Most conversations about AI tend to focus on the increase in computing power or improvements in algorithms. Both are a critical part of achieving AI, but what\u2019s often missed in those conversations is that AI is only as intelligent as the volume of data it has to learn from.\r\n\r\nAll of the recent advances in AI could simply not have been possible without our significantly increased ability to capture and store data. This applies to everything from using AI to find patterns in and to learn from the over 3.5 billion searches done on Google every day (one trillion searches year!) to the 11 terabytes per day that a Waymo driverless vehicle generates.\r\n\r\nIn each case, where we see AI making inroads and holding out the promise of radically new approaches to solving problems, we also find this tight connection between data creation and consumption to be true.\r\nHow Does AI Learn?\r\nAn easy way to understand why this is the case is to think about the way AI learns, which requires sifting through enormous amounts of data to create the equivalent of an experience base.\r\n\r\nFor example, Google DeepMind\u2019s AlphaGo Zero, which won against both the world\u2019s human Go champion as well as against its predecessor AlphaGo, had to play 20 million games against itself to train its AI.\r\n\r\nOf all the factors driving increases in data storage, none will be as pervasive as AI. The value of AI in countless applications and the cost savings it will deliver will drive adoption. Research by Deloitte projects labor savings due to AI in federal, state, and local government of over $40 billion. If we apply the same savings to private industry, savings could exceed five trillion dollars annually.\r\nThere\u2019s More to (Artificial) Life than Cost Savings\r\nAs extraordinary as that may seem, it is still calculating the value of AI based on how it simple streamlines existing processes rather than looking at all of the ways in which it will create new value and businesses that we can't yet identify.\r\n\r\nEstimating this with precision is far more difficult; however, during the last 140 years of the industrial era, every reduction in the labor force brought about by technology savings has created far more jobs, economic opportunity, and new businesses than it has destroyed.\r\n\r\nThere\u2019s no doubt that AI will amplify this effect many times over any prior technology. But as we start to realize the value of AI a virtuous cycle will begin to accelerate demand for data well beyond the anticipated currently anticipated rates of growth.\r\n\r\nTake for example autonomous vehicles.\r\n\r\nEven at 11 TB a day, today\u2019s driverless vehicles use AI in a very narrow sense. In many ways their progress is being inhibited by the lack of enough data. This leads to one of the most pressing issues in the field of driverless vehicles and autonomous devices (what are collectively called AVs). In order to conserve storage, most of the algorithms that operate AVs are trained to gather specific data about a subset of the AV\u2019s context. However, the ideal way in which to train an AV is to allow it to learn the same way that human drivers do, by capturing all available data about context. This has a dramatic impact on the amount of data that needs to be stored, increasing data storage from 11 TB to up to 200 TB a day.\r\n\r\nAnd this is where the promise of AI hits the equivalent of a brick wall.\r\n\r\n200 TB per day multiplied by 365 days results in 73 PB of data, which would today cost upwards of 21 million dollars in cloud storage costs.\r\n\r\nSo, while we can demonstrate AI in small use cases with a few dozen or even a few hundred AVs there is simply no way we could scale up to millions of vehicles.\r\n\r\nThe only conclusion is that we need to radically alter the economics of storage if AI is to be more than a curiosity. That means focusing on storage as a separate and distinct part of the cloud, rather than as a bolt on for cloud computing solutions.\r\n\r\nWhat AI uses is your business bracing for?\r\n\r\nAre you prepared for the storage requirements that will make or break the potential value of AI?\r\n\r\nThe future of AI and the future of the foundation to AI, data, are tied together explicitly.\r\n\r\nAre you ready?