models that don’t guarantee strong privacy protections. In fact, some governments and corporations have outright banned the use of Chinese-developed software due to cybersecurity concerns.
On the other hand, DeepSeek’s supporters argue that data privacy concerns exist across all AI models, not just those from China. After all, OpenAI and other Western AI companies also collect and store user interactions to improve their models. The difference is that OpenAI operates under stricter transparency requirements, whereas DeepSeek’s policies are less clear.
DeepSeek’s Growing Role in the AI Landscape
DeepSeek is proving that AI doesn’t have to be expensive or locked behind closed doors. With its open-source approach and cost-efficient design, it’s offering an alternative to models like ChatGPT. But while its strengths in technical tasks and affordability make it appealing, concerns around censorship and data privacy can’t be ignored. Whether DeepSeek becomes a true global competitor or remains a regional powerhouse depends on how it evolves. Either way, it’s already making an impact, pushing the AI industry to rethink accessibility, transparency, and efficiency in ways that could shape the future of artificial intelligence.
