This paper investigates the efficiency of time-dependent details in increasing the high-quality of AI-centered products and solutions. Time-dependency means that facts loses its relevance to challenges above time. This reduction will cause deterioration in the algorithm’s overall performance and, thus, a decline in produced enterprise value. We model time-dependency as a change in the chance distribution and derive many counter-intuitive benefits. We, theoretically, prove that even an infinite amount of information collected around time may well have confined compound for predicting the upcoming, and an algorithm that is experienced on a present-day dataset of bounded sizing can achieve a very similar general performance. Additionally, we demonstrate that rising details quantity by such as more mature datasets may possibly put a enterprise in a disadvantageous placement. Obtaining these success, we reply issues on how knowledge quantity results in a aggressive advantage. We argue that time-dependency weakens the barrier to entry that info quantity results in for a enterprise. So significantly that competing firms geared up with a confined, but ample, quantity of present information can attain superior functionality. This outcome, collectively with the truth that more mature datasets may well deteriorate algorithms’ performance, casts doubt on the importance of 1st-mover edge in AI-based marketplaces. We complement our theoretical final results with an experiment. In the experiment, we empirically evaluate the value loss in text knowledge for the following term prediction undertaking. The empirical measurements verify the significance of time-dependency and value depreciation in AI-centered organizations. For illustration, immediately after seven years, 100MB of text details becomes as handy as 50MB of current knowledge for the next term prediction activity.