Also, vendor lock-in is a huge challenge in the cloud space. I don’t think Tesla would be comfortable with the fact that all their training data sits on a potential competitor’s datacenter.
Also, Tesla is/was planning on running these chips even while not in autonomous mode in order to spot new scenarios which it can then record what the car sees and what the human does in order to collect a lot more unique road scenarios to train their models with.
[0] Credit Robotbeat: https://news.ycombinator.com/item?id=19729743
Increasing their compute per watt allows them to get more compute per watt, so they will pursue it lots, low speed energy savings are a way of making the range extension seem like a big deal when it isn't (maintaining highway speeds requires tens of kilowatts).
Power efficiency does make a range difference and is worth seeking, but Tesla is exaggerating this, IMO, to conceal one of the ways that the churn-heavy cycle for FSD has imposed organizational costs (an entire chip-level hardware program, in this case).
An actual working system might slow them down, but even then, better hardware is one of the ways to make it better.
There's commodity computing of comparable throughput that other automakers can incorporate without too much cost, but it doesn't fit into the existing TM3 vehicles because it has a larger footprint in power, space, and cooling than HW2.
Here, Tesla spends a bunch of money to develop HW3 and overcome this problem, but the advantage in a new car design between HW3's efficiency and commodity hardware is limited.