Deepseek is an exciting development; here’s why open-source innovation strengthens our position.
What is Deepseek?
Deepseek is an open-source large language model (LLM) that recently took the AI world by storm. Designed to emphasize chain-of-thought (CoT) reasoning and deep problem-solving capabilities, Deepseek pushed the existing boundaries of AI reasoning while remaining openly available for modification and adaptation, on a $5.6M training budget (not accounting for hardware spend.) Unlike closed-source models, Deepseek’s license allows developers to refine and tailor its capabilities to specific needs, which has already led to early experiments.
For companies and developers working with AI, Deepseek represents another step forward in the continued evolution of open-source AI. Its promise lies in the potential for more accessible, high-quality AI models that offer performance comparable to closed-source alternatives.
Understanding Deepseek: Open-Source Innovation in AI
A key reason for the excitement around Deepseek is its potential to offer performance comparable to closed-source models while remaining adaptable. As noted by the Financial Times, Deepseek’s success represents a shift in the AI landscape, particularly in the ongoing technological competition between global AI leaders (OpenAI and Meta included.)
While Deepseek has clear strengths, its primary appeal is in logical progression and deep problem-solving rather than real-time responsiveness. Its CoT-based reasoning process makes it useful for applications requiring multi-step reasoning, such as research assistance, coding support, and strategic planning tools. However, this structured and deliberate reasoning approach also makes it slower compared to models designed for fluid, real-time conversation. This limitation is crucial for companies such as ours, where latency and speed are key differentiators.
Deepseek’s Market Impact
Despite its promising capabilities, Deepseek is not a disruptive force for all AI businesses. According to The Times, businesses considering Deepseek must evaluate whether its capabilities align with their needs, particularly in speed-sensitive applications.
For Bland, the emergence of Deepseek aligns with—rather than challenges—our existing strategy. Deepseek represents another potential building block rather than a threat. Additionally, our independence reassures enterprise clients that our technology remains neutral, adaptable, and reliable.
Latency and Speed Matter– Deepseek isn’t Built for Real-Time AI
One of Bland AI’s key differentiators is our approach to model refinement. We don’t simply adopt open-source models as-is–we fine-tune, train, and adapt them to be performant in our domain.
At worst, an open-source model like Deepseek is simply unusable in our space. At best, it provides another foundation for us to build upon.
Deepseek’s primary strength lies in CoT reasoning, which makes it excellent for tasks requiring deep logical progression. However, this also makes it slow–far too slow for real-time AI applications. We value latency and speed, ensuring that our models deliver responses in milliseconds for seamless user interactions. A model that takes significantly longer to generate responses, even if it excels at complex reasoning, does not fit our usual use case. .
The Bottom Line: Bland AI is in a Strong Position
Deepseek is an exciting project, but it doesn’t change our trajectory. In fact, it reinforces our strengths. By building with various models, optimizing for real-time performance, and ensuring high adaptability, Bland remains well-positioned to continue delivering best-in-class voice solutions.
For a deeper dive into how we leverage open-source AI in innovative ways, check out our blog post on AI Phone Agents: Revolutionizing Call Center Technology and Profitability.
As the AI landscape continues to evolve, Bland stays at the cutting edge–not by reacting to every new development, but by staying true to our strengths: performance, adaptability, and speed.
