The narrative here is that the evolution of database systems has always been in response to a rapidly changing landscape of "constraints", e.g.

- The challenge of open R&D collaboration -> Postgres & MySQL

- The ineffectiveness of one-size-fits-all DB engines -> 19 database services at AWS

- Shared disk architectures being difficult to scale -> cloud object storage

- High storage costs limiting the applicability of data analysis -> cloud data warehousing

...and so on. Followed by a summary of the biggest constraints that databases face today:

> What limits the application of infinite cores?

> 1. Data: inability to get data to processor fast enough

> 2. Power: cost rising and will dominate

Conclusion (spoiler!):

> ML central to DB going forward + opportunities with H/W specialization = big database innovations still coming

So I/O is still a bottleneck, but I wasn't expecting power to be one.