Someone needs to pay for the R+D before the hardware can exist, so I don't see anything wrong with considering R+D as part of the cost of the hardware (its "worth" if you will). The alternative is only considering the raw materials as part of the cost of building something.

Right, but the parent comment was questioning the widsom of installing the hardware in every car. My point is the pricing model is that people using the software pay for it. The price of putting the hardware in the cars is probably low, and also Tesla probably benefit from the data gathered by that hardware anyway.

Tldr: I suspect Tesla know what they're doing with the pricing of this thing.

>Tesla probably benefit from the data gathered by that hardware anyway.

In a big way. They are using data gathered by the self-driving ready hardware to develop the software that will use it.

Thousands of cars out there collecting data is a huge advantage over groups working with a handful of prototypes.

> Thousands of cars out there collecting data is a huge advantage over groups working with a handful of prototypes.

I'm not sure this is true. At least for Google, it seems like the main bottleneck to progress is engineering time to fix the problems that arise. The problem is not a lack of vehicles driving and finding problems.

I say this because Google has made very little effort to expand their fleet of testing vehicles. The last big expansion, from 28 to 48 vehicles, was in Sept. 2015. Since then they've expanded from 48 to 58, but it doesn't seem to be a priority. [1]

[1] https://docs.google.com/spreadsheets/d/1v0BBTDOXvD8JdhrySFy6...

I think it's just that it isn't an option. The data from 100 cars isnt much more useful than the data from 50. That doesn't mean the data from 10000 cars isn't much more useful than the data from 50.

It just isn't reasonable for Google to build and manage a fleet of that size for a development program. It would cost Google half a billion dollars, but for Tesla it's just a bit of cost they roll into the product.

Teslas approach is different to Google's. Tesla is gathering hi-res location data from both human driven and autopilot driven cars to build an accurate map of every lane of every highway.

Depends on the algorithms you are using. In machine learning, bigger data sets can trump better algorithms.

Sure. But having auto-pilot a significant % of the roads in the USA (and norway by the sounds of it) gives tesla an advantage. After all you can't learn from a road you haven't been on.

It's wrong because the marginal cost of adding another unit to a car has nothing to do with r&d.

In the choice between putting the hardware on all cars vs only on a few prototypes, the R&D is a sunk cost that shouldn't influence the decision and only the pure incremental cost of the hardware matters.