Revolution in Artificial Intelligence: Giant Companies Are Now Turning to Small and Expert Models

Large language models have long held a dominant position in the artificial intelligence sector, but today the strategies of giant companies are undergoing a significant transformation. Pioneering companies like OpenAI and Anthropic have focused on building massive models capable of handling almost any task in order to appeal to the widest possible audience. These models can be thought of as the Swiss Army knife of the AI world; given enough brute force, they can do almost any job. However, these giant structures are overly expensive and unnecessarily complex for performing simple tasks like summarizing emails or organizing meeting notes. For this reason, the industry is shifting toward much smaller, specific-domain, and economical models that can run dozens of instances on a single accelerator.

Microsoft stands out as one of the most prominent examples of this new strategy, embracing the idea that smaller models can often be better. Using the MAI family detailed at its Build developer conference in June, the company has produced solutions catering to a wide spectrum, from general-purpose reasoning to coding, and from image generation to audio models. According to Bloomberg reports, these specialized models have already begun to gradually replace OpenAI models behind the AI features in Microsoft products. Because the company now has a much better understanding of how its customers use AI, it prefers to use more precise, faster, and cheaper tools rather than expensive frontier models. This approach is seen as the most crucial step taken against concerns regarding making AI a profitable line of business.

Microsoft's model, named MAI-Thinking-1, is positioned as one of the most powerful models in its own weight class, despite being mid-sized. The company claims that this model matches leading models in basic software engineering benchmarks, exhibits advanced mathematical reasoning capabilities, and is preferred over competitors in blind human evaluations. In the AI world, the size of a model emerges as the most critical factor determining the balance between performance and cost. Smaller models with fewer parameters free up memory and significantly improve hardware utilization, thereby reducing operating costs. Thanks to this, Microsoft can quickly deploy the most appropriate AI tool at the right time and in the right place.

Cloud giants are closely following the strategy of reducing costs not only through software models but also on the hardware side. Microsoft, like Amazon and Google, is designing and manufacturing its own AI accelerators. The Maia 200 series processors, announced in January, promise to deliver performance comparable to Nvidia's Blackwell parts. These custom-built chips allow operators to holistically optimize the entire AI stack, including hardware, software, and models. Spreading optimization across every layer makes AI services more efficient and competitive. These hardware investments serve as a critical component that reinforces the success of the small model strategy.

Microsoft is not alone in this field; Google has embraced this philosophy from the very beginning with its Gemini and Gemma model families, developing solutions based on its proprietary TPU architecture. Similarly, Amazon continues to strengthen its own solutions by making massive investments in its Nova model family, despite investing in Anthropic. Nevertheless, general-purpose frontier models still play a vital role in driving innovation and pushing the industry forward. It is clear that even though cloud giants are refining their existing tools and focusing on profitability, they will continue their billion-dollar investments in companies like OpenAI and Anthropic for the sake of continuous innovation. As a result, the reduction of tech giants' dependence on external models will increase the chances of finally transforming AI into a sustainable and profitable business area.