Once upon a time, Tech Clarion’s phone was “everyone’s phone” – in fact, mobile communications revolutionized the business (and the world). Today, the equivalent of that appeal is equivalent to making AI applications accessible to everyone. But the real power of AI is leveraging it to meet the specific needs of businesses and organizations. The path of Chinese startup DeepSeek shows that AI can indeed be leveraged to meet their specific needs, especially those on a limited budget. Indeed, the emergence of low-cost AI is expected to change the deep limiting model of AI solutions for many small businesses and organizations due to cost requirements.
LLMS is – or – an expensive effort that requires access to a large amount of data, a large number of powerful computers to process data, and the time and resources used to train models. But these rules are changing. Running on a thin budget, DeepSeek developed its own LLM, as well as Chatgpt-type applications for querying—its investments are much smaller than similar systems built by companies in the United States and Europe. DeepSeek’s approach opens a window for LLM development for small organizations that don’t have billions of dollars. In fact, when most small organizations can develop their own LLMs to meet their own specific purposes, the day may not be far away, and usually offers more effective solutions than a general LLM like Chatgpt.
While the debate still goes beyond the real cost of DeepSeek, it’s not just the cost of setting it and the cost of similar models: the fact is that it relies on less introductory chips and more focused training methods. As a Chinese company subject to U.S. export restrictions, DeepSeek has no access to advanced NVIDIA chips that are typically used for heavy-duty computing required for LLM development, so it is forced to use lower NVIDIA H-800 chips that cannot process data quickly or efficiently.
To compensate for the lack of power, DeepSeek takes a different, more centralized and direct approach to its LLM development. Instead of casting data mountains on the model, DeepSeek relies on computational intensity to label and apply data, but instead uses a small amount of high-quality “cold-start” data to narrow down training and apply IRL (iteratively enhanced learning, and uses algorithms to apply data to and learn from different scenarios). This centralized approach allows models to learn faster, with fewer mistakes and waste less computing power.
Similar to how parents guide a baby’s specific movement, helping her roll over successfully for the first time – rather than having the baby figure it out by herself, or teaching the baby a wider range of movements, this could theoretically help with marketing – data scientists train these more focused AI models. Such a model may not have reliable applications for larger LLMs like Chatgpt, but can rely on specific applications and execute them with precision and efficiency. Even DeepSeek critics acknowledge that its simplified development approach significantly improves efficiency, allowing it to do more with less speed.
This approach is to provide the best input for AI so that it can reach its milestones in the smartest and most efficient way and can be valuable for any organization that wants to develop LLMs for its specific needs and tasks. This approach is becoming increasingly valuable to small businesses and organizations. The first step is to start with the correct data. For example, a company that wants to use AI to help its sales and marketing teams should train its models on carefully selected datasets that hone in sales conversations, strategies, and metrics. This prevents the model from wasting time and computing the computing power of irrelevant information. In addition, training is required during the phase to ensure that the model masters each task or concept before moving on to the next task.
This is also similar to raising a baby, like I have learned since becoming a mother a few months ago. In both cases, a guided, step-by-step approach avoids wasting resources and reduces friction. Finally, this approach with infant human and AI models can be improved iteratively. As the baby grows, or learns more about the model, his abilities will improve. This means that the model can be improved and improved to better handle real-world situations.
This approach can reduce costs, thus preventing AI projects from becoming resource churn, making it easier for groups and organizations to enter. It also improves the performance of AI models faster. And, since the models are not overloaded with redundant data, they can also be adjusted to accommodate new information and changing business needs – the key to competitive markets.
The arrival of DeepSeek and the world of lower cost, more efficient AI – Despite initially scattering panic in the AI world and stock markets, overall, this is a positive development of the AI industry. At least for some centralized applications, AI is more efficient and less costly, which will ultimately lead to more AI usage, from developers to chip manufacturers to end users, which drives growth for everyone. In fact, DeepSeek illustrates Jevons’ paradox – more efficient may lead to more use of resources than less. As this trend looks set to continue, small businesses focusing on using AI to meet their specific needs will also be better at promoting growth and success.
