Industry experts say large language models are no longer a primary competitive advantage as they become more accessible and affordable [1].
This shift matters because companies that rely solely on the power of their underlying AI models may find themselves without a unique market differentiator. As the technology commoditizes, the value moves from the engine to the specific assets that fuel it.
According to the Forbes Tech Council, the rise of AI agents is forcing a strategic pivot. The consensus among contributors is that the focus must shift toward data, domain expertise, and distribution networks to maintain a competitive edge [1, 2].
"The model will keep getting cheaper and better for everyone. Plan as if it’s free," a Forbes Council member said [1]. This perspective suggests that the cost of LLMs is decreasing, making the model itself a utility rather than a proprietary secret [1].
Sanoke Viswanathan, Chief Executive Officer of FactSet, said this transition occurred during the Q1 2026 earnings call. Viswanathan said companies need to focus on "data, domain, and distribution" [2]. By prioritizing specialized knowledge and the channels used to reach customers, firms can create a sustainable advantage that a generic model cannot replicate.
Other analysts warn against viewing the AI transition as a winner-take-all scenario. Jonathan Kitchen said, "Zero-sum thinking is a dangerous trap in the age of AI" [2]. This suggests that the evolution of the industry allows for multiple successful players if they leverage their unique domain strengths rather than fighting for model supremacy.
Strategic success now depends on how well a company can integrate these agents into specific workflows. The ability to deploy an AI agent within a proprietary distribution network—backed by specialized data that competitors cannot access—creates a new type of moat [1, 2].
“"The model will keep getting cheaper and better for everyone. Plan as if it’s free,"”
The transition from 'model-centric' to 'data-centric' AI indicates a maturing market. When the core technology becomes a commodity, the competitive battlefield shifts to the proprietary inputs and the efficiency of the delivery system. Companies that possess deep vertical expertise and unique datasets will likely outperform those that simply wrap their services around the latest general-purpose LLM.
