What is AI warfare?

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In the technology space, Microsoft and Google are battling for dominance, a challenge intrinsic to the nature of large corporations. The inertia created by lengthy processes, reviews and bureaucratic structures can be a significant obstacle in terms of the speed and agility needed to compete in the dynamic world of artificial intelligence (AI).

To meet this challenge, these tech giants have acquired AI-specialised start-ups. Google, for example, has added Anthropic and DeepMind to its portfolio, while Microsoft has opted for OpenAI. This structure offers the advantage that start-ups operate more agilely and can make advances and launch products more quickly than their parent companies. However, these start-ups face the problem of funding. This is where the tech giants come in, providing the necessary financial resources. These large companies maintain product lines that engage in a “power struggle”. This competition is illustrated in the following diagram.


Both Microsoft and Google have major products in the search facebook data engine and web browser arenas. In search engines, Google dominates with Google Search, while Microsoft competes with Bing. To challenge this market position, Microsoft is integrating GPT-4 into its search engine features. The situation is the same in the web browser arena. Google leads the market with Chrome, while Microsoft competes with Edge. Microsoft also intends to integrate GPT-4 into Edge to offer a web copilot feature, similar to GitHub Copilot for programming.

This pattern continues across the Office suite offerings and AI applications. In each of these areas, Microsoft is seeking to enhance its products by integrating advanced AI technologies such as GPT-4 to challenge Google’s dominance. This strategy highlights the growing competition between these tech giants and the role AI plays in shaping their product strategies and market position. It also shows how integrating AI into core products is seen as a means to gain competitive advantage in the market and challenge existing market dominances.

In principle, this translates into a cycle for products. Companies integrate advanced AI technologies into their products to improve their performance and user experience. By improving performance and user experience, products gain market acceptance and attract a larger user base. In the digital world, end-user use of products is closely tied to data generation.


As products are used more and more, they generate a wealth of user data. This data can be used to further improve AI models. With the data collected, AI models can be further improved and optimized, leading to even more powerful products. Improved products attract even more users, which generates even more data. This reinforces the cycle.

Therefore, it is strategically important for tech giants like Microsoft and Google to dominate in the “non-core jobs.” Having a product optimized by big data is a direct competitive advantage. In a world that increasingly resembles a winner-takes-all market, the quality of data-driven product improvement is a critical factor for success.

Barriers to entry for competitors increase exponentially with the quality and quantity of data a company can use to optimize its products and services. In this context, data-driven product improvement acts as an important competitive barrier that strengthens a company’s position in the market and ensures its long-term success.