# Why Now? We know that M2ML has the potential to digitize the physical world and capture outsized economic rent. However, as investors we constantly ask ourselves - WHY NOW? We strongly believe that the incoming decade will thrust forward M2ML interactions due to significant macro-trends that are only picking up in speed: 1\) Coinciding with the frontier curve of technology expanding outward thanks to **scalable ML advancements**, we saw a 16% **increase in the number of devices online** from 2019 to 2020. This has pushed enterprise users to explore more sophisticated tools and interfaces (“consumerization of the enterprise”) to serve end-customer needs. {% embed url="" %} 2\) Further, just 13.3% of US jobs are classified as sedentary desk jobs while the rest are in the field or on the go - pushing forward **mobile-first productivity**. 3\) Most urgently, **COVID-19 has shown us a glimpse of a more automated, resilient future of work** that extends across the supply chain and beyond the mission critical. While this trend started in Manufacturing, it has now moved well beyond into areas like Healthcare, R\&D, Agriculture, Waste Management, and many more. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://bee-partners-1.gitbook.io/bee-the-machine-to-machine-learning-vector/conclusion/why-now.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.