DeepSeek And Its Impact On The Generative AI Global Race via @sejournal, @AlliBerry3

Since launching to the public on Jan. 20, 2025, Chinese startup DeepSeek’s open-source AI-powered chatbot has taken the tech world by storm.

As the top free app by downloads in the U.S. Apple app store since Jan. 26 – with 16 million app downloads in its first 18 days (ChatGPT had 9 million in the same timeframe) – DeepSeek’s performance and accompanying search feature is at least on par with OpenAI’s ChatGPT for a fraction of the cost.

Its launch led U.S.-based AI technology company, Nvidia, to the greatest drop in market value for a U.S. company in U.S. stock market history. That’s quite an entrance!

U.S. tech analysts and investors seem to all fear that the U.S. is falling behind in the generative AI global race.

This may be warranted considering how quickly and cost-effectively DeepSeek was able to get R1 developed and out the door.

DeepSeek utilizes reinforcement learning, meaning the model learns complex reasoning behaviors through reinforcement without supervised fine-tuning, which allows it to save significant computational resources.

But, is DeepSeek really going to emerge as the leader in AI? And what are the implications for this development for the future of search? Let’s dive in.

What Has Happened Since DeepSeek Launched?

While U.S. tech companies were humbled by the speed and claimed cost efficiency of this launch, DeepSeek’s arrival has not been without controversy.

A lot of questions lurk, ranging from suspected intellectual property violations to security, data privacy, Chinese censorship, and the true cost of its technology.

Legal Issues For Copyright And Data Protection

OpenAI and Microsoft are investigating whether DeepSeek used OpenAI’s API to integrate their AI models into DeepSeek’s own models.

OpenAI claims it has evidence of DeepSeek distilling the outputs of OpenAI to build a rival model, which is against OpenAI’s terms of service, but likely not against the law.

Distillation allows for the transfer of knowledge of a large pre-trained model into a smaller model, which enables the smaller model to achieve comparable performance to the large one while reducing costs.

This is more than a little ironic given the lawsuits against OpenAI for ignoring other site’s terms of service and using their copyrighted internet data to train its systems.

There are also questions about where user data is stored and how it is processed, given that DeepSeek is a Chinese-based startup.

For anyone handling customer information and payment details, integrating a tool like DeepSeek that stores data in a foreign jurisdiction could violate data protection laws and expose sensitive information to unauthorized access.

Given that DeepSeek has yet to provide its privacy policies, industry experts and security researchers advise using extreme caution with sensitive information in DeepSeek.

DeepSeek Security Breach

Wiz Research, a company specializing in cloud security, announced it was able to hack DeepSeek and expose security risks with relative ease on Jan. 29.

It found a publicly accessible database belonging to DeepSeek, which allowed it full control over database operations and access to user data and API keys.

Wiz alerted the DeepSeek team, and they took immediate action to secure the data. However, it is unclear who else accessed or downloaded the data before it was secured.

While it’s not uncommon for startups to move fast and make mistakes, this is a particularly large mistake and shows DeepSeek’s lack of focus on cybersecurity so far.

National Security Concerns Similar To TikTok

There are national security concerns about DeepSeek’s data collection policies reminiscent of fears about TikTok, which saw a similar rise in global prominence out of Chinese-based company ByteDance.

The U.S. government briefly banned TikTok in January 2025, which came out of concerns about how the company was collecting data about users. There were also fears that the Chinese government could use the platform to influence the public in the U.S.

A few incidents in the last several years that initiated that fear include TikTok employees utilizing location data from the app to track reporters to find a source of leaked information, and TikTok employees being reported to have plans to track specific U.S. citizens.

While TikTok is active in the U.S. right now, its future is unconfirmed.

For similar reasons to the TikTok concerns, a number of governments around the world, including Australia and Italy, are already working to ban DeepSeek from government systems and devices. The U.S. is also considering a ban on DeepSeek.

Chinese Censorship

Regardless of whether you run DeepSeek locally or in its app, DeepSeek’s censorship is present for queries deemed sensitive by the Chinese government, according to a Wired investigation.

However, because it is open source, there are ways of getting around the censorship, but it’s difficult.

Doing so would require running on your own servers using modified versions of the publicly available DeepSeek code, which means you’d need access to several highly advanced GPUs to run the most powerful version of R1.

Questions About Cost

Much has been written about the cost of building DeepSeek. Initial claims by DeepSeek were that it took under $6 million to build based on the rental price of Nvidia’s GPUs.

However, a report from SemiAnalysis, a semiconductor research and consulting firm, has since argued that DeepSeek’s hardware spend was higher than $500 million, along with additional R&D costs.

For context, OpenAI lost about $5 billion in 2024 and anticipates it will lose more than $11 billion in 2025. Even if DeepSeek did cost $500 million or more, it still cut costs compared to what leading competitors are spending.

So, how did they cut costs?

Before DeepSeek came along, the leading AI technologies were built on neural networks, which are mathematical systems that learn skills by analyzing huge amounts of data. This requires large amounts of computing power.

Specialized computer chips called graphics processing units (GPUs) are an effective way to do this kind of data analysis. This is how chipmaker Nvidia grew to prominence (and also had a huge fall in market value on the day DeepSeek launched).

GPUs cost around $40,000 and require considerable electricity, which is why leading AI technologies like OpenAI’s ChatGPT were so expensive to build.

Sending data between chips can also require more energy than running the chips themselves.

DeepSeek was able to reduce costs, most notably by using a method called “mixture of experts.”

Instead of creating one neural network that learned data patterns on the internet, they split the system into many neural networks and launched smaller “expert” systems paired with a “generalist” system, reducing the amount of data needed to travel between GPU chips.

The Implications Of Being Open Source

DeepSeek-R1 is as “open-source” as any LLM has been thus far, which means anyone can download, use, or modify its code.

Similar to Meta’s Llama, the code and technical explanations are shared, enabling developers and organizations to utilize the model for their own business needs, but the training data is not fully disclosed.

Many believe DeepSeek is a big step toward democratizing AI, allowing smaller companies and developers to build on DeepSeek-R1 and achieve greater AI feats faster.

This could lead to more innovation in places with more limited access to the tech needed to build AI solutions.

But, critics fear that open-source models can expose security vulnerabilities that could be exploited, which we’ve already seen in DeepSeek’s first weeks in the public.

DeepSeek And The Future of SEO

So, what does this all really mean for search professionals? The way I see it, DeepSeek is just the next splashy AI chatbot with search capabilities in the rapidly changing world of SEO.

It’s important to understand that while tools like DeepSeek and ChatGPT use advanced natural language processing (NLP) and machine learning, they still simply provide answers to real questions that real people ask.

Their responses heavily focus on semantic understanding, intent matching, and contextual analysis, but they ultimately serve the same core user need.

While we have years of experience testing optimization tactics on more established search engines like Google, we’re still at the beginning stages of understanding optimization for generative AI chatbots.

Final Thoughts

Whether DeepSeek will stick and grow in prominence remains to be seen.

Obviously, if other governments follow Australia, Italy, and potentially the U.S. to ban DeepSeek, that would limit its potential for growth.

And much as DeepSeek rose to prominence rapidly by providing a blueprint for others and significantly lowering costs, a new market-moving AI could always be just around the corner.

Regardless of what happens with DeepSeek, we are at the beginning of a very rapid period of innovation in AI technology.

As SEO professionals, we need to be prepared to test a surge of new platforms and reverse engineer how they arrive at their responses to user queries.

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