> For the complete documentation index, see [llms.txt](https://bee-partners-1.gitbook.io/bee-the-machine-to-machine-learning-vector/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://bee-partners-1.gitbook.io/bee-the-machine-to-machine-learning-vector/conclusion/why-now.md).

# 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="<https://vimeo.com/444937616>" %}

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.