They try to spin the scanning pattern as being an advantage as you get a more dense sampling if you point at it in a certain direction, but I'm not convinced. It is only an advantage if your device sits on a tripod. On a moving car, more predictable, structured patterns such as the Ouster OS-1 [1] make it much easier not only for deep learning, but also for SLAM (e.g. the LOAM algorithm [2] extracts feature points row by row, i.e. by ring). For a moving car or drone, any scanning pattern will sweep into a dense 3D point cloud anyway.
The pricing seems to be competitive if you only need a small field of view.
For 360 degrees, it takes four Mid-100s, which would cost $6000, or twleve Mid-40s, which would cost $7200, to obtain the same field of view coverage and the same number of points (1.2 M points per second) as a single Ouster OS-1, which is available to university researchers for $8000. However, buying a single Ouster OS-1 saves you the headache of extrinsic calibration between many lidar units, and the Ouster OS-1 only draws 14 W whereas four Mid-100s would draw a whopping 480 W. Four Mid-100s also weigh twenty times as much as one Ouster OS-1. For high density drone mapping, the Ouster OS-1 seems like a much better choice.
That said, the Livox does have a range advantage over the Ouster OS-1.
[0] (PDF) https://www.thorlabs.com/images/tabimages/Risley_Prism_Scann...
[1] https://medium.com/ouster/the-camera-is-in-the-lidar-6fcf77e...
[2] Zhang, J., & Singh, S. (2014, July). LOAM: Lidar Odometry and Mapping in Real-time. In Robotics: Science and Systems (Vol. 2, p. 9).