How Chinese A.I. Start-Up DeepSeek Is Competing With Silicon Valley Giants

A small Chinese start-up, DeepSeek, unveiled a new AI system, DeepSeek-V3, capable of competing with leading chatbots from OpenAI and Google. Remarkably, it was developed using 10 times less resources than what major companies like Meta spend.

DeepSeek’s strong performance raises questions about the unintended effects of U.S. trade restrictions, as Chinese researchers leverage open-source tools and creative strategies to work around resource constraints. DeepSeek has made its technology open source, enabling global collaboration and challenging the dominance of U.S. tech giants. Some experts warn that stifling open-source development in the U.S. could allow China to dominate AI innovation, as the global open-source community increasingly centers on China.

ZNest’s Take

Key Takeaways

• DeepSeek Makes AI more accessible to builders: DeepSeek proves that generative AI models can be created without astronomical budgets, and it being open source allows builders to use it for free.

• More builders leads to faster adoption: More developers and companies creating new applications accelerates adoption by providing more use cases and tools, making it easier for a broader audience to embrace the technology. 

• Senior Care is Lagging in Technology Adoption: The senior care industry is slow to adopt new technologies, often waiting until change is unavoidable, which delays the benefits of innovation, efficiency, and prevents the creation of purpose built tools.

• Embrace Technology Sooner to Drive Innovation: Senior care leaders need to experiment with new technologies early, even if they aren’t perfect, or risk falling behind.

News of DeepSeek hit AI stocks like a ton of bricks on Monday, January 27. Chipmaker Nvidia lost $600 billion in market value. So what is this all about?

Contrary to some online portrayals, DeepSeek did not come out of nowhere. It was founded in July of 2023 and has been conducting research since. The company released its AI assistant on January 10th of 2025 and its achievement of matching the performance of major generative models at a reported 95% discount on cost challenges the conventional belief that only high budgets and advanced chips can drive AI innovation.

Understanding DeepSeek’s Impact on the Average Consumer

Let’s first define the technology adoption curve which will frame how we approach this question. The technology adoption curve is a model that explains how people adopt new technology over time. It follows a bell curve and is divided into five groups based on psychological characteristics:

Innovators (2.5%) – These are the risk-takers who love trying new tech first, even if it’s expensive or buggy. Think of the people who camp outside Apple stores for a new iPhone.

Early Adopters (13.5%) – They don’t need to be first, but they’re quick to embrace new trends. They help spread the word and influence others.

Early Majority (34%) – This group waits until the technology is tested and proven useful. They adopt it once it starts becoming mainstream.

Late Majority (34%) – More skeptical and cautious, they only adopt when most people already have. Think of your grandparents getting smartphones years after they became popular.

Laggards (16%) – The last to adopt, often resisting change. They might still use flip phones or avoid technology unless absolutely necessary.

We’ve now set the stage

DeepSeek will likely shorten the adoption curve, meaning it will reduce the time it takes for AI to move from innovators to laggards. The release of DeepSeek has opened the door to more builders—such as companies and entrepreneurs—because:

1. It has proven that massive financial resources, like those of Google, are not necessary to compete.

2. DeepSeek is open source, meaning the code is freely available for anyone to view, modify, and share.

More builders lead to more innovation and a faster path to truly useful applications. Here’s how more builders impact each group:

• Innovators (2.5%) – More cutting-edge tools and applications emerge, appealing to a larger pool of innovators.

• Early Adopters (13.5%) – New AI applications appear more frequently, allowing early adopters to experience AI in more areas of their lives.

• Early Majority (34%) – More case studies and real-world examples demonstrate AI’s usefulness, making adoption easier.

• Late Majority (34%) – With stronger proof that the technology works, larger populations adopt AI sooner.

• Laggards (16%) – AI becomes embedded in legacy tools, forcing adoption even for those resistant to change.

What This Means for Senior Care

Few would disagree that the senior care industry is not at the forefront of technological adoption. In fact, it sits squarely in the Laggards category, meaning adoption often happens only when it becomes unavoidable. We believe senior care will be forced to adopt AI technology faster than usual as a result of DeepSeek and future companies like it. However, it’s crucial to highlight an important point: Being a laggard is not a good thing.

Senior care is an incredibly challenging industry, facing demographic changes, workforce shortages, high costs, regulatory hurdles, and the emotional weight of caring for an aging population. The purpose of technology is to make processes better, more efficient, and more scalable—yet industries that adopt technology slowly often miss out on these benefits for years.

Being a laggard means that innovators and builders prioritize other industries first—not because senior care isn’t important, but because it isn’t immediately profitable. Startups and tech companies focus on sectors with high returns and rapid adoption, leaving slower-moving industries to wait until solutions become mainstream.

By the time senior care finally gains access to new technology, it is often an adaptation of existing tools, rather than a custom solution designed for its unique challenges. Instead of platforms built specifically to address staff shortages, personalized patient engagement, or regulatory compliance, senior care facilities are left modifying generic tools originally designed for healthcare, business, or consumer markets.

As a result, innovation in senior care lags behind, and by the time meaningful change arrives, the industry is already years behind where it could be. This slow adoption not only impacts efficiency but also directly affects the quality of care seniors receive.

The Need for Change

It is critical for industry leaders to move up the adoption curve and experiment with new technologies, even if they aren’t perfect. Waiting for perfect solutions means missing out on incremental improvements that could make a real difference today. Leaders in senior care must foster a culture of experimentation and collaboration with technology providers to drive innovation forward.

By embracing technology earlier, senior care organizations can shape AI solutions to fit their needs, rather than being forced to adapt when it's too late.

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