China's DeepSeek Moment: The AI Race Gets a New Contender

DeepSeek’s impact on AI and how you consume it: Open-source disruption, global competition, and the fight for transparency.

DeepSeek has been all over the news with reactions ranging from excitement to fear (they were discussing banning it in the US). There's good reasons why US tech giants like Meta and OpenAI are shaken, and why it deserves your attention.

That said, there’s significant noise on this topic. From my experience during research, it was exhaustive differentiating fact from fiction.

My goal in this article is to provide you with insights to make your own informed decision. I'm going to discuss 3 aspects below of why you should consider DeepSeek.

Open-Source

DeepSeek is open-source - you can download the model, install it on your own machine, and run it privately. While Llama labels itself open-source AI model (Open Source Initiative doesn’t recognize them for good reason link here - https://opensource.org/ai/faq#which-ai-systems-comply-with-the-open-source-ai-definition), it’s reasoning isn’t at the same level. This is an open-source solution that can rival the best, competing with OpenAI’s o1.

It’s a significant shift in the GenAI landscape, which is dominated by proprietary solutions like OpenAI.

The AI industry has largely operated contrary to tech best practices—despite open-source being the foundation of modern technology. In fact, over 90% of applications today incorporate some form of open-source code, powering everything from enterprise software to the SaaS products we use daily.

The open-source community is essential to the sustainability and advancement of our systems. It fosters best practices through peer reviews and contributions from passionate developers with no direct financial gain resulting in collective innovation that benefits everyone. When it comes to AI—especially for enterprise adoption—open-source offers clear advantages over proprietary models, including:

• Transparency & Validation – Code can be peer reviewed and verified before adoption.

• Customization & Bias Reduction – You can train the model on your own data, mitigating previous biases.

• Data Ownership & Security – Your analysis and data remain within your environment, ensuring key intellectual property isn’t shared with third parties.

• Governance & Compliance – Full control over how the AI is developed, deployed, and maintained.

• Enhanced Security – Tighter control over sensitive data.

With DeepSeek, you have the flexibility to deploy AI on-premises or in your preferred cloud environment—and even retrain the model entirely, as you have full access to the code.

In contrast, proprietary AI solutions come with vendor risks. OpenAI, for example, has shifted its business model multiple times, transitioning from a nonprofit to a for-profit structure. These uncertainties make open-source alternatives like DeepSeek all the more valuable, ensuring organizations retain control, security, and adaptability in an evolving AI landscape.

It’s not all good news, as promised above I wanted to give you full insights. While the model is open-source, it doesn’t fully comply with OSI’s definition. We don’t know the training data and other vital training information.

But there is hope, the HuggingFace community seems to be working tirelessly to make this fully open-source, aptly named Open-R1.

Lower Cost and Carbon Footprint

One of the most striking findings about DeepSeek is that it was able to outperform other AI models at a fraction of the cost. While OpenAI reportedly spends over $10 million on a single training run, DeepSeek was built with just $6 million. This number may not seems significant at first, but as models grow and require more training, this cost efficiency is a game-changer, particularly in an industry where compute resources are one of the biggest barriers to entry.

Beyond savings, the lower cost of training has massive implications for energy consumption. AI development is notoriously energy-intensive, with companies like OpenAI calling for dedicated data centers and potentially consuming a significant percentage of total available electrical power. As the world becomes more reliant on AI, the industry is at risk of driving up energy demands at an unsustainable rate.

DeepSeek’s approach challenges this norm. By running on less powerful hardware, it significantly reduces energy usage, leading to a smaller carbon footprint and lower operational costs.

This efficiency not only makes AI more accessible to smaller organizations but also aligns with broader sustainability goals.

As this space continues to evolve, the conversation around energy efficiency will only become more critical. DeepSeek’s success demonstrates that powerful AI doesn’t have to come at an exorbitant financial or environmental cost.

It raises an important question: should AI development prioritize sustainability and accessibility over sheer computational scale? If so, DeepSeek could be a model for a more responsible AI future—one that balances innovation with efficiency.

Macro Global Risks

AI models will always reflect the biases of their training data, which are often shaped by the regional politics of their home countries which in DeepSeek's case is China. As AI becomes a key strategic investment for governments, we can expect increased competition between models from different nations.

DeepSeek, like all AI models has inherent biases, the same is true for U.S.-based AI companies. Allegations of data theft and misuse have surfaced on both sides, affecting companies like OpenAI and DeepSeek alike. Testing the boundaries within the systems, it’s easy to validate biases on all AI systems.

However, the trajectory of AI development in China appears to be leaning toward greater openness, whereas the U.S. AI ecosystem is increasingly dominated by a handful of private, closed-source entities. This centralization of knowledge and decision-making is concerning, as it places control of AI’s future in the hands of a select few.

For years, I've been consuming blockchain research from there because it's great quality and they're sharing critical findings. Going open-source is about sharing knowledge and learning collectively. As a species we proliferate when knowledge is shared. History shows that civilizations reach renaissance by sharing knowledge, and regress when internalizing

Looking ahead, DeepSeek and similar models may gain an edge unless the U.S. commits to substantial government-backed AI investments. It’s why we see the massive investment of $500 Billion in the StarGate initiative.

As AI continues to shape the future, more nations will likely recognize its strategic importance and ramp up their own investments. The question is not just who will lead AI development, but how the world will choose to balance openness, innovation, and control.

I truly believe that DeepSeek represents a pivotal shift in the AI landscape, in that it has popularized a open-source model and is challenging the status quo of costly, bloated, proprietary models. Its open approach offers greater transparency, flexibility, and efficiency, but the larger question remains: How should AI evolve moving forward?

Now is the time to engage in the conversation. Explore DeepSeek for yourself, test its capabilities, and consider what an open-source AI future could mean for you or your organization.

💡 What are your thoughts on DeepSeek? Do you see open-source AI as the future, or do you have concerns about its implications? Join the discussion in the comments, share your insights, and let’s talk about where AI should go next.